pestle/assets/jsonqb/Computer Science QB.json

[
  {
    "Question": "<div class=\"specification\">\n<p>Ralph owns a furniture store that trades in second-hand furniture, lamps and musical instruments. He buys these items, repairs them where necessary and then resells them.</p>\n<p>He calculates the value of each item using the information below:</p>\n<ul>\n<li>type: furniture, lamp, musical instrument</li>\n<li>brand recognition: 1 (low), 2 (medium), 3 (high)</li>\n<li>condition: “very good”, “good”, “needs repair”</li>\n<li>estimated volume of item: maximum volume accepted is 2000 dm<sup>3</sup><br/>(note that 1 dm<sup>3</sup> = 1 litre).</li>\n</ul>\n<p>Ralph is going to use a spreadsheet to model this information.</p>\n</div><div class=\"specification\">\n<p>The spreadsheet model will inform Ralph as to whether he should buy an item. The decision to buy is based on the following rules:</p>\n<ul>\n<li>the default values for the items are; furniture $100, lamp $30, musical instrument $80</li>\n<li>multipliers are applied to these default values depending on the volume of the item; <br/>for volumes that are 500 dm<sup>3</sup> or above it is 0.7, for volumes less than or equal to 30 dm<sup>3</sup> it is 1.2, for any other volume it is 1.</li>\n</ul>\n<p>Ralph will not buy any item with a value calculated to be over $90.</p>\n</div><div class=\"specification\">\n<p>The model is also used to calculate his final selling price for each item. This price is calculated using the following rules:</p>\n<ul>\n<li>initial selling value is 5 times the original default value</li>\n<li>a multiplier of 1, 2 or 3 is applied to reflect the brand recognition (a “top brand” receives a multiplier of 3)</li>\n<li>another multiplier is applied to reflect the volume occupied. This multiplier is 5 when the volume is greater or equal to 500 dm<sup>3</sup>, 3 for volumes between 30 and 500 dm<sup>3</sup> and 1 for other volumes</li>\n<li>a further multiplier is applied depending upon the item’s condition. This multiplier is 2 or 3 when conditions are good or very good, but is 0.8 if the item is damaged.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the following table showing the variables, each variable’s data type and range of values that would represent the information shown above.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using the above rules, construct the pseudocode that will help Ralph in deciding whether to buy an item.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> items that would have a calculated value of more than $90.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Calculate the selling price of a top brand guitar with a volume of 96 dm<sup>3</sup> that was damaged. You should show your working.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With the help of a diagram, suggest an appropriate design for a spreadsheet used to calculate the final selling price, following this model.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> tests that should be included in the test plan for this model.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>:<br/><em>Award <strong>[1]</strong> for each <strong>fully correct</strong> row indicating <span style=\"text-decoration:underline;\">variable name</span>, <span style=\"text-decoration:underline;\">type</span>, and <span style=\"text-decoration:underline;\">values</span> </em><em>(no marks for incomplete information, but do allow abbreviated strings)</em></p>\n<p><em>e.g.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<p><em><strong>Note</strong>: If a full column is missing, then award <strong>[1]</strong> for each correct column (for a max of <strong>[2]</strong>).</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong>:</em><br/><em>Award <strong>[1]</strong>: correct initialisation of <code>Initvalue</code>.</em><br/><em>Award <strong>[1]</strong>: correct calculation of Temp.</em><br/><em>Award <strong>[1]</strong>: correct comparison of Temp with 90.</em><br/><em>Award <strong>[1]</strong>: correct return/output.</em></p>\n<pre>proc Decide(Item, Vol)<br/>Var Temp<br/><strong>if</strong> Item = f then Initvalue = 100<br/><strong>  else if</strong> Item = l then Initvalue = 30<br/><strong>    else if</strong> Item = m then Initvalue = 80;<br/><strong>endif</strong><br/><strong>if</strong> Vol &gt;= 500 then Temp = 0.7 * Initvalue      // accept &lt;,&gt;<br/><strong>  else if</strong> Vol =&lt; 30 then Temp = 1.2 * Initvalue<br/><strong>    else</strong> Temp = Initvalue;<br/><strong>endif</strong><br/><strong>if</strong> Temp &gt; 90 then<br/>  return \"Reject\"            // accept any 2 reasonable outputs<br/><strong>  else</strong> return \"Accept\";<br/><strong>endif</strong></pre>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Any item of furniture with a volume <math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mo>&lt;</mo></math> 500 dm<sup>3</sup>;<br/>Any musical instrument with a volume <math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mo>≤</mo></math> 30 dm<sup>3</sup>;</p>\n<p><em>Example answers</em>:<br/>A piece of furniture with volume of 400 dm<sup>3</sup>;<br/>A piece of furniture with volume <math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mo>&lt;</mo></math> 30 dm<sup>3</sup>;<br/>A flute with volume of 1 dm<sup>3</sup>;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong>:</em><br/><em>Award <strong>[2]</strong> for completely correct answer (only the left or right-hand side needs to be shown).</em><br/><em>Award <strong>[1]</strong> for correctly writing just 4 factors from the left-hand side.</em></p>\n<p>5 * 80 * 3 * 3 * (80/100) = $2880</p>\n<p><em>Alternative expression for the left-hand side</em>:</p>\n<p>5 * (<code>Item.IType.Initvalue</code>) * (<code>Item.IType.IBrand</code>) * <br/>(<code>Item.IVol.Percentage</code>) * (<code>Item.ICond.Multiplier</code>)</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for some layout design.</em><br/><em>Award <strong>[1]</strong> for each of the following elements</em>:</p>\n<ul>\n<li>correct formula for <code>Itype</code>\n</li>\n<li>correct formula for <code>Ivol</code>\n</li>\n<li>correct formula for <code>Icond</code>\n</li>\n<li>correct formula for Selling Price.</li>\n</ul>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Example for <strong>[2]</strong>:</em><br/><em>Award <strong>[1]</strong> for the general layout.</em><br/><em>Award <strong>[1]</strong> for detail (Vlookup table, explanations)</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>:<br/>Type consistency, e.g. currency, integer, floating<br/>Range consistency, depending on the decision procedure,<br/>Value at the limits given by the specification (e.g. &lt; or =&lt;)</p>\n<p><em>Accept any testing that relates to</em></p>\n<ul>\n<li><em>normal data // <strong>Note</strong>: an example will get this mark</em></li>\n<li><em>extreme data,</em></li>\n<li><em>abnormal data.</em></li>\n</ul>\n<p><em><strong>Note:</strong> Answers must be more specific than alpha/beta testing, dry-run, etc</em>.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.4",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations",
      "b-1-the-basic-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>In a small shop two people prepare and sell bread rolls. The space for preparing the bread rolls is small. This space includes a sink and a cash register. Each of the ingredients used to fill the bread rolls is kept in a separate bowl with its own fork or spoon. Sometimes the bowls need to be refilled.</p>\n<p>Preparing a bread roll requires the following steps:</p>\n<ul>\n<li>slice the bread roll</li>\n<li>spread its base with some sauce</li>\n<li>add lettuce</li>\n<li>fill with at most two chosen fillings</li>\n<li>cover the roll with the other half</li>\n<li>wrap it in paper.</li>\n</ul>\n<p>After that, the roll is ready for payment and collection.</p>\n<p>Even when the shop is busy, each person only prepares one bread roll at a time.</p>\n</div><div class=\"specification\">\n<p>A larger restaurant is already using simulation software for the preparation of their dishes.</p>\n<p>The restaurant has a menu with eight possible dishes. These dishes may require a number of steps such as cutting, mixing, boiling and grilling to prepare them, as well as different cooking times. The restaurant has a cook and an assistant who prepare <strong>one</strong> dish at the time.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> problems with this method of preparation that could affect the time it takes the two people to prepare an order.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how the two people could improve the efficiency of their work, without compromising on the quality of service to the customers.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the difference between a model and a simulation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> elements that the simulation software might consider, in addition to the information already described above.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The simulation program will group all orders received in an interval of 10 minutes. The program will then produce a sequence of all the cooking steps so that these orders can be completed as quickly as possible. Once the kitchen has completed the orders for one interval, it is ready to accept orders for the next interval.</p>\n<p>Customers are impressed by the rapidity of service, but not by the quality of prepared food.</p>\n<p>Suggest <strong>two</strong> elements that the software simulation may have not considered that may lead to complaints from the customers.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong>:</em><br/><em>Award <strong>[1]</strong> for identifying a problem, <strong>[1]</strong> for an elaboration.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>There is no evidence that there are two knives to slice the bread roll;<br/>so one of them could already be delayed;</p>\n<p>There could be need of the same fillings;<br/>meaning that the two bread rolls will be prepared with a slight delay;</p>\n<p>One fork/spoon falls down and will need to be washed;<br/>there must be a policy on who will wash it;</p>\n<p>One ingredient is finished and needs replenishment;<br/>there must be a policy on who collects the new bowl;</p>\n<p>There can be concurrent access to the cash register<br/>there must be a policy on who goes first;</p>\n<p>No evidence that both persons have space/paper to wrap simultaneously on the table;<br/>There must be a policy on who goes first;</p>\n<p><em>Accept other reasonable answers</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>:</p>\n<p>Split the job in two separate sections;<br/>one guy handles all the food, the other guy takes order, wraps, handles money;<br/>to minimize possible clashes on resources;</p>\n<p>Grouping similar tasks together:<br/>Slice several bread rolls / stockpile bread rolls;<br/>To be filled on demand;</p>\n<p><em><strong>Note</strong>: Do not award marks for answers related to serving.</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>:<br/>A model is a / an accurate representation (either physical or digital) of a real-world entity;<br/>A simulation is an algorithm/method of implementing a model;<br/>By changing the various parameters/variables of the model;<br/>In order to investigate its subsequent behaviour;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>:<br/>The number of utensils;<br/>Washing times;<br/>Time spent arranging the food on a plate;<br/>The amount / quantity of ingredients<br/>The number of customers/orders waiting<br/>The number of servings for the dish they are currently preparing;</p>\n<p><em>Accept other reasonable answers.</em><br/><em>Accept application answers – option to include/exclude ingredients.</em></p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong>.</em><br/><em>Mark as <strong>[3]</strong> and <strong>[3]</strong></em>.</p>\n<p>The objective of reducing turn-around time,<br/>May lead to hastily prepared dishes,<br/>Which may be lacking in quality.</p>\n<p>Trying to cook a batch of orders in 10 minutes,<br/>May require the pans to be used for multiple dishes,<br/>Impacting the unique taste of these dishes.</p>\n<p>More appliances might need to be switched on, in case of large orders demand;<br/>For example, an oven, which take time to heat up;<br/>Causing some dishes to be undercooked;</p>\n<p>The number of customers/orders;<br/>May be too high;<br/>To be dealt with in 10 minutes;</p>\n<p>Being in charge of preparing a full dish, gives job satisfaction;<br/>Lack of satisfaction may lead to loss of interest in the job;<br/>affecting the quality of the cooking;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.5",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Jackson City University</em> has a Music Department that provides music lessons to students in a number of high schools in the city.</p>\n<p>The <em>Jackson City University</em> Music Department teachers visit the different schools in the city to teach students a range of musical instruments.</p>\n<p>The following diagram shows an unnormalized table of student data.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> benefit of normalizing a database.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> ways that incorrect data could be prevented from being added into the <em>School_phone_no</em> field.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what would be necessary to make the above unnormalized table conform to 1st Normal Form (1NF).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the 3rd Normal Form (3NF) of the unnormalized relation shown above.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the difference between 2nd Normal Form (2NF) and 3rd Normal Form (3NF).</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Normalization leads to a reduction in the chance of data redundancy occurring;<br/>Because each item of data only occurs on one location in the database;<br/>Therefore this can reduce the possibility of update anomalies occurring;<br/>More efficient use of memory;</p>\n<p>Normalization reduces the likelihood of update anomalies occurring;<br/>Because each item of data only occurs on one location in the database;<br/>Therefore if the data is edited there is no chance that the data may exist in its original form anywhere else in the database;</p>\n<p>Normalization leads to smaller tables with less information in each row;<br/>This leads to a reduction of input/output transfers;<br/>Which means that the likelihood of CPU activities being suspended are reduced/the CPU is able to work at full capacity;</p>\n<p><em><strong>Note</strong>: Do not award marks between clusters.</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Use a text field for the School_phone_no with a length of 9 characters (<em>Accept limit check</em>);<br/>Use an input mask such as 000000000 to ensure only numbers can be entered;<br/>Use a validation rule to ensure the first three entries can only be “065”<br/><em>Accept format check</em>;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Some values are not atomic in the <strong>Student_Choice</strong> and <strong>School_phone_no</strong> fields/attributes;<br/>Repeat rows for students with multiple choices / phone numbers with one choice per row;<br/>Each record must have a unique key;</p>\n<p>For <strong>Student_Choice</strong> have three columns, Stud_Choice1, Stud_Choice2 and Stud_Choice3, with null values (or default values set to “none”) as appropriate or two columns for <strong>School_phone_no</strong>;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[8 max]</strong></em>.</p>\n<p><em><strong>Alternative 1</strong></em><br/>For a three table solution:</p>\n<p><strong>Student table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>: student_ID // Do not accept other suggestions for the primary key</em>.<br/><em>Award <strong>[1]</strong> for attributes relating to student e.g. </em></p>\n<p><em>First_Name</em><br/><em>Family_Name</em><br/><em>Date_of_birth</em></p>\n<p><em><strong>Note</strong>: Do not penalize additional attributes such as Student_choice</em>.</p>\n<p><strong>School table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>. Either school_Name <strong>or</strong> user defined key such as SchoolID // Do not accept a composite key</em>.<br/><em>Award <strong>[1]</strong> for attributes relating to school such as: </em></p>\n<p><em>School(School_Name)</em><br/><em>School_Post_code,</em><br/><em>School_phone_no</em>.</p>\n<p><em><strong>Note</strong>: Do not penalize additional attributes such as Student_choice</em>.</p>\n<p><strong>Student choice Table/Instrument table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>. Either user-defined <strong>or</strong> a composite key</em>.<br/><em>Award <strong>[1]</strong> for attributes relating to choice such as Student_choice/Instrument choice</em></p>\n<p><em>Award <strong>[1]</strong> for each foreign key up to a maximum of <strong>[2 max]</strong></em>.</p>\n<p><strong>Example of three table solution</strong><br/><em>STUDENT (<span style=\"text-decoration:underline;\">Student ID</span>, First_Name, Family_Name, Date_of_Birth)</em><br/><em>SCHOOL (<span style=\"text-decoration:underline;\">School_Name</span>, School_PostCode, School_Phone_no)</em><br/><em>STUDENT CHOICE (<span style=\"text-decoration:underline;\">Choice ID</span>, Student_ID*, School_Name*, Choice)</em></p>\n<p><em><strong>Alternative 2</strong></em><br/>For a four table solution</p>\n<p><strong>Student table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>: student_ID // Do not accept other primary keys</em><br/><em>Award <strong>[1]</strong> for attributes relating to student e.g. </em></p>\n<p><em>First_Name</em><br/><em>Family_Name</em><br/><em>Date_of_birth</em></p>\n<p><em><strong>Note</strong>: Do not penalize additional attributes such as Student_choice.</em></p>\n<p><strong>School table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>. Either school_Name <strong>or</strong> user defined key such as SchoolID // Do not accept a composite key</em><br/><em>Award <strong>[1]</strong> for attributes relating to school such as: </em></p>\n<p><em>School(School_Name)</em><br/><em>School_Post_code</em><br/><em>School_phone_no</em></p>\n<p><em><strong>Note</strong>: Do not penalize for additional second School_Phone_no field</em>.</p>\n<p><strong>Student choice Table/Instrument table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>. Either Lesson_ID <strong>or</strong> Instrument_ID // Do not accept a composite key.</em><br/><em>Award <strong>[1]</strong> for attributes relating to choice such as Student_choice/Instrument choice</em>.</p>\n<p><strong>Details Table/Lessons Table</strong><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">primary key</span>. Any ID or composite.</em><br/><em>Award <strong>[1]</strong> for the correct foreign key in this table.</em></p>\n<p><em><strong>Note</strong>: This is the only table in which marks can be awarded for the use of a composite [primary] key</em>.</p>\n<p><strong>Example of four table solution</strong><br/><em>STUDENT (<span style=\"text-decoration:underline;\">Student_ID</span>, First_Name, Family_Name, Date_of_Birth)</em><br/><em>SCHOOL (<span style=\"text-decoration:underline;\">School_Name</span>, School_PostCode, School_Phone_no)</em><br/><em>STUDENTCHOICE(<span style=\"text-decoration:underline;\">Lesson_ID</span>,Instrument) </em><br/><em>DETAILS (<span style=\"text-decoration:underline;\">Lesson_ID</span>*, <span style=\"text-decoration:underline;\">Student_ID</span>*, <span style=\"text-decoration:underline;\">School_Name</span>*)</em></p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>2NF allows non-prime attributes to be functionally dependent on non-prime attributes which means there are transitive dependencies at 2NF;<br/>While 3NF allows non-prime attributes to be functionally dependent only on the primary / super key and is already in 2NF;<br/>Therefore it is not possible for update anomalies to occur when a database is in 3NF;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.3",
    "topics": [
      "option-c-web-science",
      "option-a-databases"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "a-2-the-relational-database-model",
      "a-1-basic-concepts"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Basking Coats</em> is a business that sells textile products such as shirts, coats and trousers. The company was formed in 1970 and has numerous shops in Europe and South-East Asia. To ensure their marketing is targeted at appropriate customers, <em>Basking Coats</em> has asked <em>Singalytics</em>, a data analytics company, to assist them in improving their marketing strategy.</p>\n</div><div class=\"specification\">\n<p>Extract, Transform, Loading (ETL) processes can be used to clean up data for use in a database warehouse. When ETL is carried out, certain precautions should be taken.</p>\n</div><div class=\"specification\">\n<p>Data in the <em>Singalytics</em> data warehouse is stored with a timestamp.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how deviation detection can be used to analyse this data.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why data warehouses tend to use unnormalized data sets.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> precautions to be taken before extraction is carried out on the database.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why data warehousing is time dependent.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why <em>Basking Coats</em> could use association analysis to improve the marketing of its products.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p><em>Basking Coats</em> has decided to use an object-oriented database rather than a relational database to store its data.</p>\n<p>Explain why <em>Basking Coats</em> would use an object-oriented database rather than a relational database to store its data.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Deviation detection is a statistical technique;<br/>Appropriate marketing of product;<br/>Which is used to detect outlying data that does not fit the assumed model;<br/>Therefore it can be used to predict the trends and patterns of demand for certain consumer goods in the future;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>To speed query execution, which can be especially important in data warehouses used by <em>Singalytics</em>;<br/>May effectively be used in data warehouses as they contain pre-joined tables that package data for common uses;<br/>If the data is all present in a single table, there will be no need for joins, hence the selects can be done very quickly;<br/>A single table with all the required data allows much more efficient index usage;<br/>If there is heavy read load and when the application is read intensive;<br/>If the columns are indexed properly, then results can be filtered and sorted by utilizing the same index;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>To retrieve all the required data from the source system with as little resources as possible;<br/>Designed in a way that it does not affect the source system;<br/>In terms or performance/ response time/ locking;<br/>Makes it accessible for processing on the data;<br/>Ensuring that historical data being extracted can be read by the current systems;<br/>Ensuring the different data formats being extracted can all be converted or scrubbed to become readable by the system and able to be formatted;<br/>Ensuring that the data is relevant to what the user wishes to extract and utilise;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>The content in the data warehouse is only valid for a time period;<br/>Because the data undergoes changes dynamically;<br/>A data warehouse's focus on change over time is time variant;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.</p>\n<p><em>Award <strong>[2 max]</strong> for the explanation and <strong>[2 max]</strong> for the example(s)</em>.</p>\n<p><strong>Associations</strong>:<br/>Correlate the presence;<br/>of a set of items with another range of values for another set of variables;<br/>Breaks up data sets by variables such as gender, location, age;<br/>It may be used to detect patterns independently from the geographic region,<br/>females buy more dark colour trousers than males;</p>\n<p><em><strong>Examples</strong></em>:<br/>when a female retail shopper buys a cotton shirt, she is likely to buy a stole.<br/>Associations of the type Full arm formal shirts =&gt; Dark colour trousers; Full arm formal shirts =&gt; cufflinks may produce enough confidence and support to be valid association rules of interest;<br/>If the application area has a natural classification of the item sets into hierarchies,<br/>discovering associations within the hierarchies is of no particular interest;<br/>Specifically its associations across hierarchies;</p>\n<p><em><strong>Note:</strong> For generic responses award <strong>[2 max]</strong></em>.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong></em>.</p>\n<p><em>Award <strong>[3 max]</strong> for the feature(s) and <strong>[3 max]</strong> for supporting </em><em>explanation/examples</em>.</p>\n<p><strong>Features</strong>:<br/>Enhanced modelling capabilities;<br/>Extensibility/support new data types;<br/>Object DBMS stores more complex data and relationships;<br/>Improved performance;<br/>Reusability;<br/>Eliminates need for user defined keys;<br/>Eliminates need for Joins;</p>\n<p><em><strong>Examples</strong></em>:<br/>Inheritance property, we can re-use the attributes (size, fabric) and functionalities;<br/>It reduces the cost of maintaining the same data multiple times;<br/>All this information is encapsulated and, there is no fear being misused by other objects. If we need any new feature we can easily add new class inherited from parent class and that adds new features;<br/>Reduces the overhead and maintenance costs;<br/>So it becomes more flexible if any changes and apparel business changes with fashion needs;<br/>Codes are re-used because of the inheritance feature;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "18M.2.HL.TZ0.4",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-4-further-database-models-and-database-analysis"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Neural networks are often used to model the complex behaviour of systems. These networks must first be trained in order to establish a reliable model. One such method of training makes use of supervized learning algorithms.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the basic steps involved in supervized learning algorithms.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The following block diagram illustrates a neural network used by a supervized learning algorithm to calculate a resulting value RES, for a given set of inputs.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align:left;\">The neural network in the block diagram contains 60 nodes in the input layer (block I), 36 nodes in the hidden layer (block H) and 6 nodes in the outermost layer (block O). The solid black arrows indicate that <em>all</em> nodes in one layer are feeding all nodes in the successive layer. In particular, the value RES is calculated by using all nodes in O.</p>\n<p>Calculate the number of weights that the above neural network uses in producing a value for RES. Show the details of your calculation.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong>:</em><br/>Uses a (training) set of <span style=\"text-decoration:underline;\">input data and known response</span> values;<br/>To <span style=\"text-decoration:underline;\">compare the model's output</span> with actual values from the past which will give an error in the estimate;<br/>The model is then <span style=\"text-decoration:underline;\">changed</span> in order to minimize this error;<br/>This <span style=\"text-decoration:underline;\">process is iterated</span> until a sufficiently accurate model is produced;<br/>Which can then be <span style=\"text-decoration:underline;\">used to make predictions</span>/classifications;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for evidence of multiplication between layers.</em><br/><em>Award <strong>[1]</strong> for fully correct expression / answer</em>.</p>\n<p>60 * 36 + 36 * 6 + 6 * 1 = 2382</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18M.2.HL.TZ0.8",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The electronic control unit (ECU) of a vehicle is an on-board computer that is constantly monitoring the performance of several components of a vehicle. For example, the ECU:</p>\n<ul>\n<li>controls the functioning of the lights, the brakes, the airbag, and the fuel-level signal</li>\n<li>permits the scale being changed in some digital displays, such as switching the speedometer from miles/hour to km/hour.</li>\n</ul>\n<p>The software embedded in an ECU receives input data from a variety of sources when it runs auto-diagnostic tests.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With relation to the activities that the software of an ECU has to perform, identify<strong> two</strong> of the sources that provide input data to the ECU.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With relation to the activities that the software of an ECU has to perform, suggest <strong>one</strong> reason why the auto-diagnostic program in the ECU depends upon the make and model of the vehicle.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Vehicles are tested for their exhaust gas emissions using simulation software at specialist garages. During a period of 5 minutes, a vehicle with the engine switched on is monitored for emissions of carbon dioxide (CO<sub>2</sub>) and fine particulates. The software uses 3D visualization techniques to display these parameters on a screen for the whole duration of the test.</p>\n<p>Explain how emissions of CO<sub>2</sub> and fine particulates could be represented in 3D by the software.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for each of the <strong>two</strong> requested elements that provide input</em>.</p>\n<p><em>Accept any ATD sensor explicitly stated by name.</em><br/><em>Accept: speedometer, fuel gauge, brake pads, human pushing a button.</em><br/><em>Do not accept: temp regulation unit (or any unit that is integrated in ECU).</em></p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>May depend on the car maker and patents may be involved;<br/>For example, internal communications protocols across different systems may not be revealed;</p>\n<p>Different vehicles have different uses;<br/>It doesn’t make sense to have the same firmware for different requirements;</p>\n<p>Different models operate at different parameters,<br/>Requiring different algorithms;</p>\n<p>Different Countries may have different regulations;<br/>Therefore country-specific ECUs for the same model/type of car;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong> for one example with explanations</em>.</p>\n<p><em><strong>Example answers</strong></em>:<br/>The quantity and/or direction of diffusion of CO<sub>2</sub> and fine particulates can be mapped using colour plots;<br/>Over the whole duration of the test in a 3D space;<br/>Choosing a colour in an intuitive way;<br/>To show how the density evolves over time;</p>\n<p><em><strong>Examples</strong></em>:<br/>Plotting 3D points for CO<sub>2</sub> with vectors x,y,z ; // can be given BOD<br/>Where x = time;<br/>y = density of CO<sub>2</sub>;<br/>z = temperature;</p>\n<p><em><strong>Note</strong>: Do not accept generic answers that describe the process of 'rendering'</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.6",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-1-the-basic-model",
      "b-3-visualization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A school teacher decides to write a program to store class records and marks. Part of this program involves using a sort algorithm. The algorithm shown is a selection sort and to test it, the teacher has set up an array <code>VALUES[]</code> with 5 elements of test data.</p>\n<pre>LIMIT = 4<br/><br/>loop COUNTER1 from 0 to LIMIT – 1<br/>MINIMUM = COUNTER1<br/><br/>  loop COUNTER2 from COUNTER1 + 1 to LIMIT<br/>    if VALUES[COUNTER2] &lt; VALUES[MINIMUM] then<br/>      MINIMUM = COUNTER2<br/>    end if<br/>  end loop<br/><br/>  if MINIMUM ≠ COUNTER1 then<br/>    TEMPORARY = VALUES[MINIMUM]<br/>    VALUES[MINIMUM] = VALUES[COUNTER1]<br/>    VALUES[COUNTER1] = TEMPORARY<br/>  end if<br/><br/>end loop</pre>\n</div><div class=\"specification\">\n<p> <img src=\"data:image/png;base64,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\"/><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the table below to trace the algorithm using the data set:<br/>20, 6, 38, 50, 40</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the algorithm in the flow chart, construct this algorithm in pseudocode so that it performs the same function.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the type of sort in the algorithm constructed in b(i).</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm fragment to output the data in the array <code>VALUES[]</code></p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award <strong>[5 max]</strong></em>.<br/>Both <code>COUNTER1</code> and <code>COUNTER2</code> correct;<br/><code>MINIMUM</code> column correct;<br/>Final <code>VALUES[] </code>0, 1, 2 correct;<br/>Final <code>VALUES[]</code> 3, 4 correct;<br/><code>TEMPORARY</code> column correct;</p>\n<p><em>Note to examiners</em>:<br/><em>Allow follow through (FT)</em>.<br/><em>In case of different representation of values in columns COUNTER1 and COUNTER2, then FT, award marks for the correct values of the variables MINIMUM and TEMPORARY</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Use of correct nested loops;<br/>Correct use of flag;<br/>Inner loop checking adjacent cells;<br/>Values being swapped if necessary;</p>\n<p><em>Example algorithm 1</em>:</p>\n<pre>LIMIT = 4<br/>FLAG = TRUE<br/>loop while FLAG = TRUE<br/>  FLAG = FALSE<br/>  loop COUNTER from 0 to LIMIT - 1<br/>    if VALUES[COUNTER] &gt; VALUES[COUNTER + 1] then<br/>      TEMPORARY = VALUES[COUNTER]<br/>      VALUES[COUNTER] = VALUES[COUNTER + 1]<br/>      VALUES[COUNTER + 1] = TEMPORARY<br/>      FLAG = TRUE<br/>    end if<br/>  end loop<br/>end loop</pre>\n<p><em><strong>A recursive solution is allowed at HL. SL candidates who submit an above level recursive solution should also receive credit</strong></em>.</p>\n<p><em>Version 1 – basic recursive solution</em></p>\n<p><em>Award <strong>[3 max]</strong></em>.<br/>BUBBLESORT defined as a procedure with correct pass through parameters and end/return statement;<br/>Correct loop with values swapped if necessary inside procedure;<br/>Recursive call of BUBBLESORT with parameters passed;<br/>Correct condition for recursive call;</p>\n<p><em>Example algorithm 2</em></p>\n<pre>LIMIT = 4<br/>BUBBLESORT(VALUES, LIMIT)<br/>    loop COUNTER from 0 to LIMIT - 1<br/>      if VALUES[COUNTER] &gt; VALUES[COUNTER + 1] then<br/>        TEMPORARY = VALUES[COUNTER]<br/>        VALUES[COUNTER] = VALUES[COUNTER + 1]<br/>        VALUES[COUNTER + 1] = TEMPORARY<br/>    end if<br/>  end loop<br/>  if LIMIT – 1 &gt; 1 then<br/>    call BUBBLESORT(VALUES, LIMIT - 1)<br/>  end if<br/>end BUBBLESORT</pre>\n<p><em>Version 2 – more efficient recursive solution</em></p>\n<p><em>Award <strong>[3 max]</strong>.<br/></em>BUBBLESORT defined as a procedure with correct pass through<br/>parameters and end/return statement;<br/>Correct loop with values swapped if necessary inside procedure;<br/>Recursive call of BUBBLESORT with parameters passed;<br/>Correct condition for recursive call;<br/>Correct use of flag;<em><br/></em></p>\n<p><em>Example algorithm 2</em></p>\n<pre>LIMIT = 4<br/>BUBBLESORT(VALUES, LIMIT)<br/>    FLAG = FALSE<br/>    loop COUNTER from 0 to LIMIT - 1<br/>      if VALUES[COUNTER] &gt; VALUES[COUNTER + 1] then<br/>        TEMPORARY = VALUES[COUNTER]<br/>        VALUES[COUNTER] = VALUES[COUNTER + 1]<br/>        VALUES[COUNTER + 1] = TEMPORARY<br/>        FLAG = TRUE<br/>      end if<br/>    end loop<br/>    if LIMIT – 1 &gt; 1 and FLAG = TRUE then<br/>      call BUBBLESORT(VALUES, LIMIT - 1)<br/>    end if<br/>end BUBBLESORT</pre>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Bubblesort;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Award <strong>[2 max]</strong>.<br/>Use of (any type of) loop;<br/>Correct output statement;</p>\n<p><em>Example algorithm</em>:</p>\n<pre>loop COUNTER from 0 to LIMIT<br/>   output VALUES[COUNTER]<br/>end loop</pre>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Those candidates who are proficient in programming easily got full marks. The others did not complete the exercise or completed it incorrectly.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19M.1.HL.TZ0.15",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An image processing system analyses colour images of architectural landscapes. The system compares any new images with existing ones stored in a knowledge base. The purpose of the system is to extend the knowledge base with images of the same scenes taken either from different perspectives or at different times of the year or with different weather conditions.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how cluster analysis can be used to achieve the aims of the system that is described above.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss the appropriateness of using genetic algorithms to compare the processed images with those stored in the knowledge base.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong>.</em><br/>Cluster analysis will attempt to <span style=\"text-decoration:underline;\">group</span> together images;<br/>Into groups that <span style=\"text-decoration:underline;\">have certain similarities</span>;<br/>This will be achieved by <span style=\"text-decoration:underline;\">looking at characteristics</span>;<br/><span style=\"text-decoration:underline;\">Such as</span> the presence of horizontal/vertical lines/windows/spires etc.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong>:</em><br/><em>Award <strong>[2]</strong> + <strong>[2]</strong> + <strong>[2]</strong></em>.</p>\n<p><em>Award <strong>[2]</strong> for a description of GA, such as:</em><br/>GA uses a <span style=\"text-decoration:underline;\">fitness function</span> (based on stored data);<br/>To validate/discard the “adequacy” of some detected features;</p>\n<p><em>Award <strong>[2]</strong> for one reason for appropriateness with amplification.</em><br/>The features in images have been extracted in previous processing;<br/>And can be represented in a suitable way to be used with genetic algorithms;</p>\n<p><em>Award <strong>[2]</strong> for one reason against appropriateness with amplification.</em><br/>One has to decide which features shall drive the recognition process;<br/>Choosing the wrong feature may lead to incorrect results;</p>\n<p>If the GA merges similar features to build broad classes;<br/>Then the recognition may not be precise enough;</p>\n<p>If the GA focuses on several small details taken in isolation;<br/>A limited number of features may be recognised;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18M.2.HL.TZ0.9",
    "topics": [
      "option-a-databases",
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "a-4-further-database-models-and-database-analysis",
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A two-dimensional array <math style=\"font-family: 'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>CUSTOMERS</mi><mo> </mo><mo>[</mo><mo> </mo><mo>]</mo></math> is used to store customer details for a mail order company such that each row of the array represents a customer record and each column represents a specific field. The fields currently in use are: <code>LASTNAME, FIRSTNAME, ADDRESS1, ADDRESS2, ADDRESS3, CITY, POSTCODE</code>.</p>\n<p>The array currently holds 512 records.</p>\n</div><div class=\"specification\">\n<p>The company wishes to print a number of mailing labels, such as the one shown below, that will go to all customers called <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>Jones</mi></math> who live in <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>Cardiff</mi></math>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>A singly linked list has been created including the following surnames; <code>Bale, Cousens, Davies, Pugh, Williams</code>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the pseudocode that will find how many customers live in the city of <code>Cardiff</code> and display the results.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm that will enable the company to print the mailing labels for all customers called <code>Jones</code> who live in <code>Cardiff</code>.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the steps to insert <code>“Jones”</code> into this singly linked list. You may draw a labelled diagram in your answer.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> example of where a circular linked list would be used in preference to a linear linked list.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.<br/>Initialization of TOTAL before loop and output of result after loop;<br/>Use of any loop with correct limits;<br/>Correct row and column subscripts of two-dimensional array;<br/>Correct comparison in if statement;<br/>Totalling within if statement, if statement inside loop;</p>\n<p><em>Note to examiners: Do not accept flowcharts</em>.</p>\n<p><em>Example algorithm 1</em>:</p>\n<pre>SEARCH = \"Cardiff\"<br/>TOTAL = 0<br/><br/>loop COUNTER from 0 to 511<br/>  if SEARCH = CUSTOMERS[COUNTER][5] then<br/>    TOTAL = TOTAL + 1<br/>  end if<br/>end loop<br/><br/>output \"The number of customers is \", TOTAL</pre>\n<p><em>Example algorithm 2</em></p>\n<pre>TOTAL = 0<br/>loop COUNTER from 0 to CUSTOMERS.length()-1<br/>  if CUSTOMERS[COUNTER][5].equals( \"Cardiff\")<br/>       //accept CUSTOMERS[COUNTER][5]== \"Cardiff\"<br/>       then TOTAL = TOTAL + 1<br/> end if<br/>end loop<br/>output TOTAL</pre>\n<p><em>Note to examiners</em>:<br/><em>The array structure is not given in the question</em>.</p>\n<p><em>If array subscripts begin with 1, then award 1 mark for </em><br/><code>loop COUNTER from 1 to 512</code> <br/><em>and 1 mark for</em> <br/><code>CUSTOMERS[COUNTER][6]</code></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.<br/>Use of any loop with correct limits;<br/>Correct row and column subscripts of two-dimensional array;<br/>Correct comparison in if statement<br/>Output of all row elements(fields)<br/>Output correctly formatted</p>\n<p><em>Note to examiners: Accept flowcharts</em>.</p>\n<p><em>Example algorithm 1</em>:</p>\n<pre>SEARCH1 = \"Jones\"<br/>SEARCH2 = \"Cardiff\"<br/>loop COUNTER from 0 to 511<br/>  if CUSTOMERS[COUNTER][0] = SEARCH1 then<br/>    if CUSTOMERS[COUNTER][5] = SEARCH2 then<br/>      output CUSTOMERS[COUNTER][1] \" \" CUSTOMERS[COUNTER][0]<br/>      output CUSTOMERS[COUNTER][2]<br/>      output CUSTOMERS[COUNTER][3]<br/>      output CUSTOMERS[COUNTER][4]<br/>      output CUSTOMERS[COUNTER][5]<br/>      output CUSTOMERS[COUNTER][6]<br/>    end if<br/>  end if<br/>end loop</pre>\n<p><em>Example algorithm 2</em>:</p>\n<pre>R=0<br/>loop while R &lt;= 511<br/>   if CUSTOMERS[R][0]==\"Jones\" and CUSTOMERS[R][5]== \"Cardiff\"<br/>      then<br/>        output CUSTOMERS[R][1], CUSTOMERS[R][0]<br/>        loop K from 2 to 6<br/>          output CUSTOMERS[R][K]<br/>        end loop<br/>      end if<br/>   R=R+1<br/>end loop</pre>\n<p><em>Note to examiners</em>:<br/><em>Since “Jones” can be either a first or last name, accept</em><br/>       <code>CUSTOMERS[COUNT][1] == SEARCH1</code><br/><em>and the first output line</em><br/>     <code>output CUSTOMERS[COUNT][0], CUSTOMERS[COUNT][1]</code></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.<br/><em>Accept correct answers given as diagrams or written in text applying the following marking points</em>:</p>\n<p><em>Award <strong>[5 max]</strong> if candidate explained the steps to insert </em><code>“Jones”</code><em> into sorted list </em><em>at correct place</em>.<br/><em>Award <strong>[1]</strong> for each of the following steps, up to [5]</em></p>\n<p>Create a new node with data field <code>Jones</code> and pointer field;<br/>Start searching from the beginning of the list;<br/>Find the location/position where the new node is to be inserted (<code>Jones</code> to be inserted after <code>Davies</code>);<br/>Set the pointer in the new node (containing Jones) to the pointer in the node containing Davies (to point to the node containing <code>Pugh</code>); <br/>Set the pointer in the node containing <code>Davies</code> to point to the new node (<code>Jones</code>);</p>\n<p><em>Award <strong>[3 max]</strong> if candidate explained only the steps to insert </em><code>“Jones”</code><em> at the end of the list</em>.<br/><em>Award <strong>[1]</strong> for each of the following steps</em></p>\n<p>Create a new node with data field <code>Jones</code> and pointer field NIL;<br/>Start searching from the beginning of the list to find the last node (<code>Williams</code>);<br/>Set the pointer in the last node (containing <code>Williams</code>) to point to the new node (<code>Jones</code>);</p>\n<p><em>Award <strong>[3 max]</strong> if candidate explained only the steps to insert “Jones” at the beginning of the list</em>.<br/><em>Award <strong>[1]</strong> for each of the following steps, up to <strong>[3]</strong></em></p>\n<p>Create a new node with data field <code>Jones</code> and pointer field;<br/>Set the pointer in the new node (containing <code>Jones</code>) to the external pointer (which points to the beginning of the list/to the first node in the list (<code>Bale</code>));<br/>Set the external pointer to point to the new node (<code>Jones</code>);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award marks for the use of circular lists (rather than linked lists) in an application (accept examples);</em><br/><em>in which any node can be a starting point / which traverses the whole list by </em><em>starting from any point;</em><br/><em>to repeatedly go around the list;</em></p>\n<p>Used to allow multiple applications to run in a PC;<br/>The operating system cycles through each one in time giving each a slice of time to execute;</p>\n<p>Used for the implementation of a (circular) queue;<br/>The end of the queue points to the beginning, eliminating the need to maintain both front and rear pointers;</p>\n<p>Used in multiplayer gaming environment;<br/>The OS cycles through one player at a time using time slicing;</p>\n<p>A media playlist that repeats (endlessly);<br/>The last song (node) points to the first song;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Those candidates who are proficient in programming easily got full marks. The others did not complete the exercise or completed it incorrectly.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Part (c) seems to have split the cohort; strong candidates often responded with full and correct answers, however there were many responses that were completely wrong.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not many candidates were able to outline a good example of the use of a circular list in preference to a linear linked list in Part(d).</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19M.1.HL.TZ0.17",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Neural networks.</p>\n<p>Genetic algorithms and neural networks are being used in a variety of scenarios. For example, a genetic algorithm may be used to organize timetables for trains whereas a neural network may be used to predict fluctuations between the exchange rates of different currencies.</p>\n</div><div class=\"specification\">\n<p><strong>Figure 1</strong> shows an example of a neural network. It includes inputs, a hidden layer and outputs.</p>\n<p style=\"text-align: center;\"><strong>Figure 1: A neural network</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"specification\">\n<p>Many toy companies are considering the use of machine learning using either supervised learning or unsupervised learning. <em>MAGS</em>, a large IT software company, has recently developed <em>A Doll Called Alicia</em> that allows children to interact with it.</p>\n</div><div class=\"specification\">\n<p><em>A Doll Called Alicia</em> uses machine learning to ensure the child can have the best possible communication with the doll.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the difference between a genetic algorithm and a neural network.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> ways in which the neural network could be modified that may improve its performance.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the difference between supervised learning and unsupervised learning.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the machine learning capabilities of <em>A Doll Called Alicia</em> may lead to instances when the child and the doll cannot communicate effectively.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Companies such as <em>MAGS</em> are considering products that use unsupervised learning rather than supervised learning.</p>\n<p>Explain the benefits of unsupervised learning in developing products such as <em>A Doll Called Alicia</em>.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>A genetic algorithm works in the same way as an evolutionary process whereby it starts with a large population;<br/>And uses an iterative process where the fitter solutions are selected and input into the next cycle until the exit criteria are satisfied;</p>\n<p>Whereas neural networks attempt to mimic the process of the brain;<br/>And can be used/trained to recognize patterns;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Increase the number of inputs;<br/>Increase the number of hidden layers;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Supervised learning is when the outcome related to a given input is already known;<br/>And so, the “learner” can recognize objects and name them based on the labels already given;</p>\n<p>Whereas unsupervised learning is when no examples of outcome are given to help with the learning;<br/>And so, the “learner” must deduce its own solutions e.g. classifying similar objects by colour or shape;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>The language of the child may not have been programmed into the doll;<br/>The child may not speak clearly;<br/>The child’s language may not be sufficiently developed to apply syntax correctly;<br/>The child may refer to something not in Alicia’s “recorded” content, or that Alicia has not previously “learnt”;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/>Unsupervised learning can be used for bridging the causal gap between input and output observations;<br/>Instead of finding the causal pathway from inputs to outputs;<br/>By building the model upwards from both sets of observations;<br/>In the hope that the gap is easier to bridge in the higher levels of abstraction;<br/>Possible to learn larger and more complex models;<br/>e.g. the connection between two sets of observations;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to provide a reasonable description of differences between a genetic algorithm and neural network.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The majority of the candidates answer this question correctly.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The majority of the candidates were able to provide a reasonable description of supervised and unsupervised learning. However, a few of them were unable to develop the responses beyond the generic points.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The majority of the candidates were unable to provide a reasonable response for this question. The responses were mostly limited to a few generic points.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates reasonably answered this question. However, the majority of the responses lacked variety in points specific to the question. The responses also lacked further explanation and were mostly limited to the identification of points and some description.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19M.2.HL.TZ0.8",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><strong>Figure 2</strong> below shows a web graph that is a simplified representation of the World Wide Web.</p>\n<p style=\"text-align: center;\"><strong>Figure 2: a simplified representation of the World Wide Web</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"specification\">\n<p>Web crawlers move through the web, indexing pages to provide information for search engines. When a web crawler arrives at a page it uses several criteria to decide whether to index that page or not.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the nodes that represent web pages in the strongly connected core (SCC).</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the nodes that represent web pages connected by a tube.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why web page E would be given a higher ranking than web page C using the PageRank algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> criteria that may be used by a web crawler to decide whether to index a web page or not.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The following information shows the number of active users (in millions) on different social media sites.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p>Discuss whether the application of power laws is appropriate to predict the future number of active users on these social media sites.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>D, E, F, G;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>H &amp; K;<br/><em>Accept H &amp; L &amp; K</em>;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Both pages have the same number of in-links (i.e. 2);<br/>However, the links to page E come from pages that have a greater number of in-links than the pages that link to C / E is connected with SCC while C has two in-nodes / in-links;<br/>The PageRank algorithm counts links to pages recursively;<br/>A PageRank algorithm will give a greater weighting to the pages that link to E;<br/>Therefore, the algorithm will place E higher up the ranking than C;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Whether there are any meta-tags present that restrict / guide indexing (e.g. a “robot exclusion protocol”);<br/>Whether there is a robots.txt file linked to the page that gives instructions to the web crawler;<br/>Whether the page has broken / dead links;<br/>Whether the page content / meta information matches any specialism/type sought by the web crawler (e.g. some crawlers specifically target academic content);<br/>There is no header with meta-tags;<br/>Whether the page has ever been indexed before;<br/>Whether the page has changed since it was last indexed;<br/>How long ago / how frequently the page has been indexed (web-crawlers will tend not to index pages too frequently as this increases load on the web server);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.</p>\n<p><strong>Reasons why power laws may be appropriate</strong><br/>The number of users shown for each site appears to follow the general pattern of a power law distribution / is consistent with the general principles of power laws (the “rich get richer”);<br/>It's likely/reasonable to assume that sites with large numbers of users will tend to attract more new users than sites with fewer users;<br/>This may be particularly true of social media sites where a high number of existing users may equate to a more diverse/engaging/attractive experience for new users;</p>\n<p><strong>Reasons why power laws may not be appropriate</strong><br/>However, correlation does not equal causation. / Just because the sites appear to exhibit a power law distribution, it doesn't mean that their growth is governed by a power law;<br/>Other factors may be more significant (e.g. the demographic / region a social media site attracts, changing fashion, the policies of the sites themselves);<br/>Sites that were very popular in the past but diminished/died-out may suggest that power laws are not the only / main factor influencing future development (e.g. MySpace);</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates answered this correctly.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates answered this correctly.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates answered this correctly but some missed making clear reference to the actual links in the question.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates answered this correctly though a few missed covering all the specifics of the question.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates did not structure the answers so they could not meet the requirement of the specifics of this question.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19M.2.HL.TZ0.12",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-5-analysing-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A mobile phone has been developed with its own dedicated operating system and is to be used as part of a smart home system in Singapore. The smart home system includes a centralized air conditioning system, a burglar alarm and a surveillance system.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> functions of an operating system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> benefit of using a dedicated operating system on the mobile phone instead of a generic operating system.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how sensors and microprocessors are used to ensure that the air conditioning system is able to maintain a constant temperature in the smart house.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The developers of the smart home system are considering developing a smart home system that uses a distributed control system to manage the temperature.</p>\n<p>Contrast the use of a distributed air conditioning system with a centralized air conditioning system for maintaining a constant temperature in the smart home.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Managing memory;<br/>Managing peripherals;<br/>Processor management;<br/>Scheduling;</p>\n<p><em>Note to examiners: allow any valid feature of an operating system</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p><em>Example 1</em><br/>A dedicated operating system for a mobile phone will take up less storage space than a full-sized operating system;<br/>This will allow the device to function more quickly;<br/>Because it doesn’t contain features that aren’t needed;</p>\n<p><em>Example 2</em><br/>A dedicated operating system for a mobile phone can be customized;<br/>Benefits the end users;<br/>As they deal with a familiar interface;</p>\n<p><em>Example 3</em><br/>The dedicated OS is designed specifically for the mobile phone (hardware equipment);<br/>This avoids compatibility issues;<br/>While a generic operating system is designed for multiple types of hardware (which can lead to compatibility / various issues);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/>A desired temperature is input/pre-set by the user;<br/>Sensors detect temperature;<br/>And regularly/continuously send (temperature) readings to the microprocessor;<br/>The microprocessor compares the actual readings with the pre-set (by the user) / input temperature;<br/>If the temperature is too hot/too cold the microprocessor sends signal to actuator;<br/>To adjust temperature (no need for complex details regarding actions of a heating/cooling system);</p>\n<p><em>Note to examiners: Award <strong>[1]</strong> for evidence of the use of analogue-to-digital converter (sensors) and/or digital-to-analogue convertor(actuators)</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/><em>Award [1] for each contrast/comparison, up to [3]</em>.</p>\n<p>Centralized system allows control at a central point, which is then transmitted to the various rooms whilst distributed system allows individual control of settings at each unit / room so is more flexible // Centralized system will have same or constant temperature throughout the home whilst distributed system allows individual control of setting at each room;</p>\n<p>Easier to control settings centrally whilst distributed system is more complicated to control remotely (due to limited / no connectivity of the control system);</p>\n<p>Centralized system is more difficult to install due to the connectivity required between the various components whilst distributed system is easier to install due to fewer connection issues;</p>\n<p>If a computer system / or any connection fails in the centralized system, the whole system is not able to function correctly whilst the distributed system as whole would still function correctly;</p>\n<p>Centralized system is cheaper / has lower operational cost whilst distributed system is expensive as it requires additional hardware / software;</p>\n<p>Centralized systems are difficult to expand whilst distributed systems are easily expandable because self-sufficient systems can be added or removed at any point in time without affecting the overall system;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>All parts were mostly well answered although many candidates lost marks for repetition and using vague common-sense terminology, rather than technical and complete terminology.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19M.1.HL.TZ0.16",
    "topics": [
      "topic-6-resource-management",
      "topic-7-control"
    ],
    "subtopics": [
      "6-1-resource-management",
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Machine translators are regularly used to translate text from one language to another. In spite of the advances that have been made in this field, the output may still need to be proofread by a human.</p>\n<p>Describe <strong>two</strong> problems that these translators may encounter when translating from one language to another.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.<br/>Not all text can be translated literally;<br/>Humans can bend the rules to make produce sense in the target language;</p>\n<p>Machine translators can only use the data that has been programmed into them;<br/>Cannot translate anything outside their knowledge base, e.g. obscure/new words/phrases;</p>\n<p>Knowing which tone/register to use depends upon experience;<br/>That machine learning algorithms do not yet have;</p>\n<p>There are several 1000 different languages;<br/>Linking all of them all is too big a task;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.2.HL.TZ0.10",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> roles that a computer can perform in a network.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Client;<br/>Server/email server/DNS server/file server;<br/>Router;<br/>Firewall;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were generally able to identify roles that a computer can perform in a network. However, some candidates incorrectly described actions that a computer performs in a network rather than stating actual roles.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.1",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Home banking allows individuals to perform operations over the Internet on their own bank accounts.</p>\n</div><div class=\"specification\">\n<p>Access to a bank’s home banking services requires, as a first step, identification and authentication of the user. Individuals log on the bank web site, and enter their own personal space by providing their full account number and a personal code that the bank gave them. The processing of this information takes place on the server side.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the relationship between the Internet and the world wide web (WWW).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the choice of browser should not affect a customer’s ability to access their bank account details.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>two</strong> features that make HTTPS more suitable than HTTP in the context of home banking.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why server-side processing is used in this case.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The 20 most recent account transactions can be displayed on screen in a webpage that uses XML. A print-out of <strong>all</strong> transactions of the past three months may be obtained by clicking an onscreen button on the webpage. The print-out is landscape oriented and shows many more columns than are displayed on the screen.</p>\n<p>Describe how this processing takes place with reference to the use of XML and XSLT.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em></p>\n<p><em>Award<strong> [1 max]</strong> for Internet and <strong>[1 max]</strong> for WWW</em>.</p>\n<p>Internet is a network of (networks of) computers that can communicate one with each other;<br/>To exchange/access information through the WWW;</p>\n<p>The WWW is a way to access/share/exchange information using software applications;<br/>Using the Internet as a physical medium;</p>\n<p>Internet allows the transmission of data;<br/>That constitute the information that applications on the WWW may want to share/access/exchange;</p>\n<p>The WWW provides, through hyperlinks, a level of connectivity of resources (logical connectivity);<br/>Which can be physically sparse, but connected in a network in the Internet;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>:</p>\n<p><em>Award <strong>[1]</strong> for defining the term “standards” and award <strong>[1]</strong> for a development up to <strong>[2 max]</strong></em>;</p>\n<p><em>Award <strong>[1]</strong> for defining the term “protocol(s)” and award <strong>[1]</strong> for a development up to <strong>[2 max]</strong></em>;</p>\n<p>Standards are applied by the browser;<br/>For interpreting the HTML (XML);<br/>So that the all information will appear, and also (more or less) as expected;</p>\n<p>Protocols are used;<br/>To build up the communication at different levels of the architecture;<br/>All browsers will rely upon the same internet protocols (TCP/IP);<br/>That is essential for interoperability in transmission/communication;<br/>So that the IP address is retrieved (via the DNS server);</p>\n<p><em>Award <strong>[2 max]</strong> for a generic response</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>:<br/>HTTPS <span style=\"text-decoration:underline;\">authenticates</span> the web site;<br/>HTTPS <span style=\"text-decoration:underline;\">encrypts</span> the data that need to be transferred;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>:<br/>The bank needs to store all passwords in its server, including the credentials given to the users;<br/>So that the comparison with the individual’s entry happens in the bank with the local database;<br/>To the purpose of guaranteeing security;<br/>And to possibly perform other operations (tracking log-ins or transactions);</p>\n<p>The bank cannot send out password to be processed on the client’s side;<br/>This will not be a guarantee for security for the bank/it may introduce vulnerabilities/sensitive data cannot be sent out in the public domain;<br/>Hence the comparison with the individual’s entry must happen in the bank with the local database;</p>\n<p><em><strong>Note:</strong> Do not award marks between clusters</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>:<br/>XML is used to create/organize the data on the internal database;<br/>By clicking the virtual button a script is run that transforms the information on the database into the print-out form;<br/>Stylesheet in <span style=\"text-decoration:underline;\">XSLT</span> transforms XML into an output form;<br/>The script contains instructions on how to access the database (which fields are relevant) and how to present the information for the final form for the printer;</p>\n<p>By clicking the virtual button a script is run;<br/>Which uses XML to retrieve/select the required data from the internal database/server;<br/>Which is displayed using XSLT into an appropriate output form (on the screen);<br/>The script also contains instructions for the correct printout of data;</p>\n<p><em><strong>Note:</strong> Do not award marks between clusters.</em></p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.7",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Full details of the customers are stored as objects of the Customer class. This class is partially shown below:</p>\n<pre><strong>public class</strong> Customer<br/>{<br/><strong>  private</strong> String memberId;<br/><strong>  private</strong> String email; //email address (assume only 1 per customer)<br/><strong><br/>  public</strong> Customer(String a, String b)<br/>  {<br/>    memberId = a;<br/>    email = b;<br/>  }<br/><strong>  <br/>  public</strong> String getMemberId()<br/>  {<br/><strong>    return</strong> memberId;<br/>  }<br/><strong><br/>  public</strong> String getEmail()<br/>  {<br/><strong>    return</strong> email;<br/>  }<br/>}</pre>\n<p>The objects can be accessed through the linked list <code>allCustomers</code> which is declared in the main (driver) class as follows:</p>\n<p><code>LinkedList&lt;Customer&gt; allCustomers = new LinkedList&lt;Customer&gt;()</code></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why a linked list structure has been chosen for <code>allCustomers</code>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method<code> goldMails()</code> that will return an <code>ArrayList</code> containing the email addresses of all current “Gold” members. You should make use of any previously defined methods.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><span style=\"text-decoration:underline;\"><em>Example 1</em><em>:</em></span><br/>The link list will enable space to be allocated dynamically/will only take up the space required;<br/>Because the number of customers is unknown / customers may join or leave the loyalty scheme during the year;<br/><span style=\"text-decoration:underline;\"><em>Example 2</em><em>:</em></span><br/>Customers / objects / nodes can be easily added or deleted;<br/>So customers may join or leave the loyalty scheme during the year;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre><strong>public</strong> ArrayList goldMails()<br/>{<br/>  ArrayList al = new ArrayList(); // Allow use of &lt;String&gt; identifier<br/>  <strong>int</strong> s = allCustomers.size();<br/>  <strong>int</strong> x = 0;<br/>    <strong>while</strong>(!(allPoints[x] == null)) // Allow(allPoints[x]!=null)<br/>  {<br/>    <strong>if</strong> (allPoints[x].isGold())<br/>    {<br/>      String a = allPoints[x].getMemberId();<br/>      <strong>for</strong> (<strong>int</strong> y = 0; y &lt; s; y++)<br/>      {<br/>        String b = allCustomers.get(y).getMemberId();<br/>        <strong>if</strong> (a.<strong>equals</strong>(b)) // allow =<br/>          al.add(allCustomers.get(y).getEmail());<br/>      }<br/>    }<br/>    x = x + 1<br/>  }<br/>  <strong>return</strong> al;<br/>}</pre>\n<p><em>Award marks as follows:</em><br/>Declaring a new <code>ArrayList</code> (with or without the String identifier);<br/>Looping through the <code>allPoints</code> array;<br/>Testing for “Gold”;<br/>Loping through the <code>allCustomers</code> linked list (using the size or an iterator);<br/>Correct check for identical ids;<br/>Adding the email to the <code>ArrayList</code>;<br/>Returning the arraylist;</p>\n<p><em><strong>Note</strong><strong>:</strong></em></p>\n<ul>\n<li><em>Do not allow any methods that are not part of the LinkedList or ArrayList library classes – e.g. the length function for the linked list is invalid.</em></li>\n<li><em>Do not penalize more than once for omitting the “gets”.</em></li>\n<li><em>Allow the for loop: <code>for</code> (<code>Customer currCust : allCustomers</code>).</em></li>\n<li><em>Allow for loop instead of a while loop.</em></li>\n</ul>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18M.2.HL.TZ0.18",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Describe <strong>one</strong> method of implementation for a new computer system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em></p>\n<p>Parallel;<br/>old system and new system are operated at the same time until the current system is proved to be successful;</p>\n<p>Pilot;<br/>the new (whole) system is operated in one branch/part of the organization before it is rolled out to the whole organization;</p>\n<p>Direct;<br/>the new system replaces the old system in an immediate switchover;</p>\n<p>Phased;<br/>the new system in phases / stages, gradually replaces parts of the old system until the current system is completely replaced by the new system;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>The vast majority of candidates were able to name a method of implementation for a new computer system and then went on to describe it, to achieve both marks. Some candidates, however, successfully named a method, but the description given could have applied to any method of implementation, so it was not specific enough for the second mark.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.2",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Draw the logic circuit represented by the following truth table.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em></p>\n<p><em><strong>Note</strong>: There could be many answers that are correct</em>.</p>\n<p><em><strong>Example 1</strong></em></p>\n<p style=\"text-align:center;\"><em><strong><img src=\"data:image/png;base64,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\"/></strong></em></p>\n<p style=\"text-align:left;\">Correct inputs and XOR gate;<br/>NOT gate, correct final output and link from XOR gate;<em> <strong><br/></strong></em></p>\n<p style=\"text-align:left;\"><em><strong>Example 2</strong></em></p>\n<p style=\"text-align:left;\"><em><strong><img src=\"data:image/png;base64,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\"/></strong></em></p>\n<p style=\"text-align:left;\"><em><strong><img src=\"data:image/png;base64,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\"/></strong></em></p>\n<p style=\"text-align:left;\"><em><strong>Award [2] max.<br/></strong></em></p>\n<p style=\"text-align:left;\"><em>2 marks, 1 mark for each correct input in OR gate</em><br/><em><strong>Note</strong>: in this example the two inputs in OR gate are (NOT A AND NOT B )and (A AND B).</em><br/><em><strong>1</strong> mark for drawing any 3 gates (complete with inputs and outputs)</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A wide range of correct logic circuits were seen in answer to this question, including logic gates from the syllabus – the XOR and NOT gates, as well as above level responses, including the XNOR gate. Some candidates incorrectly used regular OR or NOR gates in their solutions.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.3",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Many city authorities have realised the opportunities that social media and collective intelligence can provide.</p>\n<p>Narayan City is considering using data gathered from citizens using traffic apps on their GPS-enabled mobile devices to help plan future changes to the layout of the road network in and around the city.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage for Narayan City of using collective intelligence to solve complex problems such as changing the layout of roads.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Many social networking sites allow users to classify their posts and responses, for example by adding one or more “hashtags”.</p>\n<p>To what extent does the increasing number of social networking sites and the creation of folksonomies contribute to web users modifying their online behaviour?</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Enables Narayan City to gather data from many more sources than would be possible through the use of traditional data-gathering methods;<br/>This will enable far more information to be gathered far more quickly than using traditional methods;</p>\n<p>Data is generated proactively by citizens' actions;<br/>Therefore, data more likely to reflect what citizens actually do rather than what they might claim they do;<br/>Improves the accuracy of the data;</p>\n<p>Existing strategies used by citizens can be incorporated into future planning;<br/>for example, using side-roads to optimize journey times/timing of journeys to avoid major congestion;<br/>Thus, helping to ensure that future plans are optimized / tailored to citizens’ needs;</p>\n<p>Data gathering and analysis can be automated;<br/>Therefore, it is very cost effective;<br/>Thus, it is able to be scaled / increased without major impacts on budget;</p>\n<p>Data monitoring and collection is real-time;<br/>Therefore, the effect of implementing plans will be reflected by visible changes in use patterns / journey times / routes taken;<br/>Thus, the city council can get reliable feedback/evaluation of how effective their plans are;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p><strong>Possible changes in user behaviour</strong></p>\n<ul>\n<li>users can create their own hashtags, allowing the opportunity for issues/topics to be classified that subsequently rise in prominence/trend among a wider range of social media users/increases the chance of users' concerns being noticed and addressed;</li>\n<li>contrasting/conflicting hashtags can spark debate and discussion among users, allowing users to be exposed to perspectives and arguments that they would otherwise miss / be unaware of;</li>\n<li>Users interact with content related to their interests / views by following hashtags / social media accounts they know align with their interests / views</li>\n<li>Hashtags / social media accounts provide opportunity / platform / communication channels to connect with people for collaboration</li>\n<li>Folksonomies / hashtags / social media / web 2.0 has given all users opportunity to create instead of simply seeking information;</li>\n<li>trending hashtags may make users with opposing points of view less willing to expose their dissent / more likely that users just follow the trend;</li>\n<li>Social media allows people to be anonymous and hence more likely to attack/hurt others</li>\n<li>users including certain hashtags may find themselves open to attack/abuse from other human users / automated bot accounts (e.g. “trolling”);</li>\n<li>hashtags are open to misuse and manipulation (e.g. \"bot\" accounts flooding social media with tagged posts / users stuffing posts with multiple lines of the same hashtag to create a false \"trend\");</li>\n<li>users who follow/search on popular hashtags may find themselves \"bubbled\" – i.e. they are only ever exposed to viewpoints that reflect their own existing perspectives;</li>\n<li>Content people share is moving towards viral / inappropriate content to attract attention</li>\n<li>Followers of popular accounts / hashtags tend to be influenced by the views / content of the account / hashtag (e.g. popular YouTube accounts may start advertising products)</li>\n</ul>\n<p><em>Accept other reasonable answers that convey similar ideas</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates answered this correctly.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates answered this correctly.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "19M.2.HL.TZ0.13",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-6-the-intelligent-web"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> reasons why patches may be necessary for an operating system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> methods that can be used to obtain these patches.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Allows bugs/error in operating system to be repaired;<br/>Allows new features to be added to operating system (such as security updates, improving functionality, improving usability, etc.);<br/>Allows compatibility issues to be improved;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Automatic patches/updates sent (via internet);<br/>User requested updates (via internet);<br/>Patches sent on CD/DVD/memory stick;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to identify at least one reason why patches may be necessary for an operating system, with a large proportion correctly naming two reasons.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates correctly identified one or two methods of obtaining patches, for example, by downloading them or installing them from an external device. However, many candidates incorrectly gave the same method twice, for example, by suggesting two methods of downloading patches.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "21M.1.SL.TZ0.4",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following recursive method.</p>\n<p><code><strong>public void</strong> recursionEx(<strong>int</strong> x)</code><br/><code>{</code><br/><code><strong>  if</strong>(x != 0)</code><br/><code>  {</code><br/><code>    System.out.println(x);</code><br/><code>    recursionEx(x - 1);</code><br/><code>    System.out.println(x);</code><br/><code>  }</code><br/><code>}</code></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> essential features of a recursive algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the following table to show the output when the method is called by: <code>recursionEx(3)</code>;</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain what would happen if the method was called by <code>recursionEx(-3)</code>.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> features which should be included in either program code or UML diagrams to help programmers understand and modify programs in the future.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The method calls itself;<br/>With a changing parameter;<br/>There is a base (terminating) case;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award marks as follows:</em><br/>Outputting 6 values;<br/>Correct parameter list<br/>First 3 output numbers correct;<br/>Last 3 output numbers correct;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>An infinite loop / program will never end/terminate;<br/>Will eventually cause the program to crash;<br/>As it runs out of memory for the parameters stored / Stack overflow;<br/>As the base case is never reached;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Comments can be added;<br/>Use of meaningful names;<br/><span style=\"text-decoration:underline;\">Correct</span> indentation;<br/>Relationships/dependencies shown between class (UML) diagrams;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18M.2.HL.TZ0.19",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The organizing school arranges for visiting students to stay with a host family. The information about each of the visiting students is stored in a file. The student records are sorted by name. Some of the other variables included are school, gender, age and host family name, as shown in the UML diagram below.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>A program needs to be written to match visiting students with host families. The matching process requires data to be manipulated extensively (adding, editing, deleting). The file will be read into RAM.</p>\n<p>This program will be used for different events with different numbers of visitors. Therefore, it will be implemented using a dynamic data structure.</p>\n</div><div class=\"specification\">\n<p>It has been decided to use a single linked list named <code>guests</code> to store and manipulate the <code>Visitor</code> objects.</p>\n</div><div class=\"specification\">\n<p>Consider the following diagram which represents the list <code>guests</code>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>The following recursive method has been written to act on the list <code>guests</code>.</p>\n<pre><strong>public void</strong> recursive(<strong>int</strong> k, <strong>char</strong> a)<br/>{<br/><strong>  if</strong> (k == guests.size())<br/>  {<br/><strong>    output</strong> (\"no such record\");<br/>  }<br/><strong>  else<br/></strong>  {<br/>    Visitor current = guests.get(k);<br/><strong>    if</strong> ((current.getGender() == a) <strong>&amp;&amp;</strong> (current.getAge() &gt; 15))<br/>    {<br/><strong>      output</strong> current.getName();<br/>    }<br/><strong>    else</strong><br/>    {<br/>      recursive(k + 1,a);<br/>    }<br/>  }<br/>}</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>object reference</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason why a linked list may be more suitable than a binary tree in this particular situation.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the code needed to instantiate an object <code>guests</code> of the <code>LinkedList</code> class.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the code for the method<code> penultimate()</code> that returns the second to last element in the linked list <code>guests</code>. You may assume that <code>guests</code> is locally accessible.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using the data provided in the diagram, trace the call <code>recursive(0,'F')</code>, clearly showing the levels of recursion.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason why the use of a recursive method may be inappropriate for linked lists.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Due to unforeseen circumstances, schools may cancel their participation in an event at the last minute. Therefore, a method is needed to remove all the visiting students of one school from the linked list <code>guests</code>.</p>\n<p>Construct the method <code>removeSchool</code> that takes the name of a school as parameter and removes all students of that school from the list <code>guests</code>.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>A reference is a variable whose value points to the location of an object (in memory); </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>The ease of which pointers or references can be manipulated in a linked list;</em><br/><em>Allows easier addition/deletion of objects (compared to a binary tree);</em><br/><em>It can be difficult to manipulate pointers or references in a binary tree;</em><br/><em>Which makes it more difficult to add or delete objects in a binary tree;</em> </p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <em>Allow missing final parentheses</em>.<br/><code>LinkedList&lt;Visitor&gt; guests = <strong>new</strong> LinkedList&lt;Visitor&gt;()</code>;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for correct signature.</em><br/><em>Award <strong>[1]</strong> for using <code>size()</code> or finding the correct size of the linked list *</em><br/><em>Award <strong>[1]</strong> for correctly returning the penultimate element.</em><br/><em>Award <strong>[1]</strong> for correctly returning null if there is no penultimate element in the list.</em></p>\n<pre><strong>public</strong> Visitor penultimate(LinkedList guests) // parameter<br/>is optional<br/>{<br/>  Visitor result = <strong>null</strong>;<br/><strong>  if</strong>(guests.size() &gt; 1) {<br/>    result = guests(guests.size()-2);<br/>  }<br/><strong>  return</strong> result;<br/>}</pre>\n<p><em>*Note to examiners – allow a method that will iterate through the class as long as a valid LinkedList class methods are used.</em></p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for showing the second call to the method</em><br/><em>Award <strong>[1]</strong> for showing the third call to the method (and no subsequent calls)</em><br/><em>Award <strong>[1]</strong> for including the correct test outcome</em><br/><em>Award <strong>[1]</strong> for correct output</em></p>\n<pre>recursive(0,'F')<br/>  k = 0<br/>  current is Ana<br/>  test incorrect<br/>  recursive(1,'F')<br/>      k = 1<br/>      current is Ben<br/>      test incorrect<br/>      recursive(2,'F') <br/>        k = 2<br/>        current is Defne<br/>        test correct<br/>        output Defne Tasci</pre>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>For a large linked list, this would require a large number of recursive calls / become very memory intensive;</em><br/><em>Which may cause stack overflow;</em></p>\n<p><em>A linked list only allows for sequential access;</em><br/><em>Therefore, using recursion would not lead to any gains in efficiency;</em></p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for the correct initialisation of index.</em><br/><em>Award <strong>[1]</strong> for correct loop/iterator declaration and initialisation</em><br/><em>Award <strong>[1]</strong> for the correct assignment to current.</em><br/><em>Award <strong>[1]</strong> for the correct comparison of school (allow use of =)</em><br/><em>Award <strong>[1]</strong> for the correct remove (allow correct use of next pointer)</em><br/><em>Award <strong>[1]</strong> for the decrementing index after remove.</em><br/><em>Award <strong>[1]</strong> for correct increment of index / use of <code>iterator.next()</code>. Within context.</em></p>\n<p><em>Example answers</em>:</p>\n<pre><strong>public void</strong> removeSchool(String school) {<br/><strong>  int</strong> index = 0;<br/>  Visitor current;<br/><strong>  while</strong>(index &lt; guests.size()) {<br/>    current = guests.get(index);<br/><strong>    if</strong>(current.getSchool().equals(school)) {<br/>      guests.remove(index);<br/>               index--;<br/>    }<br/>    index++;<br/>  }<br/>}<br/><strong><br/>public void</strong> removeSchool(String school) {<br/><br/>      Visitor current;<br/><strong>      for</strong>(<strong>int</strong> index = 0; index &lt; guests.size();index++) {<br/>          current = guests.get(index);<br/><strong>          if</strong>(current.getSchool().equals(school)) {<br/>              guests.remove(index);<br/>              index--;<br/>          }<br/>      }<br/>}<br/><strong><br/>public void</strong> removeSchool(String school) {<br/>  Iterator&lt;Visitor&gt; itr = guests.iterator();<br/>  Visitor current;<br/><strong>  while</strong>(itr.hasNext()){<br/>    Visitor current = itr.next();<br/><strong>    if</strong>(current.getSchool().equals(school)) {<br/>      itr.remove();<br/>    }<br/>  }<br/>}</pre>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not many candidates related this term to a location in the memory.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A binary tree may be ideal when searching for items, but when extensive editing is required then the ability of a linked list to easily manipulate its pointers makes this structure the preferred option.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Reasonably well answered.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>When answering questions regarding a library class then the use of the correct methods are expected. Not many candidates were familiar with the use of the size method.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a quite straightforward trace of a recursive algorithm which most candidates completed well.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates alluded to the excessive use of memory if recursion was used.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates made a reasonable attempt but too many tried to use generic methods instead of ones specifically pertaining to this class.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19M.2.HL.TZ0.17",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A group of art students have taken three examinations in the school year. Each of the three examinations has a maximum mark of 100. Each student’s total mark for the year is the sum of the marks from the three examinations.</p>\n<p>The students have passed unless their marks meet one or both of the following failing conditions:</p>\n<ul>\n<li>Scoring less than 30 marks on <strong>any</strong> one of the three examinations.</li>\n<li>Scoring a total mark that is less than 150.</li>\n</ul>\n<p>Consider the following relation created by the teacher of this group of students.<br/><br/></p>\n<p><strong>CLASS_TABLE</strong></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the relation CLASS_TABLE distinguish between data and information.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>entity</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the entity for this example relation.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify an appropriate data type for Student_ID.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the role of data validation and data verification.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how Total could be validated.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the steps in a query that will output the names of all students who earned the maximum mark on Exam_Two.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">f.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the steps in a query that will output the Student_IDs of all candidates who passed.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating what data is</em>.<br/><em>Award <strong>[1]</strong> for stating what information is</em>.<br/><em>Award <strong>[1]</strong> for stating the difference using an example</em>.</p>\n<p><em><strong>Example answer 1</strong></em><br/>Data is a raw fact, for example, “Joe Skrin” is data/a string;<br/>Whilst information is data which has meaning;<br/>In this table “Joe Skrin” is the name of an art student/who had an examination/ who passed an examination;</p>\n<p><strong>Example answer 2</strong><br/>For example, number 99 is data;<br/>In this database, total 99 is information;<br/>Because it has meaning, it represents a student’s mark/it shows that the student failed;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>An entity is some unit of data that can be classified and has stated relationships to other data units;<br/>A real-world object with attributes that is represented as data in a database;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In relation to given example, the entity is a single student about whom data can be stored as a record in this relation;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>String/varchar/char/alphanumeric;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>. Award <strong>[1]</strong> for stating the purpose of data verification and <strong>[1]</strong> for further explanation, up to <strong>[2 max]</strong>. Award <strong>[1]</strong> for stating the purpose of data verification and <strong>[1]</strong> for further explanation, up to <strong>[2 max]</strong></em>.</p>\n<p>Data verification is a process that ensures the accuracy of data;<br/>(Accurate data is important because strategies devised based on incorrect data lead to inconsistent decision making;)<br/>Data verification is conducted using proofreading/double entry checks/new data cleansing software and technologies have been developed to automate the data verification process;</p>\n<p>Data validation ensures the data is logical and reasonable;<br/>Data validation is a computer-generated process using codes to validate a range of data;</p>\n<p>Data verification and data validation (applied in combination) provide quality assurance/make sure that processes and strategies are not driven in the wrong direction;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.</p>\n<p><em><strong>Example answer 1</strong></em> (<em>assuming the value of the Total is calculated</em>)<br/>The calculated value for the total mark must be an integer;<br/>In the range from 0 to 300;<br/>(If it is not, an error message could be output;)</p>\n<p><em><strong>Example answer 2</strong></em> (<em>assuming the value of the Total is entered</em>)<br/>The entered value for the total mark must be an integer;<br/>In the range from 0 to 300;<br/>The sum of marks on the three exams can be calculated and should be equal to the entered value for Total;<br/>(If it is not, an error message could be output;) </p>\n<p><em><strong>Example answer 3</strong></em><br/>Total is a validated field;<br/>Therefore, if all the individual test fields are validated, the Total value will automatically be within the specified range;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for selecting the correct field from the table and <strong>[1]</strong> for a correct comparison, up to <strong>[2 max]</strong></em>.</p>\n<pre>SELECT Student_Name FROM Class_Table<br/>WHERE Exam_Two == 100</pre>\n<p><em><strong>Note</strong>: Accept logically equivalent answers</em>.</p>\n<div class=\"question_part_label\">f.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for selecting the correct field from the table.</em><br/><em>Award <strong>[1]</strong> for each of the correct comparisons, up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for using logical operators correctly.</em></p>\n<p><strong><em>Example answer 1</em></strong></p>\n<pre>SELECT Student_ID FROM Class_Table<br/>WHERE Exam_One &gt;= 30 and Exam_Two &gt;= 30 and<br/>  Exam_Three &gt;= 30 and Total &gt;= 150</pre>\n<p><strong><em>Example answer 2</em></strong></p>\n<pre>SELECT Student_ID FROM Class_Table<br/>WHERE not (Exam_One &lt; 30 or Exam_Two &lt; 30 or<br/>  Exam_Three &lt; 30 or Total &lt; 150)</pre>\n<p><strong><em>Note</em></strong>:<em> Accept logically equivalent answers.</em></p>\n<div class=\"question_part_label\">f.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.ii.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.2",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-1-basic-concepts",
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Calculate the denary (base 10) equivalent of the hexadecimal number BF.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>11 × 16 + 15;<br/>191;</p>\n<p><em>Allow solution via binary route 1 mark for working, 1 mark for answer</em>.<br/><em>Allow both marks if correct answer given</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were required to convert a hexadecimal number to its denary equivalent. A range of different correct methods for doing this was seen, with candidates receiving the appropriate credit. However, some candidates found this question difficult and were unable to provide a correct answer or demonstrate a method of solution.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.5",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>RunAndBeyond</em> is a sports knowledge website, offering expert knowledge and technical tips on sport disciplines. Official teams and associations are invited to include some of their own multimedia digital resources to <em>RunAndBeyond</em>.</p>\n<p>A team of triathletes, finding their sport discipline under-represented in the website, wants to contribute content for <em>RunAndBeyond</em>. The team intends to use a wiki in collaborating online with other similar interested groups in triathlon when creating and updating their own resource.</p>\n</div><div class=\"specification\">\n<p>The triathlete’s online resource provides a variety of references through external links, and one of them points to:</p>\n<p style=\"text-align: center;\">ftp://files.tri-events.cc/site/index.php/en/general-info</p>\n</div><div class=\"specification\">\n<p>There are also external links on the website to videos and some of these are transferred using lossy compression. When these videos are transferred, they can either be streamed or downloaded for future viewing.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a wiki can support the ongoing collaboration in producing the triathletes’ resource.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the reason why the above link is a URL.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the processing that takes place when the line of code above is executed.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest why lossy compression should be the compression technique used.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The <em>RunAndBeyond</em> website continually automatically updates its content regarding a live sporting event.</p>\n<p>Suggest how a dynamic web page would function in providing this service to the user.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.</p>\n<p><em><strong>Note</strong>: An expansion from <strong>the technical/computational perspective only</strong> (not sociological one) is expected about aspects of the chosen method of online interaction</em>.</p>\n<p><span style=\"text-decoration:underline;\">Aspects that can gain credit</span>:<br/>Editing, permissions layer, ownership, tracking of updates, security (open to vandalism/non-monitored contributors), open source (interoperability).</p>\n<p>Software for wikis provides <span style=\"text-decoration:underline;\">collaborative</span> editing sharing of information of content, directly from the browser;<br/><span style=\"text-decoration:underline;\">Updates are usually tracked</span> in wikis (also to be removed afterwards) as they come from different authors / usually non-moderated / added content can also be removed later on (also by others);<br/>The evolution of a wiki is more immediate and spontaneous because updates are immediately visible;<br/>The wiki can easily be vandalized with incorrect/unauthoritative information, but it can also be quickly amended/corrected if this is realized quickly;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1 max]</strong></em>.<br/>It specifies how to access the resource/show the path/contains domain name, with the ftp protocol;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>:<br/>It sends a GET/PHP request to the server tri-events.cc;<br/>To execute en/general-info (index.php);<br/>And the result of this execution is made available to the client /browser (with ftp);<br/>(It specifies to use the ftp protocol to send the request and to return the resource;)</p>\n<p><em><strong>Note:</strong> Do not accept DNS</em>.</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Lossy compression results in files of smaller size;<br/>It may be needed for video <span style=\"text-decoration:underline;\">streaming</span> because the server provides execution through scripting;<br/>And guaranteeing streaming without interruptions is a priority;<br/>It may be a choice for <span style=\"text-decoration:underline;\">downloading</span> video for local execution to manage outgoing traffic on popular servers/increases the speed (using smaller files);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.</p>\n<p>Award <strong>[1 max]</strong> for identifying a technology that is needed award up to <strong>[3 max]</strong> for an extension describing how the technology is used and interacts with other components.</p>\n<p><em><strong>Example 1</strong></em>:<br/>An interactive web page that has an <span style=\"text-decoration:underline;\">auto-refresh/automatic update extension</span>;<br/>So that every N seconds a script is executed to reload the page;<br/>The web page is linked to an underlined database/data source<br/><strong>OR</strong> interrogates specific databases/news databanks;<br/>That are distributed and collectively provide regular updates;<br/>And they can be displayed back using the script;</p>\n<p><em><strong>Example 2</strong></em>:<br/>A dynamic web page equipped with a <span style=\"text-decoration:underline;\">RSS feed</span>;<br/>That collects/aggregates news from sources that cover that live event;<br/>The feed is in form of an update in XML;<br/>Therefore, it can be used to feed an underlining (distributed) database or the web page/uses syndication/can be immediately interpreted by the browser;<br/>And it can then be processed by some script to be displayed back in the original web page;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.8",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> reasons why fibre optic cable would be preferred over wireless connectivity.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Fibre optics allow faster transmission speeds;<br/>Fibre optic cables are more secure/harder to break into;<br/>Fibre optic cable transmission is more reliable/less likely to suffer interference;<br/>Fibre optics allow transmission over longer distance;<br/>Fibre optics allow greater bandwidth;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>This question was answered very well with the vast majority of candidates able to give two reasons why fibre optic cables would be preferred over a wireless connection.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.6",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Artificial neural networks (ANNs) are being used in a variety of applications, including pattern recognition, game playing, classification and data mining.</p>\n</div><div class=\"specification\">\n<p>An ANN is being trained to recognize handwritten numbers by identifying them as a digit from 0 to 9.</p>\n</div><div class=\"specification\">\n<p>Assume that each digit to be identified is input as an image made up of 30 × 30 pixels.</p>\n</div><div class=\"specification\">\n<p>A simplified version of an ANN is shown below.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> reason why ANNs are suitable for solving the types of problems found in these areas.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe an appropriate set of data that could be used to train the network.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the number of neurons that would be in the input layer.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the number of neurons that would be in the output layer.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the way in which the output from neuron D will be determined.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The network is set up with initial values. The outputs are compared with the desired outputs.</p>\n<p>Identify the steps that now take place to train the network.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The functions/mathematical relationships in these types of problems;<br/>Which are required for use with traditional algorithms/non-ANN algorithms;<br/>And are difficult/impossible to define;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Many sets of digits;<br/>Using different pens/colours/thicknesses etc.;<br/>Written by different people;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>900;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>10;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>The product of;<br/>The weights and the input values;<br/>Will be summed for neurons A, B and C;<br/>From this will be subtracted a bias/constant / This will be compared to a bias/constant/ A function will be applied;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[5 max]</strong></em>.<br/>The difference between the actual and desired outputs;<br/>Which is the value of the cost function;<br/>Leads to adjustments in the weights;<br/>Of the neurons in the hidden layer(s);<br/>The process is repeated;<br/>Until the cost function reaches an acceptable value;<br/>Or a certain number of iterations are made;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "17N.2.HL.TZ0.8",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Distinguish between a <em>variable</em> and a <em>constant</em>.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>The value of a variable can change while value of a constant does not change; <br/>During program execution/ during run-time / while stored in the memory;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates generally achieved at least one mark for this question by recognising that a variable’s value would change but that the value of a constant would not. Those who were able to expand that point and say where this would happen, for example, during the execution of a program, also achieved the second mark.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.7",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Define the term <em>peripheral</em>.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>A piece of hardware / a hardware device that is <span style=\"text-decoration:underline;\">externally</span> connected or attached / <span style=\"text-decoration:underline;\">remotely</span> connected or attached (to the computer system);</p>\n<p><em>Example answer</em><br/>A peripheral is an external (computer) device that is connected to a computer, such as a keyboard;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were generally able to name a peripheral device, but were less able to provide a complete definition, often failing to mention that it is ‘externally’ connected, or similar.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.1",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>List the output from the given algorithm for the following input. </p>\n<p>2, 6, 8, 9, 12, 15, 18, 20</p>\n<pre>loop for Count from 0 to 7<br/>    input NUMBER<br/>    if NUMBER div 2 = NUMBER / 2 then<br/>        if NUMBER div 3 = NUMBER / 3 then<br/>        output NUMBER<br/>        end if<br/>    end if<br/>end loop</pre>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em><br/>6;<br/>12;<br/>18;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were generally able to dry run the given algorithm and give the values of the various calculations, but unfortunately, not everyone identified the values that would actually be output, namely those divisible by both 2 and 3, but instead listed all the numbers from the input. The full range of marks was seen for this question.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.8",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Define the term <em>data packet</em>.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max]</strong></em><br/>A unit of data made into a single unit (that travels along a given network path);<br/>A data packet travels through a network as a unit i.e. with all parts kept together;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates generally understood what is meant by data packet and gave a sufficient response to achieve the mark.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.9",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> features of a graphical user interface (GUI).</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Menus;<br/>Dialogue boxes;<br/>Windows;<br/>Icons;<br/>Pointers;<br/>Buttons;</p>\n<p><em>Note to examiners: allow other correct user interface features</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates offered a wide range of responses giving many correct features of a graphical user interface, including, for example, menus, toolbars, pointers and icons.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.2",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following example relation. It holds data about a number of teachers and students from different schools who volunteer to support the local community on particular days.</p>\n<p><strong>SCHOOL_VOLUNTEERS_TABLE</strong> (School_Name, <span style=\"text-decoration: underline;\">Code</span>, Address, <span style=\"text-decoration: underline;\">Date</span>, Num_Volunteers)</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>The key attributes are underlined.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State what is meant by redundant data in databases.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> issue that can be caused by redundant data in a database.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> characteristics of the 1st Normal Form (1NF) which are evident in this relation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why a compound key is used for the SCHOOL_VOLUNTEERS_TABLE relation.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The following shows the normalized SCHOOL_VOLUNTEERS_TABLE relation:</p>\n<p>SCHOOLS_TABLE<br/><span style=\"text-decoration:underline;\">Code</span>, School_Name, Address</p>\n<p>VOLUNTEERS_TABLE<br/><span style=\"text-decoration:underline;\">Code</span>, <span style=\"text-decoration:underline;\">Date</span>, Num_Volunteers</p>\n<p>Discuss whether these relations are in third normal form (3NF).</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Redundant data means data that is held in two different places within a database;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying an issue caused by data redundancy and <strong>[1]</strong> for a brief explanation up to <strong>[2 max]</strong></em>.</p>\n<p>It could give the system unwanted/unexpected results;<br/>due to the use of inaccurate data;</p>\n<p>It may lead to additional storage requirements;<br/>As data is used more times than necessary;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Each attribute has a single value/is atomic;<br/>All values for a given attribute are of the same data type;<br/>Each attribute is unique;<br/>This is a unique key;<br/>There are no repeating fields;<br/>There are no two identical tuples in this relation;<br/>Order of attributes/tuples is not significant for the relation;<br/>Key (Date + Code) is unique for each tuple;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying why a compound key is used for the <code>SCHOOL_VOLUNTEERS</code> relation and <strong>[1]</strong> for a brief explanation up to <strong>[2 max]</strong></em>.</p>\n<p>The alternative is to use an autonumber field;<br/>But this would use additional storage space;</p>\n<p>Are used because it is not possible to designate a primary key from a single field;<br/>Neither the code nor the date field on their own uniquely identify a record;<br/>Is based on two primary keys in other tables;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[5 max]</strong></em>.</p>\n<p><em><strong>Example answer 1</strong></em><br/>2NF = 3NF if there are no transitive relationships/if any non-key attributes are more dependent on another non-key attribute than the key field;<br/><code>Schools_Table</code> could be the above if a school had more than 1 address;<br/>Then the <code>Address</code> would depend upon the <code>School_Name</code>;<br/>And the <code>Schools_Table</code> code would be split as follows:<br/>(<code>Code, School_Name</code>)<br/>(<code>School_Name, Address</code>);<br/>If the school had only 1 address then 2NF = 3NF;<br/>The <code>Volunteers_Table</code> has no transitive dependencies;<br/>There is no redundant data;</p>\n<p><em><strong>Example answer 2</strong></em><br/>A relation is in 3NF if it is in 2NF and it contains no transitive dependencies;</p>\n<p>Assuming that schools name is not unchangeable;<br/><em><strong>OR</strong></em> there are two schools with different names and same addresses;<br/><em><strong>OR</strong></em> two schools with same name and different addresses;<br/>Then the school name cannot be treated as a key;</p>\n<p>From the 2NF (two created relations above) the functional dependencies are not evident in the relation <code><strong>School_Table</strong> (School_Name, Code, Address)</code>:</p>\n<p>The relation given above (in 2NF) is also in 3NF;<br/><code><strong>School_Table</strong> (School_Name, Code, Address)</code><br/><code><strong>Volunteers_Table</strong> (Code, Date, Num_Volunteers)</code></p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.3",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>database transaction</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the importance of durability in a database transaction.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> different types of relationships within databases.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the nature of the data dictionary.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify four responsibilities of a database administrator.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follow up to <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for: </em><em><br/>a unit of work/logical action;</em><br/><em>performed on a database;</em><br/><em>independent of other transactions; </em><br/>changes state of the database;</p>\n<p><em><strong>Example answer 1</strong></em><br/>A database transaction is a logical unit that is independently executed;<br/>For data retrieval or updates;</p>\n<p><em><strong>Example answer 2</strong></em><br/>A database transaction is a unit of work;<br/>That is either executed in full or not executed at all;</p>\n<p><em><strong>Example answer 3</strong></em><br/>A database transaction is a way of representing a state change;<br/>And has four properties, known as ACID;</p>\n<p><em><strong>Example answer 4</strong></em><br/>A database transaction usually means a sequence of steps, treated as a unit;<br/>For the purposes of satisfying a client’s request;</p>\n<p><em><strong>Example answer 5</strong></em><br/>A database transaction is a process carried out on a database;<br/>Which may change its state, for example: moving money between bank accounts;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em></p>\n<p><em><strong>Example answer 1</strong></em><br/>Durability is important because transaction data changes must be available;<br/>Even in the event of database failure;</p>\n<p><em><strong>Example answer 2</strong></em><br/>Durability means that if the system says the transaction has been committed;<br/>The client does not need to worry about it because transactions that have been committed will survive permanently;</p>\n<p><em><strong>Example answer 3</strong></em><br/>Durability in databases is an important property because it ensures transactions are saved permanently;<br/>And do not accidentally disappear or get erased;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>. </em><br/>One to one;<br/>One to many;<br/>Many to one;<br/>Many to many;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating what a data dictionary is and up to <strong>[3 max]</strong> for describing its contents</em>.</p>\n<p>It is a set of tables/a database that provides information/meta-data about the database;</p>\n<p><em>A data dictionary contains</em><br/>The definitions of records/entities in the database;<br/>How much space has been allocated for and is currently used;<br/>Default values for columns/attributes;<br/>Integrity constraint information;<br/>Names of (database) users, their privileges and roles;<br/>Auditing information such as who has accessed or updated data records;<br/>etc. (any other general database information);</p>\n<p>They do <span style=\"text-decoration:underline;\">not</span> contain any actual data from the database, it contains only information for managing it (without a data dictionary a DBMS cannot access data from the database);<br/>Data dictionaries are usually hidden from users to prevent them from (accidentally) deleting/changing/destroying its contents;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>To direct and perform all activities related to a database system;<br/>Installing and configuring software;<br/>Creating new databases;<br/>Designing the database schema and creating any necessary database objects;<br/>Ensure database security is implemented to safeguard the data;<br/>Back up and recover the database;<br/>Work closely with application developers and system administrators to ensure all database needs are being met;<br/>Apply patches or upgrades to the database as needed;<br/>Training employees who use the database;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.1",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-1-basic-concepts",
      "a-2-the-relational-database-model",
      "a-3-further-aspects-of-database-management"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>one</strong> common feature found in the user interface of application software to improve its usability.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max]</strong></em><br/>Toolbars;<br/>Menus;<br/>Dialogue boxes;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates seemed to misunderstand what was required as a feature of the user interface for application software, which would be, for example, a menu or a toolbar, and gave answers such as colour, or answers related to accessibility, such as large fonts.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.10",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline <strong>one</strong> method of collecting information from stakeholders concerning the requirements for a new system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Surveys;<br/>(General) questions distributed to many stakeholders as a written or online document;</p>\n<p>Interviews;<br/>(Specific) questions asked of nominated stakeholders in an individual setting;</p>\n<p>Direct observations;<br/>Observer watches stakeholders performing their current tasks;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates named one of the recognised methods of collecting information at the start of the system design phase, notably questionnaires, interviews or observation; but not all correct answers were given an appropriate expansion for the second mark.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.3",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct the truth table for the following logic circuit:</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em><br/>1 mark: 1–2 correct rows;<br/>2 marks: 3–4 correct rows;<br/>3 marks: 5–6 correct rows;<br/>4 marks: 7–8 correct rows;</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were able to construct correct truth tables and populate them sufficiently well to achieve some or all of the marks. Some candidates populated the inputs in a random fashion, rather than the standard method. This sometimes led to repeated lines or missing lines, causing the candidate to lose marks.</p>\n</div>",
    "question_id": "21M.1.SL.TZ0.11",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A shop sells only fruits and vegetables. Data about the products sold is held in the relation Greengrocer as follows:</p>\n<p style=\"text-align: center;\"><strong>GREENGROCER</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: left;\">Prod_ID is the primary key.</p>\n<p style=\"text-align: left;\">The relation above would be represented using the following notation:<strong><br/></strong></p>\n<p style=\"text-align: left;\"><strong>Greengrocer (Prod_ID, Prod_Name, Prod_Price, Supp_Name, Supp_Contact, Supp_Phone)</strong></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify an appropriate data type for Prod_Price.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the steps in a query to output the names of all products supplied by <em>Veggy Co</em>. with prices in the range from 4.00 to 10.00 inclusive.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the redundant data in this relation.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the problems that may arise for the following function performed on the given relation. In your answer, you should give an appropriate example: Inserting a new tuple with an item that is supplied by existing supplier.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the problems that may arise for the following function performed on the given relation. In your answer, you should give an appropriate example: Deleting a tuple from the relation.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the problems that may arise for the following function performed on the given relation. In your answer, you should give an appropriate example: Modifying a specific attribute value in the tuple.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the database in the 3rd normal form (3NF) using the notation</p>\n<p><strong>Greengrocer (Prod_ID, Prod_Name, Prod_Price, Supp_Name, Supp_Contact, Supp_Phone)You must show all your workings.</strong></p>\n<p>You must show all your workings.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1 max]</strong></em>.<br/>Real number/float;<br/><em>Accept currency</em>.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for selecting correct field from the relation.</em><br/><em>Award <strong>[1]</strong> for correct supplier name comparison.</em><br/><em>Award <strong>[1]</strong> for correct checking the price range.</em><br/><em>Award <strong>[1]</strong> for correct logical operations.</em></p>\n<p><em>Example answer 1:</em></p>\n<pre>SELECT Prod_Name FROM Greengrocer<br/>WHERE Supp_Name == 'Veggy Co.' AND Prod_Price &gt;= 4.00 AND<br/>Prod_Price &lt;= 10.00</pre>\n<p><em>Example answer 2:</em></p>\n<pre>SELECT Prod_Name FROM Greengrocer<br/>WHERE NOT (Prod_Price &lt;4.00 OR Prod_Price &gt;10) AND<br/>Supp_Name == 'Veggy Co.'</pre>\n<p><em>Example answer 3:</em></p>\n<p><code>SELECT Prod_Name FROM Greengrocer</code><br/><code>WHERE NOT (Prod_Price &lt;4 OR Prod_Price &gt;10 OR Supp_Name !=</code><code></code><br/>'Veggy Co.' )</p>\n<p><em>Accept logically equivalent answers.</em></p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>. <br/>Redundant data unnecessarily repeats in a relation;<br/>Supplier data (name, phone, contact) are repeated;<br/>For every item that is supplied by the same supplier;</p>\n<p><strong>For example</strong>,<br/>Items P118, P122 and P220 have the same supplier (Veggy Co)<br/>Items P219, P111, P121 and P211 have the same supplier (Fruit and Veggie)</p>\n<p><em><strong>Note</strong>: Accept any correct example from the table given, such is the following</em>:</p>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArYAAAB3CAYAAAD2KAPUAAAgAElEQVR4nO29f3xU1Zn4/56BUhfDj1LZcgf9wsukgF3SWpPFVdk6EE20SOWbFN1amdDku4V+kFJZkjSIXX8VC8knLCpV6ycxBMquymRRZFuCCWMXa2EnVBu2kJTwAoW5uPhB8kMUZOZ8/7h3Zu5MZpIJJCEZn/frNa8XTM6959znec65zznPc87YlFIKQRAEQRAEQRji2C93AwRBEARBEAShLxDHVhAEQRAEQUgKxLEVBEEQBEEQkgJxbAVBEARBEISkIKZja7PZBrodQxaR1dBA9DS4EH0I/YXYlgBiB8lOd/q1xToVQQxCEARBEARBGKzEO9RreG8vECKx2WwiqyGA6GlwIfoQ+guxLQHEDpKd7hZgJcdWEARBEARBSArEsRUEQRAEQRCSAnFsBUEQBEEQhKRAHFtBEARBEAQhKRDHVhAEQRAEQUgKxLEVBEEQBEEQkgJxbAVBEARBEISkQBxbQRAEQRAEISkQx1YQBEEQBEFICsSxFQRBEARBEJKC3ju27Q2UOGzYbDE+jnzKaxvRA/3QUgACtDesxGG7h6qWT3tx2SGqchzYcqpoCVjvE+s50skvf4mGlvY+bnNsmTnyy6lt1Ok3kfEhDSWZlmfvTZsdzCrfR2fcv/dSD4OdoWjbfdDmQEsVObZMSho+vLRHGOwEdBpry8m3ymtWCVUNLTFsfBCj15Jvs2GL2T/fpzY/DZvNRlp5IxcuUxOHCgG9kdryfMv47GBWSVUfjv8DTGcLDVsjbdyRX87WgbBxs+7F6/rL7tppaXiJ8sUbaOzjCpLFDi40lpOWqK8RGkcWU6sn10hx0Su2WnE9bUqhQh8/HQ2zaXpgLvdVxHKGBiMZFNefsjyDQnW4uZ//4H7ng1Qd6kujjlGXaqPh1oM8kPmPVDSe6cO6+godT9FjPDco29Z/DEXbvpQ226cUsFN5WTP7qgFr74ATOEHDqoVk5hVRo1u+96ylMMvJ3JgTuMHO57N/9hUBfRer7ptLXlENYZPQ8awtJGvqfZQPMbkG9F2snOska36kjes1RczPcjJ35a5+nJh30vjcIrLmF/GGv39quND4K76d9Q8UvfFJn9432ewgHnpNEXmD1tfoW/owFcFOyrR7eeiJmXiKynl5qK7kpUzh9uWP8cyd+yj8USWNnf23lgqjmbawiCey91O0srYXK6oDRQZO5yGKVrzYz3IY7AxF2x6Kbe4/Aod3subJOmA6rsomOpRC+Y9TX5qN4SAOVRntGKRjx2DnUw7/5gWe9OigFVB5sA2lFH5fHaVOjSEn18Ahqhfm86RHR3M9w17fOXOhppm6MhcaOp4nS/kXT5JHZXpNktlBEJcbn2Whw+/bQ4VrOrCDipf3M7TWoXtPP+XYHqH5eKcZJnUwq+QpyvPTjeXwkgZDqJ0tNFSVMKu7kGBU6NCRX8Fv9p/onyZbsU9iXsmDZHt+zcv7Tvd/fQBNhzneecEMR+dQ8sIvzOcOhogDdLY0UFWS022oJDKkkk5++WvsP3nuIht1Lfet/GcKmstY8Zy3hxWt8+iNtSE9d9VpMNT+/1Jeay2XQ8nGfejtLeyytnvdW1GrC+fRGzdRMssRvq6qgZYBd7iHom133+aPYqQiBPR9bAzZWjr55TsjZR3QadzYwzMOIgIdp2kF4CrSvzGZFAD7RJwL7yUbCMkI4oboQmG+/FpzZScY9k8jv2qnxX4dzCrZRKN+3rwy0XIXSd061mw71kM6Uzstu9Z1CVGHQ5OdNJbPMlMYdvHnUFkHs0pqaem8QGfLznC/nVXCxogUqsTGp8HDBTpOnzL+OfI6vpE2GgC79vcsXDDT+L7pMMc7A8AF9NrFxnOFdA9hmaWRX/t+VLkXaLDKO1peIRsrpKrh9a7jYa+GtQDtnhpW1engrGD7hh8xQxth/CllCrcvL+WJglIqtz3PT5yWqEyXtIUYY6qlL2xt/EPkuyU0Rr9Pbf71ZBZ5AGgtyuQLtlmUN3aG6tkVEeZPJ7+8Nsruu74/HPnr2NXSHpLrFzKLjD7cWkTmF/oq1aY/7IDE9dundhAfu3Yj/3D3LUaVJ8/wcXSBiPE8Vt0BOls8bLXqscuYbxnntv4h6v22gX0R+u7n8ULFIM7XBm31qlhDacX1qq3LHz9RzZXzFVqpqm/zh8qiFajKg21K+T9QzYfblOpoUpWu6UpzPaP2+s4ppc4p395nlEvTlLNsr+pQSin1kfKWzVE4S5W7uU0p5VcdzW5V7NQUzFeVzZ/Eb2M0/oOqMltTZFeqZr9SSvlVW32p0shQxfWnunlOTWVXHlT+bm7draxUAnWZbTPkGSyL0lzV6mCHX/l9h9Xhjs9Ux8Fq5dKmK1fFHuXzK6X8PrW3wqU05qgy70fGvTr2qjJnqnIWu1Vzh18p1aaa3aXKCZZnT4RgO+aryuY2ddy9JLKeiL9/opTyqw5vhXJqLlWx12fK65zy1T+inKHrws8W1mmwDIrQtf7Qsxa4j4buddy9RGnW+wdtqMCtjifwXD3rSQ1N2+6DNvubK1W2xT79x92qQNPCdhQta/9R5S6wPmMsnXVPQvroQ4xnRAEKZ7GqdO82+0gMfG7lAgWLlNv3Wejrz7xlKhUULrfyKaWUek+5XanGPWN9nBXK2+HvRbkECbVvurrPNc/oU9oS5T5+LqKu1DKvMlpv9p9YdYeu61DeMmecNmrKuXy5cmnR15p2pYI2E+v+pq0NIInZltk3gs9X/Cvlrm82+2c0nymfe5FRNqR7pcIyS1Uu93uR5WJ+LGNoSIdx5B0aKxIhrLuwznu6xOjTPdpjD+003o+x7NupyrwdobEi1vXhsdt8f8S1z49jyrWnZ718dtCz3EL67VM7iDVGGfh9e1SFqe+Q3EJ1O5XLFd33rb5PcHzvqY09jHMWH6Qvxovu9NuHjm2MF3jMsqaDExpQo74Pvtjb6lWxFu0MRjtUCXLRjm08hyHMJTm2XZzTeGVPqfrijBhOnPG98VxBuYZfNl3L9NDULm025RwcnEIDXrQezPZFy8p/UFVmp5odJM6zxZpARDj7ccqEvu9GhxYuzbEdxLZ9yW1WUY5tlDPcpW0fx2lnPPuLzUA7tkq1qYOVBV2dO82lyl7Zobw+i74uxrF1PqLqIyYyKEIyTbRcglheSGX1darMqVkchRiObXD8szhW4ReL6YBYHdvQy8WcgJlOtKuySXVEOCHBa80xJlQmUt49jaF9TcK2Fce501xl6pVtXmMBQSl1cY5ttiqtP66MNYiwUxGSRUiHmnKW1kUtVhDV/3oiqHOLY9UtFmfO4kj4fXWq1KnFcbwsE12rsxqSR2znOjShDL07LJOs1DLl/UxZ7NMqi+OqvjQ7Qmah/he8rgcunx2oxPXbp3ZgkVG8j/XdFFO3YbmHnjGkn/CCW8SiVGhcsY5z4cWr8KQ6OJ72zXjRnX4vOhVBX5vFmIhdd1/EUfIetz67nS3LZxihvpicxruzDj39BqYHwyUA2Em5Oo10jtB8vJ127xts0q9l6tUpMcoMRRpZmzU+cqfisAxKTs7kWe8LLM8YG//S9j+xc5OP9Fu+hhahsRSunnqtGSr50JRrGlen2LuWuRTsk5j32KPdpCRcxew1XnxrMjnZ8Cq1tbXU1r5ASdZsCuvOXlrdBExbcHD95Ksic2dSHExN97Fp55/6NGdoKNr2xbc5isBR9ry0ByLsyM7o2avxqZcpmHLWeEYtjckTYjyjXsdO7wCl7/SK0UwreJZG73Yqi7PDX+s1FM2fQ2bGjy5hs2gqrqUFzNZGACPQZtxL/oIMoBH3/mOWcGmi5XrBmBtZXF6EE9CrqvjXP8aQvX0aBTt9KP8LzDreQG3tC5Te/wBVOkAHp9oic4u1Bd/nu9NGA2P5xiwnqQDaXPK/+zVSsJPyjW8xJ9VywYVj7Hc3AgeoKUxnlM2GzTaG6wqr0AF90xt42wdhkmLKdAperMO77VcUO7XQ13pNEfPnZZLxg40cuthUJ9cils6eiB2wazdRmD8XDdDd+/lLhKK/w9Kls4xx3a4xozCfBRqgv83+v/R27DzLyTMf93zCTrCPo5H9RBELpwXD71n89OGFRj7uc7+jOaKdM1lQ+G2mpNjBPpGb5tySUIuMjakK/5ZvcbxuG7VVD3N/3gYjjH/2NG1nA/DBn3mzTidSFhOZvXonSil8a2YzupeS6BX9aQcJ67cv7SAWGs7iSuo9q8mdOCLqb1bdamTeloFm+Wvg8O95qU4H5vPEQ/cyLcUOjECbvYSHizOAHTy3u9UyfmlkL3Axb8poYAQTb5rN7dbqBmC86MNTERRq9xoK7s6Icr6iCHzI0Xd83RTw8c7RE/iOHkbvptTAoJE+1ZG4U9AtsU5F8LF7zT9yd4bWrSICJ4/yTnfC0A9z1HeiB7leGvaJd/HYM7k0x9yFHaDz0EbyHWOYmvVL9p4JAH9NzrO1VGZDU7PP4gxHO3SJEmticB2FdX1vJUPRti+6zReL/iRZY4ZF6GPY1ELq+qGqvmMEWsZdFKzZaZwa0bwbd2UxTgC9ilUveruZIF3g1LHDZp5uNNeQPulLlv9fwZjxoy6hXG+wk5LxA8rL5gA7KFqxllffi34RtnOoqhDHMAeZ8/LIy/shaz1BCxzF+DFXRJQeOX4MI6OrGTmOMSPjGNKpYzTFFoyBfpozHw9CxxbArpFx9z+yZrcPpdporneHJj56zdO82O0ei4841vR+zL+kpk9ifLgSRo4Z11WmAKlpTBo/PPz/kWMYH7Ngd3yJSenXADqtpxNxbD/mdKsOTOX2b1xtefdY2tl6mGOnrJ7tOMaOCrZzOOMnpWGd28Sl8wBV+ekMc2QyLy+PvMK1eIJ/M23qgu8IbyVyr/6kn+wgYf32iR1YiNo8ZvgaBcyeEmuKYNVtV1sN7U9IncE3rrWOFeHxq7XpGKfCjWfC2CvDdjV+EulWYxmA8WLgf6DBfhWTr3d0U8DB9ZMn4picFjFrGFi6WSW8DNgnTOb67oShpTHZMbEHuV4qI5g4r4hnCo5RtKKKvWcsZ7oEWnh5WSm77nRz3L+TNQXfJTf3bmY7ztLc1Fcu3Hwqmz+JmhiogZnRJ8qQsO0+IruSZn9XXahBeWyYeZazzWY5z9lOyhQnuQUPU17mBGKtpiVKM7vePW5xKD6l7VTHJZTrLWPJ+OFyijXAs4UaT2SfC7RsZVlhFTrZFFe+wjavD/9nXsoS8kwS4MqxTNAAnJR5O2LYxHPkasN7usvAEjr/2UFO1SFTJ6OZMjuXgp+vNmVz8Svprbve5UhI0QHOtp0m5rpb6z7ePWJZMT/bxqleL9CN5Ks33IQGtO74He92WV38kIaVP6F8q8fYGGa/knGpGl3t0dLOaEfroviUlpcfo7DmADiLqXTvwOs7x2fesgineLjjWoz139Oc6Rjg81T72Q4S1m+f2EH/YB81ztBXdBst41fkRK4HBmC8uAw+2zgyc7LRmvZzIHqX3PHDNHEtU68ezejM21igWXYqR5TpZwLv88avt6Nn/y8KnYPgJT366+QscND01p+jdip2crz5iBk2vsqUa3AHZ1SZviCUklDBI0/vsVTho7mJLqkSgY4zXPrhMnbTFg7y1oEPIlcjOvdRPivNMiBdboaAbfeEfTIz751p2QlsYPyIg4Ocqv/hm/GesXEdswblj3aYegGoW8fP1wd3cwfoPOTmqQpjDUnLu4GvDscy8L7FjrdPEAAC+m6efvq1OPfXqdtUw7aWduA8+r6X2LipEcgg74ZJDO91uYtg9Ex+/MySGBMmi11pX+XGnO9wd8aX+eB3r7Oju1WTXtX9dXIWZAAeKp5yG2HbwAkaVuZEnhYymAi1Wadu1ZOs3xc8saCdQzW/pKIVwnqxc+XYcYZsdzXw9onzwHn0hiqerokjxLqXqNx2iE4goL9N5cbt6FhsLMQeNlX+h+FwBnT2VW5kkw5oN3HDVxNdsrMzesY8ljs18Cxn7pJnwzvQAzr71v0T9z+5nqL597H05RYCwT6OTt2qMqrNFJyAXs8vHq9GR8O5+FtMveS5SPjdo117Iznzvk3GV07xO/euyMjHV77GrdmaIYvq/zT7phlliJiM9gP9bQcJ67cv7KB/sKfdzL3ZGvAKq37+kpmWcR69YQOPr20E5rB4Vmri49dAjBe9TcpNdFNVt2W77BwP7rqz7rAL7oQPJrcP0KkIHc2qrsxlqbd7upWV6qGuhMv6u56KEEq2tuy0De1WDyZ498WpCNFytiaDR24eC2+MibVhIs49E9k8FvNUhIPKXZyd8I7ynvWkhqZt90Gbu5yK4KtTpc7Urhs5grKOdSpCs1sVO1MT3sWbkD76km52ZxO9GzdmWU3d7LzZsPsET0UIb/ZMtFyCWDePeS3Sjmp3cBNP7B3IGcrpTLVsXIu9+Sfmhp3PvKos1Vp/N7umB+2pCN3szA5tHgqOoz3JMMFTEWJu3In1Sfx0kYjnCW3+6vl5jBN04pSNeSpCTxspracLBG3j/8Y+jeNmp3Jq1o1R3ZyKYN2EZz3ZhL46FaE/7EAlrt8+toN4pyLEJGHddqefWKciRG1iDI0XwXr6ZrzoTr+XJ8qeMp2CDW423/oeJY4vYrMNY9SP/sytmz1sXxHc6DKCibmr8WyczpuzxxhlFnn526U/JruH2ydOjLzNUUt5Y9x8tjc+S8G0QRHgxjhsfwEbPOu59eRjOIbZsNmm8aPmW9jcvIUVwY1n9knkrnezMb2B2aOGYbNNY9He61haNq8P2xJMSZhu+e4qnD/5JdUzfk+W44vYbDau/unbXJP/S9zFGX1TZ+5qPJtv5WRJBsNsNmyj5vPq+EV4tywhI+VyJ4tYGDS2ffHYtdt5YssWFvrLDVsb9rc87r8/LGvTziKe0fkq45e+1LuNagOJfRK5L3TdIALTcZX9G/WedeH+bp/EvCcqqQ5tMsumuHIzLzw8N3aeJKm4Xnkbb7WZr8t0XGW/xbN+HhPtF1Pu4p9x3mOPUhC1bGufeBdPbK8JP7ezmGrvv7N56W1AYx9svgyOT/URG/M0VwV1VrkOMuwTc3mh0cu2YJ51EM1F2Su78WxYYG6UiS3DyvrneXjONbFv7vo1Xm+4vCGLWBt3FvGKd0/Y1jQXZXVu1udO6nU41a7dzurtHupfKcNltQFnMZXbvDS+mB96HlJmsKJL2WyKK+tp3r7sIsbUK0i7c5n5IwAAVwJ2Js57iO0he9dwFtfg3fp/WHp7KoQ2mtpJyVjCFq+bMlf4vaK5ynB7q3kiuAkv7U4er3CZUQmNa/DTF7GhfrWDhPXbd3bQ99hJyVjG9ubdvFLmCkeFnMVU1lvfa724Xz+PFzbT84380mYjxtdCDERWQwPR0+AiOfTxPrX5s8irAZd7Nxtz473cEi0n9AWXz7YuoNc+gCPveWPzzsbc+Ln0ei35jjxqWITb98zgy0FOAi7rGJOofsUOLpru9Hv5JwOCIAife4K/2mPr/hPxi0dCUhP6VaruPpZfuxKSE7GDXtMrx7bHQTeJP8mAsQGo52cdlJs9Pk8EDlGV4+jZLh0raRiM54MKgiAIwmVCUhEuEZHV0ED0NLgQfQj9hdiWAGIHyY6kIgiCIAiCIAhJjzi2giAIgiAIQlIgjq0gCIIgCIKQFIhjKwiCIAiCICQF4tgKgiAIgiAISYE4toIgCIIgCEJSII6tIAiCIAiCkBTEPcdWEARBEARBEAYj8c6xjfvDxHKwcWLIIdBDA9HT4EL0IfQXYlsCiB0kO90twEoqgiAIgiAIgpAUiGMrCIIgCIIgJAXi2AqCIAiCIAhJgTi2giAIgiAIQlIgjq0gCIIgCIKQFIhjKwiCIAiCICQF4tgKgiAIgiAISYE4toIgCIIgCEJSII6tIAiCIAiCkBSIYysIgiAIgiAkBf3s2H5IQ0kmNpstxied/PJaGvXz4eKdLTRUlTArosxOWjoDce4foLNxHbMcK2loj1cG6NxH+axbKGn4sE+f7rITOERVjgObLbPfny3QUkWO7R6qWj7t13o+F4T0Ft0nepBvQKdxo7V/5FBS9XpkH+I8euMmSmYF7+9gVskLvNqo000PGdRcaCwnLeYYYn7ya9H7pib02sVd79nZQsPWchava+RCn9RzCVxopDzNhs02i/LGzhgFLGNuThUtXZQeoL1hJY5E7K1feZ/a/DRstjTya9+/TG24FCy2klZO42U3DAshG4n1cTCrZFPUmDGQTTP78mCT2SVh7VORn7Ty2GNG4EQthQ4bNttianVriXZaGqos43cMPylmE3Qaa8vJd5h1zyqhqqGFyBEiQGfLTsrz00Ptc+SvY1dLe9TNot8hOZRs3IceNZYE9H1sLMkZFHYVzcCs2Gql1Lf5UUqFPx3/yq1N/0zmfRto7AxA4Bi1y/K4/83/hzW+cyil8Pue4/qmFTiXbeNEjAG689C/8fjKX+Ppru7OA2x8/En+3XOu/57vMhE4/Hteasql7Mlr2LTzT0SbpzBI6fTR3DSTyuZPIvuEepmCKVfEueg8J7b9nLnVsNDrw68Uft/PmPBmKXP/ZU9I94ETr7Nq7mZYuA2fX6H8jayZsIcfzX0aT3eTPyEOnTQ+t4is+UW84b/cbUmEcWTmZKMB1P2WPYejHdfTeHfWoQNa8WK+G9fehOREx7PWFX7vCn3AeU4ePZz45DpwjG0/+2equlzQzqGqB3FmFbLWE/zjAWqK8rrXV+AEDasWkplXRE3wMs9aCrPyWFZ7LLSgETixjWXOOyiqORC6VK9ZTrZzJbUngg5pgM7GDdyX6bK0oY61CwtZtS18Lzr3UXHfPBaurQveCc9aF3NXvR7DVxt4Ll8qQsp0Fj70INmeMla+3MKFw/U8X/VFFuTfywxthNE47RaWPfQg6VWrecpjWZEM6DRuXMWS1x3cc++1cSowZx1LGrjunm+T0v9PNMB8iKfylzQtyOX/y51L+trn2CqrqUOAAO3eN9hEGpMnjOjFZUfY+fxvSV/wAxZkaNix9I9Nb+BtDwCfcnjnv1GVfi+FC2agGYWYsayUJ9LfZKf3dD890wCRWob3MxU1GVCojbmGI3fJDEfLfa6P7znQ2BmdeRsLNIA9vLTnaORKffuf2LmpEchgQc7XGX1Z2ghwDbkbD6PUYTbmXnPZWpHcLMLt+yyir/iPuynQAM+veXnfEB8PBg2dHG8+AszvslhxeEUGwyPKBuj8Yy1PVx3oept2Ly+uqkJnOq7KJjoS1Ffg8E7WPFkHzKHM+xFKtXGwsgCNA1Q98Ly5oNHJH//1acOZdlbg7fCjOvZS5tRA30DxvzaZK8un2ffyr/Gg4SzbS4fy0+GtwBlxrwDt+7ZR4dG73EuP9tUuE4Mgx1anqdnH2SkF7FRe1sy+KkYZH+8c/dAcoD+lpXoFK5pn8Yvlf8eoOHcNtGxm4Yoj5PxiMZmjhvVb6y8b7X9i5yZYkPN1xqbdzL3Z+2O8xBoocTjIeWEXf1iXb4ZKokMGZugyZwMNf9gQEcrY2EP4umsoooqGiLBGdFglVpnPG+bsPj2Nq1N60f06fTQ3jeX6yVdFdFr7hMlcT53ptBoDrHb9ZCZEFLqKydefS/5Vfb2WfJsNW9qj1O581EzZuIeqlpbYYe9QyDYYDoxORXif2vzrySwyYkKtRZl8IW4KAMQK9dkc+ZTXNobDeME22haztfEP1JYH+2U6+eveigr3tdOya12oTxphwwSckdFfJ2dBBqBT99LvORy6pzmp0gEtm5zMcZZ2N1DVbV8GOlvYFWqvEZ58f1+s0LK13eZ48/5bUSkUcVIRotPRYoZUByuWZ9r6h4jQsCN/A/siwrSJyDx6/Lx0edgn/i1zbk8FznDyzCdR7fGwNaTfWHVZ+8cWGhtrw7buyGfdvqj3RYS9mGmFbUmTfxDmwjH2uxshdQbfuLaHCEinl+dWlMWMMgdOHuUdHdDmkv/dr5GCVV9WHyiicj44sI86gOxcvvPNscBopn33+8bkVq/rsqCROudbfCPFDilfY9acqZG3C018Z7LgO18nBTsp37yTBdka6G+z/y9nCUd9NLIX3Mk3U+yQ8nW+s2Am0Ih7/7HLnrI1CBzbRFYOZnLvzMlmY0fguHMd21ffbqxIxcHuuIvq7Q8zW+vFqtiQIbjqZ76c7JOZee8NUS+xIDp1P1zOs/yQRr9CdTSwlM1dQxt1D3D/s7Ck8RxKtdG8dBjVmf9IReOZ2C04Ucs/ZhTSMOFnRshbHeLZqW9xfyisEaCzsZJF97/F1GcPGTNY/3/x8LCXyFq6NUbu3+cF0/mc8D61Pwg6QD3nUYUGvZiYg17gQ46+44t7D/2do5z8PMi99RHy7njEeHlovVwZvwRihfrQayjKWxgZxgPgeeZn3kReUY0ZwjxAzfL5LKw+ZJY7z4nalTizl4fCi3rNcrL/Jpui1p5aEi8dwZKGsOA2MkfbLe3OojAirFhIlvNBqg6ZjpaZKpYdaq8Rnvzphrc4G1F3dLvNEOVPf8UfIwvGEKBRR1bh2vCL3wypLqk6MEScW4BWaubfFBEa1mse4MaFm0PjXs8yN8fPiLA0pjyW81yccbl7AnQeauDVXa2g3cHdN37F8v0mljhnMT+k32BdTuaW7+sq+5rvk5mZF7Z1vYblNy6jOhg17GIvB6gpuoO/ySqlR/MdYgSOvMuuVuCaP/LU7Y5uJiBnaHzuMYo8X6ag7BHui7qPfUoBO5VC+VYz2+ybdOocee8s4OiyqNEtI8cwfiSEHeIRTLj2b9CA1h2/493OAHT+md07moEM8m6YZKwsf3yGkzrAOMaOMtea7VcydsJI4H2ajn0EfMKZk2eAkUwYe6XZpuGMGmtMlFubjnEqYen1D5fNsQ3ob7H+5+uoc36fe2aMi1PoGNvWrKOp4B/ISQvOhOykaH/dc2pByl+j9WZFbCgRaGHrmmoIvZyuIG3mHWTX/ZLKGGEAreBRVl07xdEAACAASURBVC+7xZgIpEwj96ESipujQhvaEp5Z/Y9mGshopuQu5+Hik1S8vD/GKt+HeJ5aTVX6gzwUvC+jmVawhs0L/sADT+2hnfP43n0bT/otzJxiTlvsE5m9eidqZwFTklQ1PRL4kKPvnGHqxJvJf7HJDFm9xUPXelkRN48qQOfxwzT1dO9OH81NfbONalDSWkTmF6I3aERvvgBCYTSFv2Ulfzf6YiM215C78R28ZU4AUsu8fKZ2syIj1uhjpoHo4bqV/yjugunAAd48Er3aouEsdtPc4beU06l78898ANC+h6ce2ICOhrO0zsyX9rG3wpVAikScdITQaswclt9zg7mYYPZlPRz+DIUy9SpWveilHQgcruf5qgNANqX1x80c7womvbc3MrcwcISdz9dGtfs4myedZEu3phmg3fM8D1QdQHNVc7DDj1J+Og5W49IOULNqC/uGUo64sxR3cxtKneO4e4k5ydjHgQ8ukJjMz9K8+zU8gFZcT5tSKPUR3rI5wA6KVtYmsDjwPHmOL1j6yjBGXbeQGlxUbHuIeRPNCV+ghZeXlVKjY5H9OXz1j+BEx1P0WAxHOpti98HIcDn7efPAKay6jLSXPVS4pvedjAcFlrHZs4Wa4CSkS45rgM7GF1lRtAOt4FEe+156VIpCrFufoGH1wxR5dIjrJw3nK9NnkA1Q9xLVnhMEOM+J116kohVAp/X0xwQYwcR5D7GtwoXmWU7mqGHYRt1IkefLuCrW8xNnVKQ8NY1J43tq4TWkT/pST09xWRgY90J/kqwxwyJeSMMcj3Hy1ifxbllCRkwHtJ1D1Y/xwJHvsvmJu5j4eXWEYhA4/HteqnNErHTb027m3mxfjHCzRvotX4tc3U5xMDU9qmz6DUyPWN1O4eqp16KH8jctmC/ILiHviGuG4/jGTTjrfknltv+kodbTzekWnyPs0yjYeZjdERGH0Uy57TZmNBv55iKlSyUYRgN7SgojB6TOK5hS8DJKHWXLrA+pq91KVekPyTNz6c6eaota2ZzJgsJvMyXFDvaJ3DTnloi/hlfov8PSpbPC+dKF+abD2gNd0hEsaQihkCXhMCoHqClMZ5TNhs02husKq9DB7MtnObznt2a4814WOieaOd5Z/PThhRGOtjE26cbzLfx7s90Tmf3TEoq7bfdZ/rL/baPOmoVcN2pY2BHTiRlSHbxoZC9wMW/KaGAEE2+aze3WPyck8+GhFTZ9bRbT8svZWtvAsa8+akwWLmVxQK+hbEMtXjNCFNbZfJ546F6mpdiBEWizl/BwcQawg+d2t0aGl7PvpXDetKhweRDLZirXIpbODtrLTRTmzx2iues9MR1XxZ6oCaglLzWYgqAt4ZnHEvBnAidoWFVAVjB3tvwHcfwksKdlsahgOlDHk1lXM8z2Ra7O29B1M9vZNo6fPBn1/f/lvZMf0HE2ud46l+9UBLWTNQV3kREzVcDYHTh7FTzx7INJmk5wsXxqvmQaWZs1PjxZGHYdhXV6bEc0DgmFpvXDHD0ZO0Sur81iTMTq2V8xtfAV8692UjKWsb25nBvPvM7jebOYOmrYEMuZG0BSHExNh6ZmXwzZ2Em5Oo30hO6RnK8NIM7msefI1aJWFhJNPzh1jKY+i4sG6Dy0kXzHF3FkziEvb74lzAwjx4+JcrAtoT6GM35SGlbXINBx2gjZauMYe6VlmA6FGHsiOh3hRDgv7t6bSQvesicZ6Kc58/F5Ok4bwcXIyaydkWPGRTxXuN1ROuix3R9xrKm7Y7+ic0IHM9YQLTB+EulW5SYk8+FMnPcQ26uLcQJ6TRHz8/LIm5eJY1gOJbWHEhhDu24eUx0HcRdno9c8wDzzNJWQzrrkiF7BmPHGLpYu4eUJYxkVesAvMSndugHwQtheJozlytD3Xe1l6GNn9OzV+FQTGx+8JTwB/Ye7jcmMfpijJ/8nnILwTFF4pTweEU5tNqX1z7M8Y2w3TZhE7no3dWXBaE42xdW/puy+VEAjddyV2PmQhsfvJ29tU0Qkpb40Hc/aPJyPeyIXxFoPc+xUT5mywdSEwcfgWwcN6Oxbt9RwahvWUTDt8u3bHZS0/57KVXvIrjyIP3p3eFs9xVSzZmtiq35dV1xjFYrnJGix2xCRI2QnZYqT3II17FYKv8+Le85JVmXdx+PJdqZwP2OfMJnr4/qsZv6V/SomX++Ie4+E9J0MjBzHmJED/KChcK6Gs/hXuLd58fk7QmkMvcU+apzh6EZPLM+2caqnXFXjDpHpCLW/Zoe5KSS8XwG4ciwTNAAnZd6Orn1ZPUeudgWjxo0HoifDAc62nY5Yib74dv8VYycYL28j5SO6HUl0ekJCMh8Odo2M/DXsVn46mnfjdrt5pcyFRh1r837GyxdzCk7KNL5zz+2kArp7P3+5YNFZ6z7ePWK956e0neoAIDV9EuMTrmR4wvYy9AmeG+0gp+pQ7Pfuhb+w+7kdwAGq8iYzzGbD5sijBgimi4TOu7U6tVoBlQdfYbW54t0tKVO4fcVGfMFFw+9Phb2thN4NoSjBVObk3RSKpHwrL9IWwrZ5mjMdpmMb+JgzJ88STj0I9tWznDzzsfnMF+g4Y0RUemcr/cOges0F9F2szMrgxtcm8oxHnNquBDeN5bIo59quyhv9dXIWOKI2keldVwE7fTQ3RaYy0HSY4xGpAuYmJ8smk+h6mt76c9Qu7jM0lt8V52B4sGsZ5P4wnwVavB2eyY/xQxcxflAjlk6spDiYmn6mi9yMkPW1TL06hVAqSPRKvJnXmz7VkYTH3iVCcCBuZdeO/zLOWQycoOHp582XSx8Qym/+MtfemM28uzP4ygdv497RfFG3M1KLNGAPm6r/0+hnAZ19lRuNdIJEGH0D9yyfA+jUlZZSoQPZdzAzzbIqF0pZ8FDxlJtDnQFDNiuN3fqOkgbagzn8YMnjg4Bezy8er44IbcZu9wkafrGGtd22O7zC3FrxS2oOtQPn0RvM0y16+hGeoUQiMg8co7Yw3TgtYWU9HWlOcnNzyf3e3dx5KUGZwAl+595lrNDeci2O4VadvcKqn79ktIfz6A0beHytkZO9eFZqzzmhIeLZy9tUbtzeRz+mMlgwxlzQqdv0Eh79PNDOod/sYBeY/S3RNeozNFYsCqcfbP/fCflAxjvFhs12F+WNZ4z6a35p5NgGTz+xX8m4VA1oZof77VC/DNlC6jhjBT5km3vY9Nqf6CRA5x9/w6Y6HbSbuOGrIwn3VZ26Tb/hj50B6PwTr23aQ8RGtMuJikGcry+CU6q+OEOhlar6Nn/3RTv2qjKnptCWKPfxcwne/xPVXDm/x/v7mytVNhmquP5U4k1PkL6TVSIY8tSK61VbzL/7VYe3QjmZryqbP1GqrV4VayjIVsXug6pDKaU6mlSla7rSCtzquD98T9CUs9itmjv8Sqk2dbCyQGkWXRgyNO+rlPIfd6sCbbpyVexRPr9SSrWpZnepcjJHlXk/UiHdOEuVuznYWrNMr3TcNwysnrrjI+Utm6M0V7U62BG0WUPeqSGdxOKcOu5eojStQFUeNOTp9+1RFa7pEfYQ0ktlk6Fvv0/trXApLZE+OID0Rh+fectUKihSy5T3s24K+tzKFbOcKTtQWD83O5VTQ8Ei5fZ9ppT6TPnci4y/udzKp5QK2XHoOqcq83Z0rdt/VLkLpkfeH03d7LxZaRDWUbCNoTqjnjFUb5w299SO6GY1V6rs0DXTVYH7qIq0Ar/qOFitXFqMOiy2Fvv5pqv7XPOMNoZkHrvd2n0udZ9mbfd7yu1KVZCqXO73jDrMsanrs1rsOQEGpq9bbCX07DGeSSmlPvOqslSrzhOReXAsj61/rbuxIlRfd5/gOK16qEtTzrK9puxj9Q+llOpQ3jJn5HPHtBfLp6e+3AcM2Jgf9F26lXEUMcYBY+yOr7PUMq+KKbK4srb293PKV/9IHB0nYgtWO+jmmZ0VytsxMO+Z7vQ7SFZsP6Xl5XJj95++gbyrv9jlp+mMlYPPN4GW11izdoJlR3M0wTPn9rCq8vemvDScxd/lb488yRSbDduo7/Fmejme9fMiE9idC1jwt0f4+ZRh2GxjmP3mdDZ6VpMbJx/IPnEe6z3rufXkYziGGZsfnK+OY6n3BTMf6AqmLFyPd+k4XnWOMfVoltn+UM95RknLWDKWv8D2u0/x5JTghsr5vMj3+Q+LToxZuDW8NYKJ2T9m8xNXsem6MeYGzMW8k/4o238yM7yJcOJtlGx+kAmbso1NKcMczHsnnWe2L8UZvfL+uWFERL6i0ScqqX9hJXN6XEy5grQ7l1l2c18JsU5ptE9i3hOVVBdnm19kU1y9ja2b/4nboVe576E2567GU1eBy1yh01wV1P13HWWp3V8Z0azQahygzeX7t10TFemxkzJtARs89VSG2m7WZY2aRefxaS7K6txs+PEtUTmT0e2ejqvst3g2LOGbPck6ZToFG9zUVwb1ZKmnYHoSRRsSkXlwj0JkGcimuLK+6/jdCzRXGe7QOI2lrt1mqoOJs5jKeg/bV8zovey75H0advDf9U/SC/MdGqTMYPmW7bgtsusq4564wAd762L8GlkCdJF1sP46XsidFDomVZtdyhavmzLLyRSxbWFJVDljLNuy3GIHXZ5Zw1lc081hAAOLzfR8I7+02YjxtRCDQS2r9gZKpt3PO0808JuCaXHyTj6koeQOst75XzT/JnmP4RrUevocIvoYWlxoLGdaZhGtTKfA/br5wjxDY/n9ZBbtAJcb38ZctAuNlE/LpKgVtAI3//VCLhPtATob1zM3czkeFuH2PdN1w18fIrYlgNhBstOdfpPUjREEQRD6iuFTv8Vip0bEBhjblwynlukU3J3BVwCGpzJr8RwA9Ko8rh5mnp+audw4k7Ugmxu/ctkz8ARBSGLEsRUEQRC6J0a4FTDD1W7Wh0KeRqqN110WSqEwuPQQuiAIQiJIKsIlIrIaGoieBheiD6G/ENsSQOwg2ZFUBEEQBEEQBCHpEcdWEARBEARBSArEsRUEQRAEQRCSAnFsBUEQBEEQhKRAHFtBEARBEAQhKRDHVhAEQRAEQUgKxLEVBEEQBEEQkgJxbAVBEARBEISkIO4PNAiCIAiCIAjCYCTeDzTE/dFu+cWOxJBfNxkaiJ4GF6IPob8Q2xJA7CDZ6W4BVlIRBEEQBEEQhKRAHFtBEARBEAQhKRDHVhAEQRAEQUgKxLEVBEEQBEEQkgJxbAVBEARBEISkQBxbQRAEQRAEISkQx1YQBEEQBEFICsSxFQRBEARBEJICcWwFQRAEQRCEpEAcW0EQBEEQBCEp6GfH9kMaSjKx2WwxPunkl9fSqJ+Pc+0ZGsvvwlHSQHvE9wE6WxqoKskJ38uRT/muFjrjNaNzH+WzbqGk4cO+fLjLSDstDbWRMrDlUFL1ejfy7I5Paam6B5tjJQ3tgT5vrRBNOy271pHvSNB+gwR0GjeWMKtbnZ9Hb9xEySyHWcbBrJIXeLVRZ6hq9kJjOWkxxxDzk1+L3jc1odcu7nrPzhYatpazeF0jF/qknkvgQiPlaTZstlmUN8ayGMuYm1NFSxelB2hvWInDZsNmu4eqlk8HoNGxeJ/a/DRstjTya9+/TG24FCy2klZO42U3DAshG4n1cTCrZNNFvif6omlmXx5sMrskrH0q8pNWHnvMCJyopdBhw2ZbTK1uLdFOS0OVZfzuyU8K3lCnsbY8/E6ZVUJVQ/Q7JUBny07K89ND7XPkr2NXS3vUzaLfITmUbNyHHjWWBPR9bAz5IJfXrqIZmBVbrZT6Nj9KqfCn41+5temfybxvA42d0aNvO4c2PsnKf9/f5VaBE9tY5lzGmxN+hs+vUOocvm0zaMrPY1ntsa4v784DbHz8Sf7dc66/nm5gCZygYeV8pt7/Kqdve5oOU55+32puPP0Kcx1zWdlwopdOzBVMKXgZ5VvN7NGyiN+/nOdE7Uqcq09xt6cNpfx0eO7m1OpvM7d8XzfO7XlObPs5c6thodeHXyn8vp8x4c1S5v7LntDkL3DidVbN3QwLtxn9w9/Imgl7+NHcp/HIpOUi6KTxuUVkzS/iDf/lbksijCMzJxsNoO637Dkc7biexruzDh3Qihfz3SlXDHwThcuIjmetK857V7g4znPy6OHEJ9eBY2z72T9T1eWCdg5VPYgzq5C1nuAfD1BTlNe9vgInaFi1kMy8ImqCl3nWUpgV6RMZvtMdFNUcCF2q1ywn27mS2hNBhzRAZ+MG7st0WdpQx9qFhazaZvGvOvdRcd88Fq6tC94Jz1oXc1e9zonBYFYqBnG+vghOqfriDIVWqurb/F3+6m+uVNloKrvyoAr+1e/bq6qLl6iyvXtUZbamtOJ61Ra64hPVXDk/xv1ifX9O+bw1qti1Xu31/kplk6GK60/10XOF6TtZJcJHyls2R6EtUe7j5y7i759fBlZP3dBWr4q1aFuMZ9cW/AdVZXZqRF9RyuxDoevM+2RXqmZ/9LU394v9Xyy90cdn3jKVCorUMuX9rB8bFZMO5S1zKkCllnnVgFcfzWdeVZaKAqcq83bELtNWr4o1FFFja+Tf+mc8HAwMTF//TPncixSXzS67IWQji5TbF9kw/3G3KriM+h/IvjxwY77p5zBfVTZ/0kNZv+rwVignGLZj1VGob05Xrsom1aES05fhR6FgjirzfqSUalMHKwuUBpZ3Sngcw1mhvB1+pTr2qjKnFjW2BZ9FU86yvarD2t7Qvfyqrb7UuH+Xew2cXXWn30GwPKfT1OwzVqoCh6he+BjNOaUsz/xyjLI9rCzqhzl60ph5BFo2s3DFEXJ+sZjMUcP68wEGjvb9vFyxn+wnHmDexBExCowl44fLKWYDDzxlruK1N1DicJDzwi72WcLYkSGIWKkI0SGRHEqqGmgJzRrNkGfOBhr2WcIWXcLq0fdxMKukioYu4Y/PCaNns8bnZc3sq3p3XaeP5qaxXD/5qogwi33CZK6njp3e00Anx5uPoF0/mQkRha5i8vXn2LTzTyS11PVa8m02bGmPUrvzUdPW76GqpSV22DsUsg2GA6NTEd6nNv96Mos8ALQWZfKFuCkAECvUZ3PkU17bGA7jBdtoW8zWxj9QW55vhjDTyV/3VlS4LzJlxeizp3uWw+ivk7MgA9Cpe+n3HA7dM0C79w026YCWTU7mOEu7reldcfpoZwu7Qu01wpPv74sVWra22wxRvv9WVApFnFSEzhYaqizpNjFDqoMVyzNt/UNEaNiRv4F9EWHaRGQePXZeujzsE/+WObenAmc4eeaTqPZ42BrSb6y6rP1jC42NtWFbd+Szbl9UulOEvaSTX76TlrakyT8Ic+EY+92NkDqDb1zbQwSk08tzK8rwxPhT4ORR3tEBbS753/0aKVj15eOdox/GiMRe4IMD+6gDyM7lO98cC4xm2ne/zwIN0IPvhjCpc77FN1LskPI1Zs2ZGnm79j+xc1MjMJMF3/k6KdhJ+eadLMjWQH+b/X85Szjqo5G94E6+mWKHlK/znQUzgUbc+49d9pStQeDYZrAg5+uMBrBfzZ3V/8bq2RMvrmHZdzAzzTAsu+Muqrc/zGwtlgM4FAm+lBxdnJsIzJeavukNvCEnVafuh+W4R/2A7Uqh/MfZPPG3ZC+qjBPeMEMi97/JhDWNlrD3MqbOXR95Td3Pedx9JYXbjxtpIZuvZUf2cp5rPIMR1qhk0f1vMfXZQ0YKiv+/eHjYS2Qt3Roj/+/zyHn0fZX8fNVBCp5ZhDNOKkho0IuJOegFPuToO764NenvHOXk50HmrY+Qd8cjxstDS2PyhIEZA2KF+tBrKMpbGBnGA+B55mfeRF5RjRnCPEDN8vksrD5kljNTVrKXh8KLes1ysv8mm6LWnloSLx3Bkoaw4DYyTVsz2p1FYURYsZAs54NUHTIdrcAxapflkR1qrxGe/OmGtzgbUXd0u80Q5U9/xR8jC8YQoFFHVuHa8IvfDKkuqTowRJxbgFZq5t8UERrWax7gxoWbQ2NezzI3x86IsDSmPILja28J0HmogVd3tYJ2B3ff+BXL95tY4pzF/JB+g3U5Y6dI1XyfzMy8sK3rNSy/cRnVwZztLvZygJqiO/ibrFJ6NN8hRuDIu+xqBa75I0/d7uhmAnKGxuceo8jzZQrKHuG+qPvYpxSwU6nIhbtOnSPvnQV6eO9HM3IM40dC2CEewYRr/wYNaN3xO97tDEDnn9m9oxnIIO+GSQwH+PgMJ3WAcYwdNdxs2JWMnTASeJ+mYx8Bn3Dm5BlgJBPGXmm2aTijxhoT5damY5xKWHr9w2VzbAP6W6z/+TrqnN/nnhnBlYMUNC2l9/c68R+sWXWQgkVZpAWfKOWv0VIGgd/eZwTzeK5l6tUJyMiyeg2gFZfwUO40UgDsGpm3ZaB53uZdX4xk73YvL67ax53PPMayGRp2wK7dwoNPr6e4uYyVL7eEX9LaQh5+aB5TUuzACLTMv2eGtp9d754kwHl8776NJ/0WZk4ZbZS3T2T26p2onQVMSSb1XASBlipybF/EceMD7Lp9MYtu0uJ0yACdxw/T1NMNO300N/XNNqpBSWsRmV+I3qARvfkCQMNZtpcOpfC3rOTvRl9sxOYacje+g7fMCUBqmZfP1G5WZMTqf59yeOe/UaWH61b+o7gLpgMHePNI9GqLhrPYTXOH31JOp+7NP/MBQPsennpgAzoaztI6M1/ax94Kl+Gwdoud0Zm3GSs27OGlPUeNukOrMXNYfs8NxmICH+J5ajVV+nRclU1mzn4bBysL0PQqVr3opR0IHK7n+aoDQDal9cfNyW4Fk97bG5lbGDjCzudro9p9nM2TTrKlW9MM0O55ngeqDqC5qjnY4Tfyzw9W49IOULNqC/uGUo64sxR3cxtKneO4e4k5ydjHgQ8ukJjMz9K8+zU8gFZcT5tSKPUR3rI5wA6KVtYmsDDwPHmOL1j6yjBGXbeQGlxUbHsoHPULtPDyslJqdCyyP4ev/hGc6HiKHovhSGdT7D5o9LHjbgo0gP28eeAUVl1G2sseKlzT+07GgwLL2OzZQk1wEtIlxzVAZ+OLrCjagVbwKI99L53hPd76BA2rH6bIo0OEn2RlOF+ZPoNsgLqXqPacIMB5Trz2IhWtADqtpz8mwAgmznuIbRUuNM9yMkcNwzbqRoo8X8ZVsZ6fOKMiiKlpTBrfUwuvIX3Sl3p6isvCwLgW+pNkjRkW8UIa5niMk7c+iXfLEjIuxQHtPED1ytUcWVjBE/MmDYYl6CGAnZSr00jnCM3Ho+fiwZXh67hl+lci5ZniYGo64dSRWESUGYHjGzfhrPslldv+k4ZajyWVQQjN0P3H2TzxNW7MWG5J4hcunmAYDewpKYwckDrNNCl1lC2zPqSuditVpT8kr8pY0Tp7qi1qZXMmCwq/bUwI7RO5ac4tEX8Nr9B/h6VLZ6EZs0tmFOabDmsPdElHsKQhhEKWhMOoHKCmMJ1RNhs22xiuK6xCBzPyc5bDe35rhjvvZaFzojnZzeKnDy+McLQDh3/PS3W68XwL/95s90Rm/7SE4m7bfZa/7H/bqLNmIdeNGhZ2xHRihlQHLxrZC1zMmzIaGMHEm2Zzu/XPCcl8eGiFTV+bxbT8crbWNnDsq48ak4VLWRjQayjbUIvXTI0I62w+Tzx0L9OCixSzl/BwcQawg+d2t0aGl7PvpXDetKhweRDLZirXIpbODtrLTRTmz01gYjYUmY6rYk/UBPQAVQ88b2zaDaYgaEt45rG7mNiT7gInaFhVQNaTdcAcysp/ENdPsqdlsahgOlDHk1lXM8z2Ra7O29B1M9vZNo6fPBn1/f/lvZMf0HE2ud7Ll+9UBLWTNQV3kXEpqQKdB6ha8j1WsZRnV2YZg2jSMoIJk9PQYjqjMbjoEGzPOzwTD2nbSclYxvbmcm488zqP581i6qhhQyxvbgAIvvip5fmdR2LkUQUnIj2Q4mBqenK+NgBILcP7mYoaR54jV4taWUjU9k8do6nP4qIBOg9tJN/xRRyZc8jLm28JM8PI8WOiHGxLqI/hjJ+UhtU1CHScNkK22jjGXmkZ2EIhxp6ITkc4Ec6Lu/fmcGSrJxnopznz8Xk6ThvBxcj8bTsjx4yLeK5wu6N00GO7P+JYU3fHfkXnhA5mrCFaYPwk0q3KTUjmw5k47yG2VxfjBPSaIubn5ZE3LxPHsBxKag8lMH4uwu37LOo0ooO4i7PRax5gnnmaSkhnXXJEr2DM+FFAjPDyhLGMCj3gl5iUfo3ljxfC9jJhLFeGvu9qL0MfO6Nnr8anmtj44C3hCeg/3G1MZvTDHD35P+EUhGeK4uyPsRDh1GZTWv88yzPGdtOESeSud1NXFozmZFNc/WvK7ksFNFLHXYmdD2l4/H7y1jZFRFLqS9PxrM3D+bgncv9F62GOneopUzaYmjD4GLKuYEB/i3VLvscqVtCwYYE5y0xmguHFeEnkJma40ZpD1zuCDnR8umxO6hY7KVOc5BasYbdS+H1e3HNOsirrPh5PmnOF+wo97mq4fcJkro+rFDP/yn4Vk693xL177/Q2hBk5jjEjB/hBQ+FcDWfxr3Bv8+Lzd4TSGHqLfdQ4w9GNSinibBunespVNe4QmY5Q+2t2mJtC7p05OTzwXzmWCRqAkzJvR9SkIThxuIJR48YD0ZPaAGfbTkesRF98u/+KsROMl7eR8hHdjsNszL2muxsMHRKS+XCwa2Tkr2G38tPRvBu3280rZS406lib9zNevpgziFOm8Z17bicV0N37+csFi85a9/HuEes9P6XtVAcAqemTGJ9wJcMTtpehT/DcaAc5VYdiv5cv/IXdz+0ADlCVN5lhNhs2Rx41QDBdJHTerdWp1QqoPPhKYnuOUqZw+4qN+IKLht+fCntbCb0bQlGCqczJuykUSflWXqQthG3zNGc6TMc28DFnTp4lnHoQ7KtnOXnmY/OZL9Bxxoio9M5WCCDOWAAABupJREFU+och+Jo7j97wKFmO+bw24VE8nwun1mT0Ddyz/AbqVj3Dtpgh6zM0/qqCtSzhmR/PNHPoekvwhXiQtw58ELXL1UdzE6RPddD7TGjz7loGuT/M79lBT1KMvNrMOD8WYtlIGU2Kg6npZ7rIzAhZB/OuU7h66rVdV9QDH3L0nTOXpLehTXAgbmXXjv8yzlkMnKDh6efNl0sfEMpv/jLX3pjNvLsz+MoHb+Pe0XxRt7On3cy92Rqwh03V/2mclhDQ2Ve50UgnSITRN3DP8jmATl1pKRU6ERtsjTLBlAUPFU+5OdQZMM/KNnbrGz+QcwVpM++IyuODgF7PLx6vjojuxG73CRp+sYa13bY7vMLcWvFLag61ExzrZ9lsyfXjMYnIPHCM2sJ047SElfV0pDnJzc0l93t3c+elBGUCJ/ide5exQnvLtTiGW3X2Cqt+/pLRHs6jN2zg8bVGTvbiWak954SGiGcvb1O5cXsf/ZjKYMEYc0GnbtNLePTzQDuHfrODXWD2t0TXqM/QWLEonH6w/X9TMK3nt7jxTrFhs91FeeMZo/6aXxo5tsHTT+xXMi5VA5rZ4X471C9DtpA6zliBD9nmHja99ic6CdD5x9+wqU4H7SZu+OpIwn1Vp27Tb/hjZwA6/8Rrm/YQsRHtctLb88F6R/fn2HaL/2CMc2zDZ6ppBW51PMFbGue8JcM5tkop/3FVX5qt0FyqrK5ZBU+y9Pu8yl3mUhrZqrT+ePjsSvNsvEg5Rssk+hxV8xw8zaUq9vqMe3U0qUrX9PC5dfF0G6E3877OUuVuDtbepprdpco5wGftDrie4mKcNay5qtXBDlNupk4jvuvCOXXcvURpWoGqPGjI0u/boypc0yN0a5x7GD4HUfl9am+FS2kX0wf7kd7oI+GzL31u5YpZzpRd6OxI83OzUzk161mSlrNJXW7lU0qF+kboujjnx/qPKnfB9Mj7o6mbnTcrDUv/C7Yx6ozR0DOG6o3T5p7aEd2s0BmXxvmYBe6jkefaKr/qOFitXFqMOiy2Fvv5pqv7XPOMNoZkHrvd2n0udZ9mbfd7yu1KVZCqXO73jDqCY0yXZ7XYcwIMTF+PdY5tjGdSKsa5sonIPPq80yh5dvf+C9XX3Sd45qnqoa7geaZRzxyyU6XCZ6RanjumvVg+yXSOreU82PgyjiLGOBA+szb2J+452nFlbe3v55Sv/pE4Ok7EFqx20M0zh/yD/qc7/Q6tpc5ACy+vNM6A06vyuHpY9A7peCthSYR9IrNXb8e3fT7j3lhqbjywMcyxkr3j5rPdt/3ij0sLMZppBevwbL6VkyUZRuhk1D/RfOt6mrcv68VmvyuYsnA93qXjeNU5xtTRGJyvjmPp9od6zjVKSsaSsfwFtt99iienmBsqhxWwM60kIvpgzMKt4a0RTMz+MZufuIpN140xdb6Yd9IfZftPwqvz9om3UbL5QSZsyjZsY5iDee+k88z2pXGPEkt+RkTkKxonElRS/8JK5vS4mHIFaXcus+zmvhJindJon8S8JyqpLs42v8imuHobWzf/E7dD1PF7CbY5dzWeugpc5gqd5qqg7r/rKEvt/sqIZoVW4wBtLt+/7ZqoscFOyrQFbPDUUxlqu1mXZ114xSg6j09zUVbnZsOPb4nKmYxu93RcZb/Fs2EJ3+xJ1inTKdjgpr4yqCdLPQXTkyjakIjMg/sTIstANsWV9XjWz+t5A1IcNFcZbu8LlrzNYF27zVQHE2cxlfUetq+Y0XvZd8n7NOzgv+ufpBfmOzRImcHyLdtxW2TXVcY9cYEP9tbF+DWyBOgi62D9dbyQG9xQPwJtdilbvG7KLCdTxLaFJVHljLFsy3KLHXR5Zg1ncc2lHwbQR9hMzzfyS5uNGF8LMRBZDQ1ET4ML0cfQ4kJjOdMyi2hlOgXu180X5hkay+8ns2gHuNz4NuaiXWikfFomRa2gFbj5rxdymWgP0Nm4nrmZy/GwCLfvma4b/voQsS0BxA6Sne70K47tJSKyGhqIngYXoo8hRuc+yufOM87U7EIcZzcGYWe3/5oqtiWA2EGy051+L/+asSAIgjC4iRFuBcxwtZv1oZCnkWrjdZeFUigMLj2ELgiCkAiyYnuJiKyGBqKnwYXoQ+gvxLYEEDtIdmTFVhAEQRAEQUh6xLEVBEEQBEEQkgJxbAVBEARBEISkQBxbQRAEQRAEISkQx1YQBEEQBEFICsSxFQRBEARBEJICcWwFQRAEQRCEpCDuObaCIAiCIAiCMBiJd45tzB/slkONBUEQBEEQhKGGpCIIgiAIgiAISYE4toIgCIIgCEJSII6tIAiCIAiCkBSIYysIgiAIgiAkBeLYCoIgCIIgCEnB/w+GqHcpZPq8RQAAAABJRU5ErkJggg==\"/></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for explaining a problem that may arise (such as inconsistency, inaccuracy / unnecessary additional/repeated work needed when updating)</em>.<br/><em>Award <strong>[1]</strong> for including example with reference to the relation given</em>.</p>\n<p><em>Example answer</em>:<br/>When inserting a tuple supplied by the existing supplier, for example, inserting:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>All data about supplier should be accurately entered;<br/>And consistent with the data about this supplier in all other tuples;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for explaining a problem that may arise (such as inconsistency, inaccuracy)</em>.<br/><em>Award <strong>[1]</strong> for including example with reference to the relation given</em>.</p>\n<p><em>Example answer</em>: <br/>Deleting a tuple could remove data which is not intended to be lost;<br/>For example; deleting the following tuple.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Which is the only tuple in the relation which holds data about New Fruits suppliers (all data about this supplier will be lost);</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for explaining a problem that may arise (such as inconsistency, inaccuracy/unnecessary additional/repeated work needed when updating.</em><br/><em>Award <strong>[1]</strong> for including example with reference to the relation given</em>.</p>\n<p><em>Example answer</em>: <br/>Modifying/changing the phone number for Fruit and Veggie. would require that each and every tuple containing item supplied by Fruit and Veggie should be modified (unnecessary work);</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Otherwise the data in the relation would be inaccurate (for example, would contain different/incorrect phone numbers for the same supplier);</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">c.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[8 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for explaining/clearly showing each of the steps.</em><br/><em>Award <strong>[1]</strong> for identifying two entities (Product and Supplier) in Greengrocer.</em><br/><em>Award <strong>[1]</strong> for choosing/identifying a primary key in relation Supplier.</em><br/><em>Award <strong>[1]</strong> for clearly showing that the relation must be in 1NF and then could be put to 2NF.</em><br/><em>Award <strong>[1]</strong> for showing that each tuple in 2NF has a primary key.</em><br/><em>Award <strong>[1]</strong> for showing that in 2NF all attributes are on the whole dependent of the primary key.</em><br/><em>Award <strong>[1]</strong> for introducing new relations or using a foreign key.</em><br/><em>Award <strong>[1]</strong> for showing that the relation must be in 2NF and then could be put to 3NF.</em><br/><em>Award <strong>[1]</strong> stating that there are no transitive dependencies in 3NF.</em></p>\n<p><em><strong>Note</strong>: Because there is significant inconsistency in textbooks’ accounts of precisely what constitutes 1NF and 2NF, award marks depending on the explanation. Please see the two different example answers</em>.</p>\n<p><em><strong>Example answer 1</strong></em>:<br/>Given relation<br/><strong>Greengrocer(Prod_ID, Prod_Name, Prod_Price, Supp_Name, Supp_Phone,Supp_Contact)</strong></p>\n<p>There are two entities in this relation (Product and Supplier).</p>\n<p><strong>Product(Prod_ID, Prod_Name, Prod_Price)</strong><br/><strong>Supplier(Supp_ID, Supp_Name, Supp_Phone, Supp_Contact)</strong></p>\n<p>Primary key (Supp_ID) in relation Supplier is added to make sure that it is unique because it might be that two suppliers have the same name.</p>\n<p><strong>Product(Prod_ID, Prod_Name, Prod_Price, Supp_ID)</strong><br/><strong>Supplier(Supp_ID, Supp_Name, Supp_Phone, Supp_Contact)</strong></p>\n<p>To construct to 2NF relation must be in 1NF;<br/>Each tuple in 2NF must have a primary key;<br/><strong>And</strong> all attributes in the 2NF must be dependent on the whole of the primary key.</p>\n<p>New relation is introduced.</p>\n<p><strong>Product(Prod_ID, Prod_Name, Prod_Price)</strong><br/><strong>Supplier(Supp_ID,Supp_Name,Supp_Phone,Supp_Contact)</strong><br/><strong>ProdSupp(Prod_ID, Supp_ID)</strong></p>\n<p>There are two different contacts with the same name (Mia Abiss) at the two different companies.<br/>Assuming that each contact has a unique phone number, a new relation could be introduced in which Supp_Phone is a key.</p>\n<p>Supplier(Supp_ID, Supp_Name)<br/>SupplierContact(Supp_Phone, Supp_Contact)<br/>Product(Prod_ID, Prod_Name, Prod_Price)<br/>ProdSupp (Prod_ID, Supp_ID)</p>\n<p>The relation is in the 2NF <strong>and</strong> there are no attributes which are not dependent on the key.<br/>There are <strong>no</strong> transitive dependences so it is in the 3NF.</p>\n<p><em><strong>Example answer 2</strong></em>:<br/>The table is already in 1NF because all values are atomic. There are no “fake” attributes/columns, like Supp_Phone1, Supp_Phone2;<br/>So there are no repeating groups, either obvious or created by repeating columns for what is really the same attribute;<br/>To be in 2NF, in addition to being in 1NF;<br/>Every non-prime attribute must be dependent on the whole of every key;<br/>To be in 3NF, in addition to being in 2NF;<br/>All the attributes in a relation must be determined only by the key(s);<br/>And there should not be any transitive dependences;</p>\n<p>There are two entities (Supplier and Product);<br/>Supp_ID should be introduced;</p>\n<p>Supplier(Supp_ID, Supp_Name, Supp_Phone, Supp_Contact)<br/>Product(Prod_ID, Prod_Name, Prod_Price, Supp_ID*)</p>\n<p>Where underlining marks primary key(s) and *foreign key(s);</p>\n<p>There are two different contacts with the same name(Mia Abiss)at the two different companies.</p>\n<p>A new relation introduced in which Supp_Phone is a key;</p>\n<p>SupplierContact(Supp_Phone, Supp_Contact, Supp_ID*)<br/>Supplier(Supp_ID, Supp_Name)<br/>Product(Prod_ID, Prod_Name, Prod_Price, Supp_ID*)</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.3",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A school currently has a cabled network but wants to add wireless networking across the whole campus.</p>\n</div><div class=\"specification\">\n<p>There are concerns that unauthorized people could access the data on the wireless network.</p>\n</div><div class=\"specification\">\n<p>The school has decided to implement a virtual private network (VPN) to provide access to its network.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> hardware components the school will need to implement the wireless network.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> advantages to the students of the new wireless network.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> methods the school could employ to prevent network data from being accessed over their wireless system.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> technologies the school would require to provide a VPN.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> benefit to the staff of using a VPN to remotely access the school network.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em></p>\n<p>(Wireless) router;<br/>A central hub for all the computers to connect to;<br/>Enables wireless network packet forwarding and routing;</p>\n<p>Wireless Network Interface Card (NIC);<br/>To allow the computer to ‘talk to’ the (wireless) router;</p>\n<p>Wireless access points;<br/>allow Wi-Fi devices to connect to a wired network;</p>\n<p>Wireless repeaters;<br/>To expand the reach of the network;</p>\n<p>Mark as 2 and 2.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em><br/>The ability to use their own devices at school;<br/>The ability to access the school network from anywhere in the school;<br/>No cables laid, so reduces the risk of tripping over cables;<br/>Numbers of connections are not limited to cable ports, so greater numbers of students can connect at any given time; </p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p>Use of encryption;<br/>So that data cannot be understood if it is intercepted;</p>\n<p>Use of user authentication/usernames and passwords;<br/>To prevent unauthorized access to the system;</p>\n<p>Setting up a file of accepted MAC addresses;<br/>To only allow access to the network by registered mobile devices;</p>\n<p>Hide network ID;<br/>So that the wireless network is not publicly seen;</p>\n<p>Mark as 2 and 2.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em><br/>Client VPN software (to make a secure remote connection);<br/>VPN-aware routers and firewalls (to permit VPN traffic to pass);<br/>VPN appliance/server (to handle incoming VPN traffic);<br/>Encryption protocol IPSec or SSL; </p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/>Enhanced security of data;<br/>for example, using encryption;<br/>This prevents unauthorised access;</p>\n<p>Remote access to data and resources (from any location);<br/>Normal access of materials on the network;<br/>as though the user was using the network on site;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly only gained half of the available marks for this question. This was achieved by either naming two appropriate hardware components, but not describing them sufficiently for the expansion marks, or by only naming one correct component with a good description. It is important to note that for this question, candidates needed to identify additional components needed to implement the wireless network within the already existing school network. Therefore, equipment already in place, such as servers, would not gain credit.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly identified one or two advantages to students of the new wireless network. Occasionally, candidates gave the same answer twice, mostly in relation to accessing the network from anywhere.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were able to identify and expand at least one method of preventing network data from being accessed over the wireless system. Some responses were not sufficiently detailed for both marks.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates found it difficult to identify two technologies that would be required by the school to provide a Virtual Private Network; however, a significant number did name one.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates answered this question well with responses related to one of the two suggested answers of enhanced security or remote access to network resources, or in some cases, a combination of the two.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "21M.1.SL.TZ0.12",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline why a prototype would be used to demonstrate the proposed system to the client.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>To provide feedback on the efficiency or design of the product;<br/>To give an idea or feel of the final product;<br/>To encourage dialogue between the developers and the client;<br/>Clients can identify errors or omissions in the design;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates were able to achieve at least one mark, generally for ‘providing feedback’ or for ‘clients to identify errors in the system’, but not all responses had sufficient detail to achieve both marks.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.4",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A web application (app) runs on mobile devices such as smartphones and tablets. It allows users to locate their position in real time on a map, as they walk around a city, as well as the surrounding attractions. The app uses icons to represent tourist attractions such as art galleries and museums. When the user clicks on the icon, further details are shown, such as opening times. The app includes some use of client-side scripting.</p>\n</div><div class=\"specification\">\n<p>Many art galleries have websites that can be found by search engines. White hat techniques and practices allow website developers to optimize the search process. It is good practice to maintain the source code of websites up-to-date with actual information.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the functioning of this app. Include specific references to the technology and software involved.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the use on mobile devices, outline a feature of this application that may rely on client-side scripting.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>two</strong> metrics used by search engines.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why maintaining a clean HTML source code of a website by removing old information optimizes the search process.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The evolution of the web, architectures, protocols and their uses has led to increasingly sophisticated services that run on peer-2-peer (P2P) architectures.</p>\n<p>Explain how a P2P network can provide more reliability than a client-server model.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for showing understanding of the need <strong>of each</strong> of the following: GPS, mapping software, underlying distributed information</em>.</p>\n<p>The GPS of the device gives its current location (coordinates);</p>\n<p>Coordinates sent out through the Internet (with 3G/4G/5G/Wifi/using the specific features of the mobile device OS);</p>\n<p>Coordinates are linked to map software (e.g. Googlemaps) to retrieve current’s device position and neighbourhood information (using thematic databases);</p>\n<p>And integrated with further annotations (relative to museums) that can be provided by third-party services;<br/><em><strong>OR</strong></em> by information incrementally gathered from users;<br/><em><strong>OR</strong></em> by further databases (GIS services);<br/><em><strong>OR</strong></em> by interrogating an (incremental) database accessible from the server where the app has been downloaded from, for its first installation;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em></p>\n<p><em>Award <strong>[1]</strong> for identifying a feature and award <strong>[1]</strong> for reasonable expansion of the feature up to <strong>[2 max]</strong></em>.</p>\n<p><em><strong>Example 1</strong></em>:<br/>The <span style=\"text-decoration:underline;\">design of the front-end</span> (the user interface) with the specific mobile device;<br/>So that the commands are specific (and built-in) within the device, to support interactive use;<br/>(With the effect that the app can be used across a range of devices and mobile operating systems;)</p>\n<p><em><strong>Example 2</strong></em>:<br/>To <span style=\"text-decoration:underline;\">personalize the app with a “history”</span> on the client’s side, after the first use of the application;<br/>So that only the new information added in the database (underlying sources used by the app) can be fetched by the client’s script;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Number of hits;<br/>Popularity of linking pages;<br/>Amount of links to the same page departing from the source page;<br/>Trustworthiness of the linking domain;<br/>Relevance of content between source and target page;<br/>Time to download;<br/>…(there are others).</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[5 max]</strong></em>.<br/>Old information is more likely to be retrieved by web crawlers;<br/>But this requires time to crawl;<br/>But this interferes with the response time for the current search;<br/>And it may even be penalised by policies in search engines;<br/>Without returning currently relevant information;<br/>Example of old information are old or broken links/orphan pages/old text masked by comments in the HTML code, and it can be revised from time to time;<br/>Cleanly written code allows for a more efficient search process;<br/>Which facilitates correct indexing;</p>\n<p><em><strong>Note:</strong> Award <strong>[2 max]</strong> for a generic response.</em></p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>In P2P all machines can interchangeably be client as well as server (no division of client-server);<br/>Data and services can be replicated across sites, and be retrieved from them;<br/>Services are virtualized/reside on virtual machines;</p>\n<p>Therefore, if one node is unavailable the service can still be provided by resorting to other nodes;<br/><em><strong>OR</strong></em><br/>System updates can be made while not interrupting the provision of the service<br/><em><strong>OR</strong></em><br/>The failure of a node does not imply the failure of the entire network</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.9",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-2-searching-the-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A water authority supplies water to its customers. The amount of water supplied to each customer is measured by a meter. There are two types of meter; an old meter and a new meter.</p>\n<p>The water authority also provides sewage services. This is when waste water is returned from the customers’ houses to a central processing plant. Only the new meter is able to estimate the amount of waste water returned.</p>\n<p>The authority reads the meters annually and the charges for these services are sent to the customers.</p>\n<p>The annual charges to customers are calculated using a model that takes into account the following variables:</p>\n<p><code>METER_TYPE</code>  <br/>The type of meter which can be either old or new.</p>\n<p><code>WATER_IN</code><br/>The reading displayed on <span style=\"text-decoration: underline;\">both</span> meters (the reading is a 6-digit display in litres) which is used to measure the volume of water supplied.</p>\n<p><code>WATER_OUT</code><br/>The reading displayed on the <span style=\"text-decoration: underline;\">new</span> meter only (the reading gives a 6-digit integer display in litres) which is used in estimating the volume of waste water returned to the sewage system.</p>\n<p><code>INFRA_CHARGE</code><br/>The cost of maintaining the infrastructure for supplying water and providing the sewage service. This is an annual charge of $204.80. This amount is reduced by 10 % if a new meter is used.</p>\n</div><div class=\"specification\">\n<p>The digital displays on each meter function as follows:</p>\n<ul>\n<li>The digital display on each meter increments by one every time a litre of water passes.</li>\n<li>If the display reaches its maximum value it resets to 0 and then continues to function as before.</li>\n</ul>\n</div><div class=\"specification\">\n<p>The rules for calculating the annual bill for each household are as follows:</p>\n<ul>\n<li>The price for each litre of water supplied is $1.90.</li>\n<li>The total annual bill is the sum of the charge for the water supplied and the infrastructure charge.</li>\n<li>If the household has a new meter and at least 95 % of the water supplied is returned to the sewage system, then the infrastructure charge for that household is reduced by 10 %.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the possible range of values <strong>and</strong> their data type for each of the above variables.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the yearly supply of water is calculated.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the pseudocode that will calculate the annual bill for a household based on the information given above. You should introduce any new variables where necessary.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> possible measures that customers could take to reduce their infrastructure charge.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for <strong>correctly</strong> indicating <span style=\"text-decoration:underline;\">type</span>, and <span style=\"text-decoration:underline;\">values</span> expected, for each variable up to <strong>[3 max]</strong></em>;</p>\n<p>“<code>MeterType</code>”, String/char; “old”,”new”;<br/>“<code>WaterVol</code>”, Integer; 0–999999;<br/>“<code>InfraCharges</code>”, Currency/Float /Double; 184.32–204.80;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Making use of both the previous and new readings;<br/>Calculating the difference between them;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[7 max]</strong> as follows</em>:<br/>Involving previous readings;<br/>Calculation of water supplied <strong>[2]</strong> – allow <strong>[1]</strong> if correct apart from the reset;<br/>Separate code for use of new meter;<br/>Correct reduction of infrastructure charge <strong>[2]</strong> – allow <strong>[1]</strong> for an attempt to calculate this;<br/>Calculation of final bill;</p>\n<p><em><strong>Example</strong></em>:</p>\n<pre>householdBill(OLD_WATER_IN, OLD_WATER_OUT)  // the previous <br/>                                            // meter readings<br/>INFRACHARGE = 204.80<br/>    if WATER_IN &gt; OLD_WATER_IN // allows for possible <br/>                               // resetting of meter <br/>                               //reading to 0<br/>        WATER_SUPPLIED = WATER_IN – OLD_WATER_IN<br/>    else<br/>        WATER_SUPPLIED = 1000000 – OLD_WATER_IN + WATER_IN<br/>    end if<br/><br/>if METER_TYPE = \"new\" // possible reduction in sewage charges <br/>                      // if new meter is used<br/>    if WATER_OUT &gt; OLD_WATER_OUT<br/>        WATER_RETURNED = WATER_OUT – OLD_WATER_OUT<br/>    else<br/>        WATER_RETURNED = 1000000 – OLD_WATER_OUT + WATER_OUT<br/>    end if<br/>    if WATER_RETURNED &gt; 0.95 * WATER_SUPPLIED<br/>        INFRACHARGE = INFRACHARGE * 0.9<br/>    end if<br/>end if<br/><br/>BILL = WATER_SUPPLIED * 1.9 + INFRACHARGE</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Changing to a new meter (if they have an old one);<br/>Not using the supplied water for watering the garden etc.;<br/>Diverting rain water into the waste/sewage system;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.4",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-1-the-basic-model",
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>State the hexadecimal equivalent of the following binary number:</p>\n<p>11011111</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>DF;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Generally, well answered, but a significant number of candidates gave their answers in decimal rather than hexadecimal.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.5",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Computer simulations are often used in situations where practical experimentation is, for some reason, not possible. One of these reasons could be an ethical issue.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between a computer model and a computer simulation.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> reasons why some systems are difficult to model successfully.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With clear reference to the ethical issue, describe <strong>one</strong> example where practical experimentation would not be possible for ethical reasons.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>three</strong> other advantages, apart from ethical reasons, of simulating a computer model rather than constructing a physical one.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>A computer model is a representation of a system;<br/>Made up of variables and formulae/mathematical representation;<br/>Whereas a computer simulation is a process that uses the model;<br/>In order to see the outcome(s) when different values are used for the variables (in the model);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Either all of the variables are not known/difficult to define;<br/>Or the relationships between them cannot be expressed accurately/mathematically;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em><strong>Example</strong></em><br/>Experiments on living animals in a high school science class;<br/>Such as effects of altering diet;<br/>It is not ethically acceptable to harm animals (for such purposes);<br/><em><strong>Note</strong>:</em> <em>Accept reasonable examples, provided they are sufficiently explained</em>.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>Computer models/simulations allow the designers to</em>:<br/>Make alterations and quickly see the outcomes;<br/>Repeat tests several times over;<br/>Model dangerous situations safely;<br/>Learn from “what if?” scenarios;<br/>Saves costs if several different models have to be built; </p>\n<div class=\"question_part_label\">c.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.4",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations",
      "b-1-the-basic-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Medical students require extensive training in preparation before they qualify as a surgeon. Part of this training involves the use of simulation software.</p>\n</div><div class=\"specification\">\n<p>The simulation software projects the surgery scenario onto a touchscreen display. Students wear “smart” gloves to hold tools for performing the virtual operation on the touchscreen monitor. The touchscreen display shows realistic surgical tools.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage and <strong>one</strong> disadvantage of training medical students with simulation software.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> technical aspects that the gloves have to address so that they can be used in this way for the simulation of operations.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>visualization</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The medical school is considering updating its simulation software, which currently displays the simulation in 2D, to one that displays it in 3D.</p>\n<p>Discuss whether this change to a 3D display would improve the quality of the training.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong> as follows:</em><br/><em>Award <strong>[1 max]</strong> for identifying an advantage and <strong>[1 max]</strong> for an expansion.</em><br/><em>Award <strong>[1 max]</strong> for identifying a disadvantage and <strong>[1 max]</strong> for an expansion.</em></p>\n<p><em>Advantages (requires an expansion)</em>:<br/>They may see a complex situation numerous times / gain confidence in using the surgical tools;<br/>Without putting patients at risk / or keep practicing until they feel confident that they will be able to get it right in reality;</p>\n<p><em>Disadvantages (requires an expansion)</em>:<br/>They don’t have real-life experience;<br/>Therefore, we don’t know whether they will faint at the sight of blood;</p>\n<p>They may be trained just on the software (a unique body shape);<br/>But body shapes are different and students might find themselves at odds;</p>\n<p>They do not get an idea of how hectic the theatre can be/space available/interferences among people;<br/>And this training alone will be useless;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong>:</em><br/>It needs sensors to assess the <strong>pressure</strong> of the hand on the instrument;<br/>Its <strong>positioning with respect to the touchscreen</strong> needs to be tracked (for example by an external camera);<br/>Its <strong>inclination</strong> with respect to the touchscreen needs to be tracked;<br/>The <strong>change of tools</strong> held should be quickly reflected in the image that is displayed (affects communication);<br/>The <strong>specific tool</strong> needs to be recognized;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The representation of (abstract) data;<br/>In a way that is understandable by humans;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1 max]</strong> for identifying an advantage of the use of 3D and <strong>[1 max]</strong> for an expansion up to <strong>[3 max]</strong> for discussing the advantages of the use of 3D;</em><br/><em>Award <strong>[1 max]</strong> for identifying a disadvantage of the use of 3D and <strong>[1 max]</strong> for an expansion up to <strong>[3 max]</strong> for discussing the advantages of the use of 3D;</em><br/><em>Award <strong>[1 max]</strong> for a considered conclusion;</em></p>\n<p><em>Note to examiners: To achieve <strong>[6]</strong> a conclusion must be provided.</em><br/><em>If there is no conclusion the maximum mark is 5</em>.</p>\n<p>A 3D simulation will be more realistic;<br/>As depth can be shown;<br/>Patient’s body can be rotated; // allow up to <strong>[2]</strong> for example;</p>\n<p>But, a 3D simulation takes more time to render / more calculations have to be made;<br/>So, may not be displayed in real time;<br/>It is more demanding on memory / may exceed available memory;</p>\n<p>For an application, as serious as medical training, adequate hardware and software should be made available so that 3D simulations can be carried out;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.6",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-3-visualization",
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct the truth table from the following logic circuit.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award <strong>[3 max]</strong></em>.<br/><em>Award <strong>[3]</strong> for all 8 correct rows</em>.<br/><em>Award <strong>[2]</strong> for 7 correct rows</em>.<br/><em>Award <strong>[1]</strong> for 5,6 correct rows</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates demonstrated that they were able to represent a logic circuit as a truth table, but a common error was to provide a truth table with insufficient rows, so not all combinations of input were covered, thereby losing marks.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.6",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A hotel chain has a loyalty scheme in which customers are awarded 1000 points for each day they stay in one of their hotels. With these points, customers can achieve one of three status levels: Gold, Silver or Bronze. The level will determine the extra services to which they are entitled.</p>\n<p>The total number of points collected during the <strong>current</strong> year will determine which of the three status levels they are assigned for the <strong>following</strong> year: For example <strong>only</strong> the points collected in 2018 will determine the status level for 2019.</p>\n<p>Occasionally, new customers receive additional <strong>bonus</strong> points as part of a promotion.</p>\n<p>The <code>Points</code> class keeps details of the points and status levels of each customer.</p>\n<pre><strong>public class</strong> Points<br/>{<br/><strong>  private</strong> String memberId;    // id of the hotel customer<br/><strong>  private int</strong> totalPoints;    // this year's points<br/><strong>  private int</strong> bonusPoints;    // any bonus points given to this year's new member<br/><strong>  private</strong> String statusNow;   // current(this year's)status<br/><strong>  private</strong> String statusNextYear;   // following year's status<br/><strong>  private</strong> Visits[] allVisits = <strong>new</strong> Visits[366];//details of each visit<br/>                                               // during this year<br/><strong>  int</strong> y;                      // number of visits this year<br/><strong><br/>  public</strong> Points(String id) // constructor for new member<br/>  {<br/>    memberId = id;<br/>    bonusPoints = 0;<br/>    y = 0;<br/>    statusNow = \"Bronze\";<br/>  }<br/><br/>  //constructor for new member given bonus points (valid for current year only)<br/><strong>  public</strong> Points(String id, <strong>int</strong> bp)<br/>  {<br/>    memberId = id;<br/>    bonusPoints = bp; // multiples of 1000 - maximum number is 5000<br/>    y = 0;<br/>    statusNow = \"Bronze\";<br/>  }<br/>  <br/>  // all the accessor and mutator methods are present but not shown<br/><strong><br/>  public</strong> Visits getAllVisits(int v)<br/>  {<br/><strong>    return</strong> allVisits[v];<br/>  }<br/><strong><br/>  public void</strong> addVisit(Visits v) // adds a new Visit object to the array<br/>  {<br/>    allVisits[y] = v;<br/>    y = y + 1;<br/>  }<br/><br/>  isGold() {code missing}<br/>  calculateTotalPoints(){code missing}<br/>  daysMissing(){code missing}<br/>}</pre>\n</div><div class=\"specification\">\n<p>The instance variables in the <code>Points</code> class are preceded by the modifier <code>private</code>. The choice of modifier affects the way in which these variables are accessed or used.</p>\n</div><div class=\"specification\">\n<p>The customers will be assigned one of three levels for the following year (Gold, Silver or Bronze) depending upon the current year’s total points as follows.</p>\n<ul>\n<li>Bronze = less than 10 000 points</li>\n<li>Silver = 10 000 or more but less than 50 000</li>\n<li>Gold = 50 000 or more.</li>\n</ul>\n<p>In 2018, Tim became a member for the first time and was awarded a bonus of 1000 points. So far, in 2018, Tim has stayed three times at one of these hotels. The first visit lasted 2 days, the second visit lasted 1 day and the third visit lasted 6 days.</p>\n</div><div class=\"specification\">\n<p>The different <code>Points</code> objects are stored in an array which is declared globally in the main (driver) class as follows: <code>Points[] allPoints = new Points[10000]</code>;</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With the use of <strong>two</strong> examples other than <code>private</code>, outline how the choice of this modifier affects the way in which these variables are accessed or used.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the <strong>two</strong> methods with the same name in the <code>Points</code> class, explain the OOP feature that makes it possible to successfully implement either of these methods.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the status level that Tim has been assigned, for 2019, following these visits.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State how an individual object can be identified using this array.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The attribute <code>statusNow</code> is assigned its correct value at the beginning of every year for existing members. It cannot be changed during the year.</p>\n<p>Construct the method <code>isGold()</code> in the <code>Points</code> class, which will return whether the current status is “Gold”.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><code>public</code>;<br/>Allows access to variables from outside of the class/unlimited access;</p>\n<p><code>protected</code>;<br/>Allows access to variable from within the package (project) in which they are created/subclasses;</p>\n<p><code>final</code>;<br/>Prevents variables from being modified;</p>\n<p><code>static</code>;<br/>Refers to variables that act on the class as a whole (and not on individual objects);</p>\n<p><em><strong>Note</strong><strong>:</strong></em></p>\n<ul>\n<li><em>Accept at most one example pertaining to methods.</em></li>\n<li>\n<em>Do not accept two examples pertaining to the same modifier</em>.</li>\n</ul>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The OOP feature shown in the constructors (accept the 2 signatures) is overloading (accept polymorphism);<br/>The constructor methods have a different number / type of parameters / different parameters;<br/>The method calling the constructor / compiler will determine which of these methods is selected;<br/>By matching up with the parameters;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Silver;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Through the use of the (appropriate) array index / appropriate code description;</p>\n<p><span style=\"text-decoration:underline;\"><em>Example</em></span>:</p>\n<p><code>Individual object = allVisits[individual object’s location]</code></p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre><strong>public</strong> boolean isGold()<br/>{<br/><strong>  return</strong> (statusNow.<strong>equals</strong>(\"Gold\"); // allows =<br/>}</pre>\n<p><em>Award marks as follows:</em><br/>Signature;<br/>Correct comparison (allow use of <code>getStatusNow()</code>);<br/>Return (that matches the signature – allow FT);</p>\n<p><em><strong>Note</strong><strong>:</strong></em></p>\n<ul>\n<li><em>Allow the equivalent use of IF/THEN statements.</em></li>\n<li><em>Do not accept parameters to be passed.</em></li>\n<li>\n<em>Do not allow the use of</em> <em><code>totalPoints</code>.</em>\n</li>\n</ul>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.10",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development",
      "d-2-features-of-oop"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p><em>Google Translate</em> is an algorithm whose function is to translate text from one language to another. One of the resources that it uses is the body of documents produced by the United Nations which are routinely translated by humans into various languages.</p>\n<p>Discuss the reasons why <em>Google Translate</em> takes a probabilistic approach in preference to a cognitive rule-based approach.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award marks as follows up to <strong>[5 max]</strong></em>.<br/><em>Award<strong> [2 max]</strong> for an outline of how the statistical/probabilistic approach works (award </em><em><strong>[1]</strong> for a less complete response)</em>.<br/><em>Award <strong>[2]</strong> for an outline of how the rule-based/cognitive approach works (award <strong>[1]</strong> for a less complete response)</em>.<br/><em>Award <strong>[1]</strong> for a reasonable conclusion that explains why the former approach is chosen</em>.</p>\n<p><em><strong>Example answer</strong></em><br/>The statistical model involves comparing the text to be translated against previously stored sets of text pairs (e.g. English and its French equivalent) and then selecting the most probable translation;<br/>The cognitive model involves establishing all the rules of each of the languages and then breaking down each section of text to establish its grammatical structure and then replacing the text with text that has an equivalent structure from the other language;<br/>The second approach has not proved particularly successful for two reasons. The first is that the process of establishing a clear set of rules is extremely labour intensive.<br/>The second is that the process can be ruined by the problems of context, double meanings and non-standard language;<br/>The first approach was taken because the company has stored an enormous set of validated text in different languages and the increase in processing power which allows this corpus to be searched and processed;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.2.HL.TZ0.9",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline the purpose of the memory address register (MAR) in the central processing unit (CPU).</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>The MAR holds the memory location of data/instructions;<br/>…that need to be accessed (read/write) (fetch/store);</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates mostly achieved at least one mark here, but a common error noted was some candidates wrote that the ‘MAR stored data’ rather than the address of the data.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.7",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>With reference to the bowtie model of the web, consider the following graph. It represents a snapshot of popular sites about a specific topic.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>After two months, the graph has evolved into the one shown below.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>We can interpret the graph as follows:<br/>Each node represents a document and each outgoing arrow represents a hyperlink in the document.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the nodes in the strongly connected core.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the set of OUT nodes.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why the edge between node 2 and node 1 is a tendril.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> reasons why an edge may disappear in the new graph, for example the edge between node 5 and node 4.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State how many hyperlinks are in the document corresponding to node 6.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Power laws are often used to model phenomena occurring in very large networks and predict their evolution. Power laws may be suitably applied in different cases, in the context of the World Wide Web.</p>\n<p>With reference to the relation between the number of documents in the web and the number of hyperlinks in each of them, suggest how power laws may help to explain the shape of the World Wide Web.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for all three correct</em>.<br/>3, 5, 6</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for both correct.</em><br/>4, 8</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>No;<br/>Because 2 reaches the node 3 that is in the SCC;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>There is no longer a <span style=\"text-decoration:underline;\">link</span> between 5 and 4;<br/><em>because</em><br/>5 has deleted its link / 5 no longer finds 4 interesting/relevant;<br/>4 has changed its URL;<br/>4 has removed itself from the internet;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>4</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[5 max]</strong></em>.<br/><em>Award up to <strong>[1]</strong> for showing understanding of what a power law is, <strong>[2]</strong> for </em><em>addressing both ideas of incremental web and preferential connectivity (award </em><em>only <strong>[1]</strong> if one is addressed), and <strong>[2]</strong> for discussing them in context <strong>AND </strong></em><em>mentioning some element of the web graph</em>.</p>\n<p>(<span style=\"text-decoration:underline;\">Incrementality of web</span>): Number of nodes in the web increases (nodes are not deleted) / New nodes (documents) have hyperlinks that connect to existing nodes in the web;</p>\n<p>(<span style=\"text-decoration:underline;\">preferential connectivity</span>): This connectivity happens following some preferential element/affinity/alikeness (e.g. common theme) and over time it induces an aggregation of nodes;</p>\n<p>(<span style=\"text-decoration:underline;\">Incrementality and preferential connectivity</span>): Have the consequence that the <strong>SCC</strong> grows;</p>\n<p>(Nodes are not deleted, but) some of the hyperlinks in documents may be changed/deleted/break over time, this may produce <strong>isolated nodes</strong>;</p>\n<p>(<span style=\"text-decoration:underline;\">Power law – proportionality</span>): Relates two quantities, x and y, when y is proportional to x^c, for some constant c;</p>\n<p>The probability for a <strong>new</strong> document N to be connected to some document M (with a certain out-degree) is proportional/depends on the probability that the hyperlinks in N to connect to M (i.e. the sum of the out-degrees of the nodes reached from N, recursively);</p>\n<p>Therefore, a few nodes receive many in-links (because many nodes link to them directly or indirectly based on preference), whereas nodes that have a few in-links are probably new;</p>\n<p>Niche themes/new themes with new language/obsolete themes (when theme is a preference) typically bypass the SCC, making <strong>tendrils and tubes</strong>;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "17N.2.HL.TZ0.13",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-5-analysing-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The online edition of a newspaper includes a collection of audio podcasts. These podcasts are interviews with artists and scientists and last for up to 10 minutes. They may have been provided by other broadcasters and agencies.</p>\n</div><div class=\"specification\">\n<p>At any time, no more than eight podcasts are available on the online edition of the newspaper.</p>\n</div><div class=\"specification\">\n<p>The podcasts can either be downloaded or streamed.</p>\n</div><div class=\"specification\">\n<p>The podcast can be stored online using either lossy or lossless compression.</p>\n</div><div class=\"specification\">\n<p>A person is streaming one of these podcasts at home. However, he is experiencing buffering problems, where the podcast keeps stopping and starting.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> actions relating to the source of these podcasts that should be taken by the newspaper before publishing them.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> technical reason for restricting the number of podcasts to eight.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> format for an audio file.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare these <strong>two</strong> approaches for a person who downloads or streams these podcasts.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason for this problem.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>These podcasts are normally displayed on the website in the chronological order in which they were added, with the most recent on top. However, users who regularly access the site for science podcasts, may see them presented in a different order, with all the scientific ones on top.</p>\n<p>With reference to the technology involved, explain how this is made possible.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Provide a reference to the source/provider;<br/>Obtain permission from the interviewee;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>To better control access to their servers;<br/>Because it could be cause of bottleneck on the server / to reduce/minimize risks of DDoS;</p>\n<p>To better manage the UI;<br/>So that the offer of podcasts is well visible also from small devices;</p>\n<p>To have a smaller system that is easier to maintain;<br/>That can be maintained more effectively also remotely by one person;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1 max]</strong></em>.<br/>(accept for general sound format:) MP3, FLAC, WAV, WMA;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[5 max]</strong></em>.<br/><em>Award up to <strong>[4 max]</strong> for comparing lossless and lossy</em>.<br/><em>Award <strong>[1]</strong> for a conclusion with reference to the scenario</em>.</p>\n<p>In terms of audio quality for spoken language there is not much difference in using lossy or lossless compression/ lossless compression does not really bring benefit to the spoken audio;</p>\n<p>A minor benefit could be in using lossy compression in relation to transmission while downloading (and also in streaming);</p>\n<p>And it is relevant, in general, in a situation with low bandwidth;</p>\n<p>However, these files are small in size anyway, because their duration is limited to 10 minutes;</p>\n<p>And this suggests that for the specific nature and size of the files (spoken and 10 minutes duration) lossy might be slightly preferable for some users for downloading without compromising on quality of audio.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying a reason why buffering may occur and <strong>[1] </strong>expansion/example up to <strong>[2 max]</strong></em>.</p>\n<p>Too many people are using their own Internet access point at home simultaneously;<br/>Possibly on different devices, including tablets, smartphones on WiFi;</p>\n<p>There is low bandwidth because it is peak time;<br/>Possibly too many people are downloading simultaneously;</p>\n<p>Old router/modem/computer / outdated software may slow performance;<br/>Computer that is not protected by antivirus may have performance affected by malware;<br/>Performance;</p>\n<p>There are too many applications running simultaneously on the computer;<br/>So, memory is insufficient and one should close some applications;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award up to <strong>[2 max]</strong> for the use of dynamic scripting. <br/>Award up to <strong>[2 max]</strong> for the means of tracking a person’s preferences.</em></p>\n<p><strong>Dynamic web pages / scripting</strong><br/>Podcasts are associated to XML objects;<br/>So, that the list of podcasts can be processed by scripts that can re-arrange the order of the XML objects;<br/>To present them in a way that takes into account the previous uses from the user;<br/>By monitoring their preferences, over time;</p>\n<p><strong>Tracking a person’s preferences</strong><br/>For example, counting the clicks and themes to extract regularities / support data analytics;<br/>That can be identified for example by the IP of their device when this is not anonymised;<br/>And also stored in the history of the browser/by use of cookies;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.7",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-4-the-evolving-web",
      "c-1-creating-the-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>State the part of the central processing unit (CPU) that is responsible for carrying out calculations.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Arithmetic and Logic Unit/ALU; </p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A well answered question.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.8",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A company has 600 employees whose names are currently stored using a collection called <code>NAMES</code>. The names are stored as surname, first name. For example: Smith, Jane, Uysal, Rafael, Ahmed, Ishmael, Jonsonn, Sara, …</p>\n</div><div class=\"specification\">\n<p>The names in the collection are kept in a random order. However, it would be more useful if they were kept in alphabetical order.</p>\n</div><div class=\"specification\">\n<p>The company’s staff list is now organized in the arrays in alphabetical order.</p>\n<p>A binary search was used to find a specific name in the array.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a pseudocode algorithm that will store the surnames in one array and first names in another.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a pseudocode algorithm that will sort the surnames into alphabetical order using the <em>bubble</em> <em>sort</em> method. The order of the first names must also be changed so that they keep the same index as their corresponding surname.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the process a binary search would follow to find a record in the surname array.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> benefit of using sub-programmes to implement your algorithms from parts (a) and (b).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>Collection method <code>NAMES.getNext() / NAMES.getData()</code> correctly used;<em><strong><br/></strong></em>Correct loop;<br/>Correct use of index (in both arrays);<br/>Correct assignment in array for surnames;<br/>Correct assignment in array for first names;</p>\n<p><strong>Note</strong>:<br/>Award 1 mark in case that string methods are used to separate ‘name’ and ‘surname’ in the data item.</p>\n<p><em>Example answer 1</em>:</p>\n<pre>NAMES.resetNext() // reset and<br/>SURNAME[600] //initialization of arrays<br/>FIRSTNAME[600] //may not appear in candidates’ responses<br/>COUNTER = 0<br/>loop while NAMES.hasNext()<br/>    SURNAME[COUNTER] = NAMES.getNext()<br/>    FIRSTNAME[COUNTER] = NAMES.getNext()<br/>COUNTER = COUNTER + 1<br/>end loop</pre>\n<p><em>Example answer 2</em></p>\n<pre>NAMES.resetNext()<br/>loop COUNTER from 0 to 599<br/>    SURNAME[COUNTER] = NAMES.getNext()<br/>    FIRSTNAME[COUNTER] = NAMES.getNext()<br/>end loop</pre>\n<p><em>Example answer 3 (assumes that items in the collection are objects- two attributes: surname and first name)</em></p>\n<pre>I = 0<br/>loop while NAMES.hasNext()<br/>        X= NAMES.getData()<br/>        SURNAME[I] = X.surname<br/>        FIRSTNAME[I] = X.firstname<br/>        I = I + 1<br/>end loop</pre>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max] </strong></em><br/>Correct outer loop;<br/>Correct inner loop;<br/>Checking of surname order;<br/>Swapping surnames if necessary;<br/>Swapping corresponding names;<br/>Correct use of flag;</p>\n<p><em>Example 1</em>:</p>\n<pre>loop I from 0 to 599<br/>   loop C from 0 to 598-I //accept 598<br/>       if SURNAME[C] &gt; SURNAME[C + 1] then<br/>         TEMP1 = SURNAME[C]<br/>         TEMP2 = FIRSTNAME[C]<br/>         SURNAME[C] = SURNAME[C + 1]<br/>         FIRSTNAME[C] = FIRSTNAME[C + 1]<br/>         SURNAME[C + 1] = TEMP1<br/>         FIRSTNAME[C + 1] = TEMP2<br/>       end if<br/>    end loop<br/>end loop</pre>\n<p><em>Example 2</em>:</p>\n<pre>FLAG = TRUE<br/>loop while FLAG = TRUE<br/>    FLAG = FALSE<br/>    loop COUNTER from 0 to 598<br/>       if SURNAME[COUNTER] &gt; SURNAME[COUNTER + 1] then<br/>         TEMP1 = SURNAME[COUNTER]<br/>         TEMP2 = FIRSTNAME[COUNTER]<br/>         SURNAME[COUNTER] = SURNAME[COUNTER + 1]<br/>         FIRSTNAME[COUNTER] = FIRSTNAME[COUNTER + 1]<br/>         SURNAME[COUNTER + 1] = TEMP1<br/>         FIRSTNAME[COUNTER + 1] = TEMP2<br/>         FLAG = TRUE<br/>       end if<br/>    end loop<br/>end loop</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p><em><strong>Example 1</strong></em>:<br/>Calculate the index of the middle point in the array SURNAME: (first + last)/2;<br/>Compare surname found with the one stored at middle point;<br/>If greater than the value at the middle point, search the upper half of the array (right side) by calling the binary search method again with a new first index (first = middle + 1);<br/>If smaller than the value at the middle point, search the lower half of the array (left side) by calling the binary search method with a new last (last = middle −1);<br/>if found algorithm terminates;</p>\n<p><em><strong>Example 2</strong></em>:<br/>Find the centre point of the array SURNAME[ ];<br/>Compare surname to be found with the current name in SURNAME[ ];<br/>If correct surname found =&gt; STOP;<br/>Else if surname to be found is greater than the current name in SURNAME[ ]eliminate lower half of array from search and repeat algorithm;<br/>Else if surname to be found is less than the name in SURNAME[ ] eliminate upper half of array from search and repeat algorithm;</p>\n<p><em>Note: Allow a mark for provision for name not found</em>;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p><em>1 mark for a benefit (such as reusability, modularity, maintainability, readability)</em><br/><em>1 mark for an expansion</em>.</p>\n<p>Example answer:<br/>These sub-programs (sorting/searching) could be used in (many) other programs;<br/>Which saves programmer’s time/ effort;</p>\n<p> </p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates who realised that the data from the collection was to be accessed sequentially with the first item stored in one of two arrays, the second item in the second array, the third item in the first array, and so on, until the collection was empty, scored high marks for this question. Some candidates did not correctly apply standard collection methods so lost marks.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates who scored highly in part a. also scored highly for this part. However, this question did see the full range of possible marks awarded. Candidates who were unable to complete the whole algorithm correctly generally achieved some of the marks, typically for performing correct swaps during the sort, or for correctly initialising and implementing one or both of the loops.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question required a description for the binary search algorithm and was generally well answered with a high proportion of candidates achieving full marks. A small number of candidates incorrectly described a sorting algorithm, while others incorrectly described a linear search.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates achieved at least one of the marks for this question, with many acquiring both marks. Candidates most frequently recognised that the sub-programs could be re-used in other programs, therefore saving programming time.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "21M.1.SL.TZ0.13",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-1-general-principles",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Compilers translate source code into object code. Identify <strong>two</strong> other operations performed by a compiler.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Pre-processing;<br/>Lexical analysis;<br/>Parsing;<br/>Semantic analysis;<br/>Syntax analysis;<br/>Linking;<br/>Optimisation;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A good range of the possible correct answers for this question was seen, but the most common correct answer was ‘syntax analysis’. However, a high proportion of the answers for this question were too general, or they repeated the example given in the question.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.9",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The average weekly consumption for a family of four is 2700 litres of water. This includes the water needed for domestic cleaning, personal hygiene, cooking and drinking.</p>\n<p>An online software tool is available to help families calculate how much water they are using for regular activities that require the supply of water. The software tool includes a model which estimates the water usage. A simulation can be run using this model.</p>\n</div><div class=\"specification\">\n<p>The data given below estimates the amount of water (in litres) needed for different household activities:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAVAAAADaCAYAAADwtBrTAAAgAElEQVR4nO3df3RTdZ7/8WfSWWRmQuDL4k6T4ne62G2rSx3W9uCOdJf+wGTYWVZOEZiDlGJ7FL9fQJQFMhTQMwPilHZxq+KP8TQCRb4LY3NgGBdbIJYZ0LWbsGj5jk22w6lrSTpHvnwhzVexY+79/pGbNv1JiW2TMu/HOTml6c29n3tvPq/7+XFDdKqqqgghhLhp+ngXQAghxisJUCGEiJEEqBBCxEgCVAghYvSNgZ7U6XRjXQ4hhEhYg821Dxigqqqi0+kGfZEQIrFJ/R05QzUopQsvhBAxkgAVQogYSYAKIUSMJECFECJGEqBCCBEjCVAhhIiRBKgQQsRIAlQIIWIkASqEEDFKiABVvHasOh06nQ6dLgeb8/LXWFsAr/MQVY/vwf1V5LlPcZSkodOlUeL49CbWNdDrFILeRt6qsvG8O/g1yinE+PaVu4o0nQ5dWlVUXRuA30GJTodO9zgOf2TBgerp+JMAAXqd1jPv0ND9u5tdFb/Eq8S2tq/cP+fvCn/ExpNfjFD5+m7gP3j17/JZvLGJ0OhsQYhb3qjX0zEy4Gfhx5TSxplDZ4Bs1v98BVefWYe94R3OtC4jPX3iCG3kDor2taLuG6vXCSG6mYrYp6rcitUo7i1QpfU9DjX4wWThh0t/xMPF2cAZDp1po38jtAu/20FVSZbW3ddhLnmeE94A8BV+x+P8Sc5Gfgfwu43k/ImOtCo3X/Xtigec2MwDDRdcxmnLCa/X5iTQ93V+ByV/ksPG3wE0sjFnErq0p7G/uiRcHqu9d8tZacFuNY/AsIQQ41ivLvz1YdTTfH7meIvyfDM6nRmrvSWcBUEvTruN/MhwX74Nu9NLz0CaQtDrxG6zdueDTmfFZnfiDcbYpb2BOAdoT/fdVDyPHOM0cqwWTPhpqD3Of/TaaYWgew/Lchaxcf+F7mf9+9djySvHcalr+Js13k/ZjsWAm9r6jwhEng98RH2tG/gh65fci3F4K+N7Sx5nkwloeIczrdd7Shx1cbDmTB1++YT4o9bI5kWLea7RD5iZlToNvfIJjnWLKCzbRWP3YrsoK1zEavuFcIgGXby6ajlluxqi1tXArrLlrHrVxWjMWMQ3QKO678XWezCix5gzj2IT0Pgmh5uuRC3r5XB5JY2YyNvcgC+koobaObXZAv49rHnh3/h20av8wVXJnQB3VuL6g0rrhuwBxikmkpb7AyyAv/YkroACKARcJ6n1A5Yi/uGvpvQvr6mIfX9wUXknQB6Vrk7U1g1kT70Ha7+Wc+TiYMKyYwV5xrg39oVIAN/ANJx6mrcbV2cINfRrtv31FAKNr7HGfgHTir183BlCVUN0fryXFaYL7N96kKaAwleeX/Nqox9Mmzl1TVvGtZs8/DRurOKw9/rARfoa4lirFYL/cZzavi00YySM+rQOf/9bTjf4gX9g7dp8THpAn0LBznpUVcVXUTDMFmOYPu1+llpM4G+g3nUFuIKrvgE/JixL7yftpo7M1J6W86H3aFWA4Ef8svYMkMvS3NR4N/WFGEdMWIrn81cGPei/heFb1/nPc+/jB/z7V3LXpCR0uiQm3bWS/X6667A+eQZzTYD/OQozH6HqrSM0fJJBle9LVPUwpSM2p9IjjvX6Ck2H3ww3x/3PUTg5SRuzuJ3CXW4gunUIX/kucnYkN69P5yHbSkyRoI50301FrLLOuMkDo8c4eyHr80xaN/5zAk1H2N3ox1T6I6xpI3/ihLh1ad327t//L580D3X74VU6rn6BPuXv2XFsP5vyTODfz8bFi1i06IfkmFPJtzlGZRw0fgHaPd44BP8x3jz5KQrwDfMM5gBwhaudI3HjWM9wgb+2noaGemr9YCouYl7KhJtfneEe/qE4l3A3vokP6hvwk03xw39LijQ/hbgJk7h9cnSj45tMSQ4Pqd1Z6eIPqora69HKvqI7gAmYsoupeNeH2unhVF0ddb+oZIXJT+OuNaw97B1gYvrriVPVjhpv7B6viD4gn3FqUzZwAftrp8Jd4u/czVyLCThD7d7f4FcAArTYyzAPNAM+HMZ7WbL+h+DfxeLFu8KBZ73npoYCekTGVf001PyEnbVumTwSYkREhsjgd7tfZn9LAOjC7/xJeEbeXI4zcJ1LjjXhLMj/Cc7OVAqKiigqWsyD82eOXtHUQQzxpxHwmXpqU7YKqKyoU339/h5Sr53arJpAhcVqjecLVVVDaqdrt5oH4df1eljUzafa1ZCqqiFPjWqJ+tudlS71D+p/qXUr7lThTnVF3X/13lL08nm7VVdnKOqvA7wu9LFaYzH1bPvOStX1h8jK2tS60pna30yqpeZjNaQKMfZGt/6G/cFVqd45YH2MPFapdb4/qKqvTl0R/bt6o3qap1a6OntvrLNZrVkxc4BtzFRX1DSrnaqqqp0fqJV5poHLYlqt1rV/GdN+DnUs49MC7e6+m7DMvZvv9Fsgaja+e2ZbjyF7NQdddVSu6LmimFZUUufay46CFPSAPm0+23evwBT+K3cQYqi5t+7JpOiB66HoZzB/+xZWmLTf70iC61rTV38H8x5eoG1bJo+EGMzN1lMMMyndU8epmk3kRZ4zraCyoY49pTMxABhms+FYY+9lMJG3qYZTjTspimVo7gZ0WsL2/4N8q18MArTYn6KgzI7fUoPneCnpkqAiDqT+jpyhjmX8P8p5S/gUR0k+i/b/Tvs9m022f5DwFOIWJ1V8RPw3vpt1h/ZvC5vqDrCtYFpcSySEGH3ShRfiFiT1d+QMdSylBSqEEDGSABVCiBhJgAohRIwkQIUQIkYSoEIIESMJUCGEiJEEqBBCxGjATyLpdLpeP4UQ44/U39E3YICqqio34goxjkn9HTlDXYikCy+EEDGSABVCiBhJgAohRIwkQIUQIkYSoEIIESMJUCGEiJEEqBBCxEgCVAghYiQBKoQQMZIAFUKIGMUvQANObGYdOl3/h7mkirecXoLRyyst2K1mzDYngeGs/2aXH1PX8dqXoDOX4wwo8StGsAWHzaod9yXYvUN+M3eY0oLdmobV3oICKF471iFfq+2r1Y53THb1Mk5bzhhuT3w9CkH38+QPVBe0Otw/I4b5Xh0DcW+Bmjad4pqqonY/rtG4PIljyxex2n6hJ0T1mZTW+/BVFGCMZ4FvGdfxHn6aRbV/QV37l6jqYUrTJ8a7UOKPikKw5V/YXv4mjQP9OejD05xLjeeLqHxQE+q9GvcA7c9I+gOr2fnSXE6U/ZhX3VfjXaBbnJEpkwb8P2WEGD2KH/e+raz+lZklS2cMtAAB10lqSSM1ecKYF2+4EjBAASaQsnANOyzn2H34XLgL3q9LHsDrtGPLjzTxzeTb7Di9fTrsHec4XlWCuXt44HlOeAMM3I1WCDjLMetysDkv96wj4MRm7nlO8btxRK1Tp7NiszvxBnvWE/Q6sXd3j3Xo8m3Y+w5LcIlzx3dTEhnKMJdQdaLv0IUf9z4b+YOtJ+DEZjaTb3uBqpKs8D7eaNhCacFunUFG2S/A/xyFk5Mw207w6UD7HukSj+hwQ99z1/f4AXThdzu692mwY9j7XGRRUvVLznV8OULlFKPjOt69G9jgyedn6/+aSQMu00VHWyv+rDSmGxI0pkjYAAX000idZcZ/vo2OfvVWIeiuYdXys2S80hJu1of+nW1Jhyhc+1avsS///hOcm7EZr6qihto5kPIOllU1uIMTSMv9ARZ/A/WuK9rSV3DVN+DHx/m2yyjatsJXQgvWnKkQbGL3slUcTXoMdyjcpQj5NpBU+xirXnWFK3fQxaurNnE645/oVFVU9Ut8275FbeFWDkeP3fhP8Pa5GWzxhsLLHJjB25b1Pa1u5RMcj1pY4PzvVPi+RFVDdL5yN6eXL2Kd4xN6dtNPY+2HTN18FjX0exofyxl6mEOfSWn9RTw1i8G0mVPXQvgqCpkc04m6WQFa7E+Rt/w0yRVuQqpKyPc0yafXkbGgGndQIXx+97BswVGSVjcQ6nUMo45PsIndy5by4mcP0tgZQlXPsmVGK2/vvzAmeyJiNQHz/Oc5tvMBTIMmUJB2z0VMyZ/ieCRyEc2ipMqB2981loUdUuIGaERzK+3Bvgnahe/D92nMmkNuuhYV+hQKdtaj1peSHrVXpk02thRlYtCWyVu5FEvj+3zo60Kfdj9LLVd7wlK5TNt5WLbiPpoPvUerApFQpXgeOUYINB1ht8dCSdn3u0++3vQ3rCy+l8YTF/ApoPgucKJxBnNz08LbZQKmgmd4t+/YjWkl27YsJN2gDy+Tt5RiSwsnPuxAQSHQ+Bpr7HexY0sZs00TAD2GzGJePLCA42teozGqRWgqfpiHMo2g/zPS70zgUeKAize2NjH/pZ+ybrYJPaA3zeGpF6vZ5Kmk/LAXhSs0HX4TT3EJZdoyAx6fpiPs9iyJOoZG0ovWs21Tdlx3UdyIHoPpz7S6MQjlMm3nr5KRcj8lbzRrY59n2TLDxYZle7QLbfwlfoAOaALm732fvIaXqTnyG5yOxj7dvxu5iKc9CPpUcpfeS4MWlkrrexxqtrBy9d+RFQnuwEfU10Kx9R6M6DEW7MTn28Hsjl/jcDhwON7Cbnsw3B3W6M0zeSDvDFtrfkWT81f9hxWG9DnNHh/BSGvY1HcMSI9hehpZvVrO44XWmvffxZyZ3+n95jOYychC2/dpFFS48FXk0OE8qh3n17EVFlDW8Ln2Au349OviGZieMdCYmhhX9JmU1rfybq9WqpH0efOY3X2hjb/ED9ABx0D0GLLXccxTxX1Xf8X2RflkTEoaZJxxKBPD3fiGdzjT+jnB9lZ+VzyP+3L+hqVZp6l3XUHpaON8pPsOELyAveR7TMpYxosf/B9AzxTrLlw1i3tay4bZbDjWyIH7/i912x+jMGPyION8w6CNUUbfxpGUUUbDza0lQWjjWkMsER6yUQi27KPEPJmMwpf54KoC/BnWVxzUWLSQVS7Tdt43VgUXiaLXhTb+EjdAAx9RX+vGNCuV5AFLqceQnkdRaQXvqiohn4u6H3awtXAZ23tNggwt3I2/hKe9DVf9ae7MMGPQTyN1Fpxv+xTvmXdoLp5HjlFP+Nafn1J2Yi517W28W/EoRUVFFBWkcM1zsfeKDekUFD1Kxbs+1JAPV90DdGwtJG97483dl2qpwRNS+9zGoaKqLioKpt3MmhLABJJT0zANsYRpVirJeDm8bjMn5tfRHqqnovQhiooepMD8OZ5mLX61MXIh4ilBA7SLSycd1PoXs6Ps/mHd96k3ZVP0WAnFpugJoOG8cBqps76ktqaCmtoUluamomcqOda5NNdW8VztJa37DpGBbbLuZaYpqlut/D+uXh5i5ldvIrtoJSXF2YNMig1kKjlWC6bmc1zoNWiu3Xg84jcTa0MDI7jGgbZhzJlHseljzl74fe9zFPThaYasDDOGyL/n3N1rkkHpvErPpTFyfPqOkWvnSIxr4Q9o9L0jBO19Yo6qk/GVgAEawHtiD+VrTvNAzdMsGfCGWe0WpPxyHN3jiwG8J0/SRBGrrDNuYsfCFZGD+znYfc+ZFiaNB9nvmdvTfY9U2oZD7G28pE08+Wmqfpo19p6Z3/DJt2JztGjdDIWg99fUN0HpqkLShlU4Pca8Vbw0/zRryl+nyd+lrecI2zfsgcoNgxyb2IVb4z5q971NS1ABAngdu9m+yz1yGzHm8MiO2Rxf8zTVTf7wMQxewL52HbsyNrJzSTp64z1Yi8001B6iUbt4KP6zVJc/g727/x85PsdYvrZ29Mor4kKfXsTOyuSo9yJAgJa33qRufjlP5CVG7yvuAerfVcjkXh/TymTVSQNFxxp4o3TmIDN1E0lfWY1r7VSO5k3WXjeZvKNTWXtsCwtTbubG20irCEzdXfVImJj6jMHqMeat5djeWbxfOJ0knQ7d9B/z6ztKOFK3qbtrqk9fzl7XKm4/uphJOh06XRKT8o5y+9rX2LHwu8M/6PrvUlRdx4G5/4XNfFvUeg5xcP3swWcxIx+Bu9mPM+rTWfLiG6ynirsmJaHTLaam08K2ny++iZXciJHM0udpPDCXDlt2+BhO+kc8c6vxHFtHtkEPTCPvyZfZO/s9Cs23odPpmP7j97mj5GXqomfYteOzL8tJwaSk8Hvng7tYW7lwBMsr4mMK2etf59iDn/FcemQOYDFv8DD/Wr2QlLgnV5hOHeS7T+VrUce5gBPbsjbKftn7ti7xx0Hq78gZ6lhK1bolKQT/80PO/+VgE3BCiJEg1euWdIWmd8H2ZG5CDLQLcauSLrwQtyCpvyNHuvBCCDEKJECFECJGEqBCCBEjCVAhhIiRBKgQQsRIAlQIIWIkASqEEDEa8NvEdDpdr59CiPFH6u/oGzBAVVWVG3GFGMek/o6coS5E0oUXQogYSYAKIUSMJECFECJGEqBCCBEjCVAhhIiRBKgQQsRIAlQIIWIkASqEEDGSABVCiBjFJ0ADTmxmM1Z7C72/dVch4CzHrNMN+JW84e9bz8HmvDwixRjW+rSvCDbbnAQGX+rWMKx9vY7XvgSduRxn4Ga+M3kEBVtw2KzaV90uwe69fuPXKC3YrWk977m+v/ej7efNfjV0zC7jtOWM4fbirQu/uxZbvlk7j1Zs+5rwR++74se9z0Z+91eeW7HZf4Xb3xW3UvcVnwA13oO12Eyzx0ew1x+66Ghrhbw87m9upT0YfTSv03rmHRpMFqw5U8eurPpMSut9+CoK5AvaAJhIeulhVN9OCozxePtcx3v4aRbV/gV17V+iqocpTZ8Yh3KI2HVxybGev3lB4ZFj7ahqiE7PKti7jJV7Ixe0Li4deZYFe2Gly0dIVQn5nib59GYW/POZhGnMxKkLb2B6xgz8tSdxRbdilDbOHLpE8epScmmg3nUl6jVB2j0XMRXPIycuFVckFiNTJg34XzmIRBc4wwtr/o1FJT8k06AH9BjSF7Jl2xKat+6nMaCAcpH6194hq/gRirNN6AG9aQ7rtjxFVt/ciKM4JdFE0nJ/gMXfSltHT3NcaX2PQ81zsVqsWIuhtv6jnitN4CPqa31kZZgxQLh576iixBxp3pvJt9lxeqOuTUEvTnvfLoATb6+W7Zd0nPslVSVZ2jJZlFTV9yzTt1sbGX54/QRNUd0Lc8nznIjeNgpBb33Pes0lVL31Orb8obrIkW7cz3DUP9+zb/k29rkvEeizvueb/FHdzy78bkfUfoRfZ3d6e7fy+3SL+pcb6DjH8aqS8FBKv2X6dOGHfTxA8Texr7vrPcD5uhGlBbt1BhllvwD/cxROTsJsO8GnznLM/YZitGM5okMNAbxOe+9uZ7/30/DOg+J34+g+xlmUVP2Scx1fjlA5E5lCwHWSWvr2JPUYC3bii/Rsgj48zVOYlTqtV0jpk1OZ1a9xFT9xa8rp0+5nqeUch860aSGgddGz0phumEaO1QLn2+iI5FhHG+f9uSzNTUXPVdy7H2XB0W+y2v0lqqqihv6dbUmHKFxVgzuoAFdxv7qe5afv5pXOEKqqEvJtIKn2MdYe9kYFzwX2v93KjC1ntWV2k/L2ala96uozvBDNT8NjVdRNeoRjqooaaudAyjtYurcNyqUjrMvbQPPc/0WnqqJ6NzL12AvsavTf+OA0vMCLzu+yxRtCDbVz6vvnWZkzncxnW/nbn7lR1Wt8vOMbVC58liOXugCFoHsPyxYcJWl1AyFVRVW/xLftW9QWrudV91XtIH6C41ELOXuTWOu5hqpewzn3AiV55Tgu9VzI/PtPcG7GZryD7Ftsx8PBo9llOJOfxhdSUdUWXsk4y/I+2x6SPpPS+ot4ahaDaTOnroXwVRQyeXiv/poCtNifIm/5aZIr3FFdynVkLKjW9nOY5yHYxO5lS3nxswdp7AyhqmfZMqOVt/dfGJM9ia/wMJ0/68+Z7GvEHrmgmkuoOtFzkQnX98HW4eN82+VBxq7HVvz6wvpppM6aEjUOGqTdcwnL0vtJ0+sx5syjuPkdzrReBxSC7a00W35AbtpECJzj8O4OikuWMts0QVtfCnkrl2JpfJ8PfV2gdPDhiRay5t5HuiG8m3rTA+x8t5X60syoHc9m07b1FKUbtWX+hpXF99J44gK+Ic6QaZONLUWZ4daw3kTOvGxMkW1zmcYXdnJ8/k/YuXJmeBnDTEpfrGaTaRjHxrSSbVsWhssdWTeL2bGlTNtfI+m5c8jy/xsfeALAFZoOv4mnuISy2SZt3yZgyltKsaWFEx92oABK6yles98Wtb9GMh96mGIcvFZ/sfsN2Xvf+hzXr3E87FlPsWXdHEx6wtsureBA8b+x5oXEGdMaVMDFG1ubmP/ST1k3u6dL+dSL1WzyVFJ+2IsyrPOgEGg6wm7Pkp5zjJH0ovVs25Qd110cG+GhOIIH2bDuN6Q+eSzcAPJuZOqBVaxzfIISqe/xLuowxHEwcWq4lRkZzwh8RH1titbCBAxmMrIiLdQruOobYFYqyXrAWECFz0XF7Cs4HQ4cDgcOu43CjDIaIqvXJ/O9BzJp2PoGR5qcOPp2ZW+k3yTWUPQYpqeRxUU87cGe4YY5d2thoTGYycgaToLerGkUVLjwVeTQ4TwaPh6O17EVFlDW8Lm2jNbCZwYZ0w09LzUWUOHz9bmoDETbt2EZ6Hi4MUXOX7dBxsITjtbt9N/FnJnf6X2cDGYystAaAsM5D+H3sj8rjemGXm8OpmfMGLtdirf3bqf4RRsFkQaQ4W4eKvk+x9e8Fh4DHSfiGKBaK5NW2jquh9+gWVoLE0CfSu7Se8NvTOUybedvo9h6jzYTHqDFXoZ5UgaFL37AVYAp83nF9XMs3RV9CtkbDuI5cB9X6ypYVJjBpFjG3eKhX+W6EYVgyz5KzJPJKHyZD64qwJ9hfcVBjYUB7naID/+uQiZ3j0fr0Om+GR7PTHhat3OIJfzn2+hQhnEelMu0nfeNVcETlymN1OQJUU9oF11/K20dX2kX4MQX32nM7lamh/v5hKylK0jrzo3wRFPW1pN8sOjPOdQ8F5s26Kx432JdWRPz69p4vei72lVAIeBsoBmY1b0BI+kFRaQXFFFa0YXf/SvefOEZCvNaOdWyg7yx3dvRo3g5vG4zJ+bX0f56ESmRYxhwYmv2Rx+QODJhqXFy/IYt3UQ0geTUNEy0DrqEaVYqyQzjPOinkTrLDOfHpOAJKNzzNNUOvZQ+OZVZg3bWzP0ml+IlvmWItDL/dz1HDrX1n3FLu5+lWa24f/0hzd2tssj4SN/u1Fd0Xh2qZTkBU/ZCHitZgKnP7P+IG+w+16APT/MwJpFuVtCHp5l+QwZK51V65qW1Ox/6dsW1uwxG9QbuyPE4+9veN0pzFXfV33/NbevHoLWi9ZZMH3P2wu97T15Ejn2GGcOwzoMWIP2GiLSxwVueHkNGDvP7zaRr9Trv+3zPPEFrXF3tN1kUnlzqMwwVR3EOca2VuXszm5vn9r9BXj+N1FkX2bzRQdbS+7XWaeTNfIbavb/RKmQX/qbXKV+zp6ebpX3SJN/m6LnNJOjlZL0bSn+ENW00b76eRt4T5cyvreBZR0s4RIMtOJ6tYNco5GckoBpqD9GofUpD8Z+luvwZ7FHb06dZsW3+U3Zt34PT3wV04W88RG3DvVTuLCJ91N4N2vE4/gzl1We1cxbA69jFho187W2H7+jwUbvvbVqCirbu3Wzf5R6h8gPGHB7ZMZvja56mOnL7WPAC9rXr2JWxkZ1L0tEP6zzoMeat4qX5x1i+tnb0ypvA9CkF/M/1yezaXttzl4b/fWr2vc/8tUX8lUEP+hlYV/2A5q2V7G0JaMucpfrZ52ne9DgPJciHJ+LeCo401Qe+QV67WvdtshtzefJYBbPfL8GcpEOny+bHv55GyZFf9Mxy6zNZufcQa28/St6kpPCY26TFHL19Fcd2/H1P92q09itlIdWN67n96GIm6XTo0p/jYsGjVFpGZxIp78mX2Tv7PQrNt6HT6Zj+4/e5o+Rl6qJndvUpFOzYi2vl52w334ZOdxvm7Z+z0vU667OnjEK5oje9kOrGauZ2/FQ7Z5PJOzqVtdHbjrU1rE9nyYtvsJ4q7pqUhE63mJpOC9t+vngE98BIZunzNB6YS4ctmySdDt2kf8QztxrPsXVkG/QM/zx8l6LqOvZlOSmYlIROl8mqD+5ibeXCESxvItPmJ8rhhfRw3UzK/jmh5a9R3T0kN4EUyxMc2DGN2rsmh5cxP875rJ9w7MnchPlUoE4d5Kv75Fv9RoHSgn1+GR7bUSoKpsW7NIkp4MS2rI2yX5aOYov41if1d+QMdSzlLToqwp+CMZfs07pogOKnqfo5tnY9xJLZY/hZ/nFFIfifH3L+L/ve7iREYpK36aiYRt6Tr/FSdxdNhy7Jwp7Qgxw7uFrr7on+rtD0LtgSqIsmxFCkCy/ELUjq78iRLrwQQowCCVAhhIiRBKgQQsRIAlQIIWIkASqEEDGSABVCiBhJgAohRIwkQIUQIkYD/n+gOp2u108hxPgj9Xf0DRigqqrKJxmEGMek/o6coS5E0oUXQogYSYAKIUSMJECFECJGEqBCCBEjCVAhhIiRBKgQQsRIAlQIIWIkASqEEDGSABVCiBjFLUAVrx2rThf+wrVeDzP5NjtOb+DmVxr0cqLqWfZ5r2sbCX/PuNnmJIa1CSFGXPgba/vX+8gjB5vzcnhRxY97n4387r9Zsdl/hdvfFd9diBLnFqgJS83HhFQVNfIIualIPs3yvHIcl27mQCkEmvZSsvFDQqNWXiHE1zONggpXT32PPDo/oDLvTvIqX2ZbwTSgi0tHnmXBXljp8hFSVUK+p0k+vZkF/3wmYRpEideF15uYXVZCMQ5eq7+IEu/yCCFG2VXcr/6Ujaym6vEcDADKRepfe4es4kcozjahB/SmOazb8hRZtSdxBRIjGRIvQLuZmZU6raeAih+3o4oS80BdfYWAcyuZhc/h5xeUZXyzd7e94xzHq0owaz8MJ1IAABABSURBVF0Bc8nznIhliEAIMcIUgu432LCxg03bisk2aDU+6MPTPKV3BgD65FRm0UC960pcSttX4gWo4qepxsHljdU8mTdNe/Iq7t2PsuDoN1nt/lLr6v8725IOUbiqBncQjAU7aDm1GROLqfF8ga+iAKP2av/+E5ybsRmvqqKG2jmQ8g6WVTW4g4lxFRPij5byKQ0v2/GUlvNEd30HpaON8/7BXuTjfNvlhOidxjlA/TSU3UVS9CBykpn71ju42PF7Oj/XDlHgHId3d1BcspTZpgnh5/Qp5K1ciqXxfT70DT1WatpkY0tRZrhrcBOvE0KMLqX1FK/Zb6P44b8lpae7SbC9leZ4FmyYEm8SSb2Gp24l7FrDqlddBAGMBVT4XFTMvoLT4cDhcOCw2yjMKKMh5m1fxNMeHLE9EULcrOu0nnmHhryHWTJ7arwLE5PE68JjJH3hCoot0Lj7CE0BBQjQYi/DPCmDwhc/4CrAlPm84vo5FglCIcYnpY0zh85hKZ7PXxl6jXRimJ5GVtwKNnwD/o/0caefRuosM5E2vOJ9i3VlTcyva+P1ou9qqa8QcDbQDMyKX0mFEDFSWt/jUMMUZtmm9WvJ6ZNTmWUa7JXmfpNL8ZIIZehPuUzbeR+m4nnkGNHGQ+5izszvRBX4Kzqvyky6EOOT1n03WbDmDNB9N5jJyLrab7IoPLk0g4zphjEr6VASMEADeI/sp7bhXtYvuRcjeow58yg2naF272/wKwBd+Jtep3zNHnom6qKb/QqfBz9PiFk6IcRAgrR7LkJWGtMNA8SQfgbWVT+geWsle1vCDSXFf5bqZ5+nedPjPJQ+cYzLO7DEm4XXZbLqgz/nSdfrrM+eEl7MmMuTxyqY/X4J5iQdOl02P/71NEqO/IJNUc18fZoV2+ZrlGV8m28v+hdaJUGFGKcmkGJ5ggM7plF712R0Oh1J5sc5n/UTjj2Z232LYrzp1EG+uk++1U+I8Uvq78gZ6lgmYBdeCCHGBwlQIYSIkQSoEELESAJUCCFiJAEqhBAxkgAVQogYSYAKIUSMJECFECJGEqBCCBEjCVAhhIjRgP+dnU6n6/VTCDH+SP0dfQMGqKqq8llaIcYxqb8jZ6gLkXThhRAiRhKgQggRIwlQIYSIkQSoEELESAJUCCFiJAEqhBAxkgAVQogYSYAKIUSMJECFECJGEqBCCBGjOAdoAK/zEFUlWVHfC2/FZnfg9AZ6FlNasFvNmG1OAoOvTAgxXgS9nKgqwazVe3PJ85zw9qndWr3vyYbIYwl27/X4lLuPOAZoF5cc5eQt/1eSVjcQUlVUVUXtfJF5V46yPO8p7C0Sl0LccpRPcKxbxs7PHqSxM4SqXqPxwc/YmbGMKvfVnuWCPjzNudR4vghnQ/fjMKXpE+NX/ijxC9DAGV5Y4yBrx2bWzTb1FMSQzgPrN7Mj6zhb33BJi1OIW4pCoPE11hy3sG3LQtINesBI+sIVFFvOsfvwOa3OKwRcJ6kljdTkCfEt8hDiFqBKRxvn/YP8UZ9Jab0PX0UBxujnO85xfMhmv0LQ68Rus2pNfTP5Nnv3cIDitWPV5WBzXu55ScCJzazrMzxwGactJ+q5LvzuWmz55qhhBifeoNJreV2+jdcj5TOX4wwoCCGi6TEW7MTn20mBcaj46aKjrRV/VhrTDYk7VRO3kunTCllV+qc0lC3mkaqDOJxegjd4jX//Cc7N2IxXVVFD7RxIeQfLqhrcQQVQCLbUsjpvHaeTn8YXUlFDbiqST7Nc6xro0+5nqcVHbf1Hva9yfvCfb6MjkneBj6ivhWLrPRjp4pJjPdkLTpJc4Q4PNXT+Exmn15G37giXojOy8V85M3UjXvVLfI2rmD3kG0QIAYDip6n6ObY2F/HSE7laoylIu+cipuRPcTwSmSPJoqTKgdvfFecCR1EHMcSfRk6nR22oXKGaQCXyyNuk1tS9q3o6Qz3LhT5Waywm1bTplHot6uUhT41qYbFa4/lCVdXP1FObslVTaZ3aHoreSPh5LDWqJ/SF6qlZrP1bVVU1/Ltp2Qp1mSmynpB67dRm1WTarJ66FlLVa6fUTSaTaqn5WO212mun1E2mbHXTqc96thF5jRBxNib192vT6iOoMFNdsfuM6otUn9DHao3lTjVvc0PPc+o11VO3Wc3L2626Oseung11LOPbRDKk88CGffhCPlxH6qj7RSUrPLsoW5RPRvqjw5xEuoinPai1Gn1kzbkbU6+9MjA9YwY0t9IenEBa7g+wNLzDmdbroLRx5tAlilc+QmHWpfB6uIKrvgGK55FjRGuhmpmVOq13c91gJiMrujUrhLg5E0kvPYyqqoR8u0n55WKyH3WEe3X6TErrW3l35wNR9dlI+rx5zPZUUn7YSyIMkCVGH1NvIvvBIooe2sA+X4hOTx2bMo5Ttu4tvMM8SkOOqQL4W2nr6NK68ec4dKYNJejD87u5WO+bTe7SlHAYKpdpO3+b1n2PcLOr8Pbet1Ik3UVZw1AbFEIMl95UyI+3rQT7v1DfOsQtSgYzGVnQ7PHdcMhvLMQpQK/jtS8ZZKJFjyH97ygrzoVIS3EY9MmpzDINsYBJm83Tp5K79F6aPZ9yyXWS2jvTmG6YSHJqGpxvw+d9j0PNc7HmTI168eIBbqUIP/pNdAkhvgatRzlOxClAJ4a70v4G6l1XBl/M8gNy04Z5v5fxHqzFZprP/hZ/r0wOD0bTPZs3IRyWtS+ztaaBrKX3k6bXY8yZR3FzLc89V0tz8TxyjHpAe970MWcv/L53lyHYRFV+GlZ7S0J0JYQYH4ZqPAEmC9acqQPfMQPavaHmPj3E+InfLHz6Q1TXfI/a5f9IlcPdE3qKH/e+Z1hV9jmVO4tIH3YJpzL7kbU8cPwZyqvPausL0GK3sXxXctS6tFDkCPsP0jO2aTCTkeVh//7O3ifHmMsTL83l+JqnqW7yh8My2IJj+zY2spqdS9ITZBxEiPFgIulLNlCZ0cC+t37b3Q1X/Kf42fYGHtixjNlGPfr0InZWJlO7721aum8XDNDy1pvUzS/nibxp8dqBXuJY941klr6C+9hipn5QjjkpMrZo4YXP7mWb5yAbsqfcxPr0GDKL2dNYzdyOn2rry+R/eOZwoO+6jPdgLc7uvtqFX55K7tJcYAYZ0w1R651AStFOGg/MpcOWTZJOh27SYo7evgrXwdVkJ/A9akIkJMNs1h98jQevVJKuzSkkLTtJ2raD7CmdSbj2TSF7/esce/AznktP0uYeFvMGD/Ov1QtJSZBqp9Om6fv/Qb4WVYhxS+rvyBnqWCZIjgshxPgjASqEEDGSABVCiBhJgAohRIwkQIUQIkYSoEIIESMJUCGEiJEEqBBCxEgCVAghYiQBKoQQMfrGQE/qdLpeP4UQ44/U39E3YICqqiqfpRViHJP6O3KGuhBJF14IIWIkASqEEDGSABVCiBhJgAohRIwkQIUQIkYSoEIIESMJUCGEiJEEqBBCxEgCVAghYhSfAA04sZm1rzEe4mG2OQncYFWK145Vl4PNeRm4jte+BJ3Vjle5wQujBb2cqHqWfd7rQ2/Lf4LyfHO4fNYXcLy+BJ25HGfgZjZ2A0oLdqt5WPsuxK0tgNdpxxapc+YSqk54u79LPhHEJ0CNBVT4VFRVe1w7xSYTmDad4pra87yvogDjDValTy+lXnVRUTAtxsIoBJr2UrLxQ0JDLneZxn/ezN4ZL9EeUlHrH2NmUoybFELcQBeXHOXkbW/lvtdawpnQvpN7313PgqqmhAlR6cIPl3KZtvM+mDaFSXLUhBhdgTO8sMZBVvEKFqZrzSh9Cnkri5iwsYrDN+gtjpUEjwKFoNeJ3WbVuvVm8m12nN6ezm3vLvxAuvC7a3u6ATorNrsTb1ABFALOrWQWPoefX1CW8c0Bu86K14416S7KGvz4dxUyWZeDzflZ74W0YYner1cIOMsxR5cv6MVpt5HfPVQRXZ4oHec4XlWCOTKcUfI8J7zSqRd/HJSONs77zcxKndYrpPTJqcwyneHQmTZGcOAsZgkcoArBllpW563jdPLT+EIqashNRfJplmcso8p9dRjr6OKSYz3ZC06SXOEmpKqonf9Exul15K07wiVFj7FgBy2nNmNiMTWeLwYcNtCnl1If+pgai0kbZnBRUXB7DPt0Ffer61l++m5e6Qyhqioh3waSah9j7WFvrzeEf/8Jzs3YjFdVUUPtHEh5B8uqGtx9g1aIPzp+mj2+hOjGJ3CAXqHpjRc5Mf8n7Fw3B5Me0JuY/dQ/cWBTBxvLHTeeKIp0A3ZsZt1sU3hnDTMpfbGa4uM7eaFxsFbrKFE6+PBEC1lz7yPdED70etMD7Hy3lfrSzF4nw7TJxpaiTAygdV2WYml8nw99XWNbZiHiINzS7P98uGU69uUZTOIGaOAj6mt9ZM25Oxye3QxMz5gBza20D9kaUwi4TlI7QDcAg5mMLB+19R+N7Uy3PpnvPZBJw9Y3ONLkxOG82RnFi3jaE+G6K8QoM+byxEt/Te32PTj94UaD4j9LdYWDrvsHSNY4SdgAveGVxt9KW8dwWmNudhXe3vsWKW08c+xNIXvDQTwH7uNqXQWLCjOYNMC4rhBiAilFO2ksN7Iv+zZ0OjOF/3yRv/3psxQbvkVWhjncO4uzhA3QwZrw3UxppCZPGMaawmObatTtUTdzm9TIM5JeUERpRT2q+iU+10v8sON5CvN+NrL3kwox7hlJf+Ap9vlUVNXHuxXFZE/6P3iap/TvVcZJIpRhYMZ7sBabaT77W/y9ciVIu+ciZKUx3TBU8fUYc+ZRbPqYsxd+33vGLthEVX4aVnvLyM3kGcxkZN1s12ICpuyFPFayANOwW9RC/BFQWrBb5/S7u0bpaOM8Fqw5U+NUsN4SN0CZyuxH1vLA8Wcorz6rhWiAFruN5buSqdxZRPqNSm/M5YmX5nJ8zdNUN/nDYRlswbF9GxtZzc4l6ejRY5ieRhYACp8HP48tVPWp5C7NxV/7Jm+1BAjfgnWEZ7fvpXuwQGnBbk0j3+bouW0p6OVkvRtKf4Q1bWIsWxbi1qNPJXdpCrt6jYE2UVvzDtNfWkWeMTGiKzFKMSA9hsxi9jRWM7fjp5iTdOh0mfwPzxwOeA6yIXvKMNahjaMcmEuHLZsknQ7dpMUcvX0VroOryY7MhKdZsW2+RlnGt/n2on+hNaYEnUj6kh00rP+KrXdNRqebzoKa/8eibVuwdO9SJiv3HmLt7UfJm5QUHo/VynNsx9+TksBnQ4ixNZH0ldW4Vn7OdvNt6HQ6kpbVwZJqXi/6bsIEl04d5Kv75Fv9hBi/pP6OnKGOZaIEuRBCjDsSoEIIESMJUCGEiJEEqBBCxEgCVAghYiQBKoQQMZIAFUKIGEmACiFEjCRAhRAiRhKgQggRo28M9KROp+v1Uwgx/kj9HX0DBqh8hlYIIW5MuvBCCBEjCVAhhIiRBKgQQsRIAlQIIWL0/wF8yKm4BGfgigAAAABJRU5ErkJggg==\"/></p>\n</div><div class=\"specification\">\n<p>By using the data in the table, a family uses a spreadsheet to run some simulations of how to distribute the use of water.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the scenario described above, explain the difference between a model and a simulation.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the choice of data in the table may affect the quality of the simulation.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how a simulation in a spreadsheet can be organized so that the family can ensure a consumption of no more than 2700 litres per week.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The same spreadsheet is used by a group of four young people who share a house. The simulation, however, does not reflect their actual situation and the weekly target is often exceeded.</p>\n<p>Identify <strong>two</strong> reasons why the simulation may not reflect the actual situation in this case.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The model is a static description of some rules;<br/>In this case a list of appliances and their water consumption (min or max);<br/>Whereas the simulation is the use of that model;<br/>With input from real parameters (such as those that families may input);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>There may be missing data:<br/>For example, watering plants (allow any suitable example);<br/>The consequences, e.g. the totals will be underestimated;<br/>The data may be inaccurate;<br/>For example, the shower times may exceed the stated;<br/>The consequence, e.g. the results of the simulation may not be valid;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The answer shows the use of columns and rows;<br/>The data from part (b) is included together with the addition of a <code>TimesPerWeek</code> variable;<br/>Weekly total formula for each variable is included;<br/>Overall weekly total has correct formula;<br/><code>TimesPerWeek</code> is adjusted until target is met;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>Individual and individualistic use</em>:<br/>They don’t know each other, and they do their laundry separately, not full load;<br/>They don’t cook together and consume more water consequently;<br/>Somebody washes the car every week without telling the others;</p>\n<p><em>Special occasions</em>:<br/>Some of them may be athletes and need to wash the sport gear separately;<br/>Somebody invites friends over night/for dinner and the water consumption increases;</p>\n<p><em>Technical reasons</em>:<br/>There are water leaks in the house and they are not aware about them;<br/>The counter was manipulated by the landlord years ago;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.5",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> ways that user documentation may be provided.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>On a CD/DVD/Blu-ray disc;<br/>Through embedded help files;<br/>Online/website;<br/>Physical/printed media;</p>\n<p><em>Note to examiners: accept answers relating to methods of supplying or media used to present user documentation. Do not accept answers related to training</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were able to name at least one and often two methods for the provision of user documentation.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.10",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The details of hotel stays during the current year are stored in the variable <code>allVisits</code> which is an array of the Visits class. <code>allVisits</code> is used in determining the total points awarded in the current year.</p>\n<p>The <code>Visits</code> class is outlined below:</p>\n<pre><strong>public class</strong> Visits<br/>{<br/><strong>  private</strong> String hotelCode;   // id of the hotel<br/><strong>  private</strong> <strong>int</strong> days;           // number of days of the visit<br/><strong><br/>  public</strong> Visits(String h, <strong>int</strong> d)<br/>  {<br/>    hotelCode = h;<br/>    days = d;<br/>  }<br/><strong>  <br/>  public int</strong> getDays()<br/>  {<br/><strong>    return</strong> days;<br/>  }<br/>}</pre>\n</div><div class=\"specification\">\n<p>The main (driver) class manages the <code>Points</code> and <code>Visits</code> classes. It contains the following code:</p>\n<pre>Points[] allPoints = <strong>new</strong> Points[10000]; // declared globally<br/><br/>allPoints[0] = <strong>new</strong> Points(\"m100\");<br/>allPoints[1] = <strong>new</strong> Points(\"m101\",5000);<br/>allPoints[2] = <strong>new</strong> Points(\"m102\",2000);<br/><br/>Visits v1 = <strong>new</strong> Visits(\"h003\", 3);<br/>Visits v2 = <strong>new</strong> Visits(\"h013\", 1);<br/>Visits v3 = <strong>new</strong> Visits(\"h013\", 2);<br/>Visits v4 = <strong>new</strong> Visits(\"h005\", 6);<br/><br/>allPoints[0].addVisit(v1);<br/>allPoints[0].addVisit(v2);<br/>allPoints[0].addVisit(v3);<br/>allPoints[0].addVisit(v4);<br/>allPoints[1].addVisit(v1);<br/>allPoints[1].addVisit(<strong>new</strong> Visits(\"h004\",6));</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a UML diagram for the <code>Visits</code> class.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the output given by the following statement:</p>\n<p><code>System.out.println(allPoints[2].getMemberId())</code>;</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the output given by the following statement:</p>\n<p><code>System.out.println(allPoints[0].getBonusPoints())</code>;</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the output given by the following statement:</p>\n<p><code>System.out.println(allPoints[1].getAllVisits(1).getDays())</code>;</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>calculateTotalPoints()</code>, in the <code>Points</code> class, which will calculate and return the total number of points awarded so far in the current year.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for three compartments, <strong>[1]</strong> for correct + and −, and <strong>[1]</strong> for correct contents</em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em><strong>Note</strong><strong>:</strong></em></p>\n<ul>\n<li><em>Allow variations in the format, but must use + / −.</em></li>\n<li>\n<em>Accept additional getters/setters, but the given content must be present</em>.</li>\n</ul>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>m102;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>0;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>6;</p>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><span style=\"text-decoration:underline;\"><em>Example 1</em><em>:</em></span></p>\n<pre><strong>public int</strong> calculateTotalPoints()<br/>{<br/><strong>  int</strong> totalPoints = 0;<br/><strong>  for</strong> (<strong>int</strong> x = 0; x &lt; y; x++)<br/>  {<br/>    totalPoints = totalPoints + allVisits[x].getDays();<br/>  }<br/>  totalPoints = totalPoints * 1000 + bonusPoints;<br/><strong>  return</strong> totalPoints;<br/>}</pre>\n<p><em>Award marks for <strong>correctly</strong> including the following</em>:<br/>Signature + matching return;<br/>Loop through the number of visits (y); // <em>do not allow length statements;</em><br/>Any use of <code>allVisits</code> array;<br/>Correct update of <code>totalPoints</code> (with or without bonusPoints);<br/>Inclusion of bonus points <span style=\"text-decoration:underline;\">outside</span> of the loop (or if the loop is absent);</p>\n<p><span style=\"text-decoration:underline;\"><em>Example 2</em><em>:</em></span></p>\n<pre><strong>public int</strong> calculateTotalPoints()<br/>{<br/><strong>  int</strong> totalDays = 0;<br/><strong>    for</strong> (<strong>int</strong> x = 0; x &lt; y; x++)<br/>    {<br/>      totalDays = totalDays + allVisits[x].getDays();<br/>    }<br/>    totalPoints = totalDays * 1000 + bonusPoints;<br/><strong>    return</strong> totalPoints;<br/>}</pre>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.11",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-1-objects-as-a-programming-concept",
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A manufacturing company that produces several products is using spreadsheet software to model its finances. This includes calculations that will estimate different quantities including the profit that the company will make in future years.</p>\n<p>The model involves the use of spreadsheet software which will be organized using different sheets for different areas of the company’s finances. Previously less sophisticated methods were used to keep track of costs and sales.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>By including examples where appropriate, describe a basic structure for this model.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest how the reliability of the model could be tested.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The company has established certain profit targets that it wishes to achieve over the next three years.</p>\n<p>Explain how this model can be used to investigate different strategies that will reach these targets.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong></em>.<br/>Different sheets will be dedicated to different categories, e.g. salaries, running<br/>expenses, sales, different products, different months etc.;<br/><em><strong>Note</strong>: Award <strong>[2 max]</strong> for identifying at least two categories</em>.</p>\n<p>Each sheet will contain a list of items for that category including associated values;<br/>Formulas will be included as necessary;<br/>Each sheet/category will include a formula that totals the values in that category;<br/>Intermediate values will be calculated, e.g. tax to be paid;<br/>The final profit will be determined from the previous totals;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Figures from previous years can be entered into the model;<br/>With the results checked against the previously calculated results;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Different (“what-if”) scenarios can be run;<br/>In which the values of different variables are changed;<br/>For example, the number of items sold / the increase in the level of salaries;<br/><em><strong>Note</strong>: Award <strong>[2 max]</strong> for identifying at least two items</em>.</p>\n<p>Selling prices/other (acceptable) parameters can be adjusted to achieve the desired profit;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.5",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-1-the-basic-model"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline <strong>one</strong> reason why protocols are used in communications between computers.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>To provide a set of rules/procedures;<br/>To enable two or more different electronic devices/computers/entities to understand each other during data transfer / enable successful communication;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were generally able to state that protocols are needed to provide rules and many candidates went on to give an appropriate expansion for the second mark.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.11",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> characteristics of a personal area network (PAN).</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Smallest type of network;<br/>Consists of connected devices in close proximity to the individual using them;<br/>Connected via Bluetooth/wireless;<br/><em>Suitable example</em>: smartphone to car connection;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates generally recognised that a PAN made use of Bluetooth or wireless, or that it covered a very small area. Unfortunately, answers with more than one marking point being covered were rare.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.12",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain how data is transmitted by packet switching. </p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>A message/the data is broken into a number of parts;<br/>Which are sent independently;<br/>…over the optimum route for each packet;<br/>The individual parts are reassembled at the destination;<br/>Each packet contains the (IP) address of both the sender and recipient; </p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates demonstrated that they understood what packet switching was but didn’t always provide sufficient distinct information to achieve full marks. However, the vast majority of candidates achieved at least two marks.</p>\n</div>",
    "question_id": "19M.1.SL.TZ0.13",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A large mail order company is concerned about the security of its stored data.</p>\n</div><div class=\"specification\">\n<p>The company decides to improve its service by introducing a new user interface for its customers and has developed this interface to the point that it needs to be tested by users who are outside of the company.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> possible causes of data loss.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> backup strategies that may be used to limit data loss.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why beta testing is used to gather feedback for the new user interface.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> consequence of not involving end-users in the design and testing stages.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> features that could be used to improve the accessibility of the new user interface.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Malicious activities;<br/>An unauthorized user gaining access to data and deleting/altering it;</p>\n<p>Natural disasters / earthquake / storm / power loss;<br/>Causing the system to crash and destroy data;</p>\n<p>Malware/viruses/spyware/worms;<br/>Which infiltrate and damage the data;</p>\n<p>Human error;<br/>Accidental deletion/overwriting of files;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Copies of backup could be kept off-site/cloud backup;<br/>Unlikely that the other site would be affected by the natural disaster/can be reloaded/reinstalled if needed;</p>\n<p>Incremental backup only backs up data that has changed;<br/>Therefore, requiring less storage capacity / can be completed more quickly than a complete backup;</p>\n<p>Failover system/mirrored system/disk mirroring;<br/>A duplicate copy to be used in the event the main system fails;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Testers outside the organization use the operating system in a “real world” setting;<br/>Enables feedback to be given to the developers;<br/>So that the software can be improved/corrected/debugged;<br/>Before it is finally released;<br/>“Real world” testers may find more bugs as the system is used in ways not originally intended / tested;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>The software may not work as expected / may not be better than the existing software / may not meet user requirements / expectations;<br/>The software may be missing some key features;<br/>The software may not be user friendly;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Touch screens;<br/>Voice recognition;<br/>Text-to-speech;<br/>Braille keyboard<br/>A colour-blind option<br/>Large font option; </p>\n<p><em>Note to examiners: allow other correct accessibility features</em></p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were usually able to name two causes of data loss, however, their expansions were not always as specific or relevant as they could be, to achieve full marks. For example, the question was about data loss and the minimisation of this through the use of backup. Therefore, responses involving data being stolen would not be appropriate.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were able to name and correctly expand at least one appropriate backup strategy, however, candidates should be careful in this type of question that their responses are appropriate to the scenario and that both methods quoted are different, for example, an organisation in which data was constantly changing, such as the case here, would not be relying on hard copy as a method of backup. In addition, storing backup data offsite, or storing data on a cloud-based system are really the same marking point.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally demonstrated their understanding of what beta testing is and where it fits in the development process, they should, however, be careful to not give generic responses and make sure that their answers match the scenario. Some candidates lost out by simply restating aspects given in the question.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally recognised that not involving end-users in design and testing may mean that the final product did not meet user requirements, but not many were able to expand on this sufficiently to gain the extra mark by saying, for example, it may have missing features.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>There are many correct possible answers to this question, including touch screens, braille keyboards and text-to-speech systems. A surprisingly large number of candidates failed to recognise that this was being asked, and gave incorrect answers, such as GUI, or left it blank.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19M.1.SL.TZ0.14",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations",
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>BesTea</em> is a company that sells tea and tea-related accessories online. Users browsing the website encounter the following URL:</p>\n<p style=\"text-align: center;\">https://www.BesTea.com/customer/pages/delivery</p>\n</div><div class=\"specification\">\n<p>A person enters the URL for <em>BesTea</em> into their browser.</p>\n</div><div class=\"specification\">\n<p>The logos of the credit and debit cards accepted by <em>BesTea</em> are displayed as images in the footer of all of its web pages.</p>\n</div><div class=\"specification\">\n<p>The <em>BesTea</em> website includes a shopping basket facility that enables users to make purchases.</p>\n<p>The following fragment of PHP code is present in the script that allows users to place a completed order:</p>\n<pre>&lt;?php<br/>  …<br/>$_SESSION['sessionUserID'] = new_random_number();<br/>$query = $db -&gt; query (\"SELECT * FROM users_db WHERE id = \" <br/>.$_SESSION['sessionUserID']);<br/>$userRecord = $query -&gt; fetch_record();<br/>…<br/>&gt;</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how this URL provides security in communication over the Internet.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the steps that the Domain Name System (DNS) server will take in order to locate the correct IP address for this request from the browser.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the footer of a web page can be made identical across all pages of a website.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest the function of this fragment of code.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Users may perform several actions on their shopping baskets. These actions require interaction with a central database of products. Each record in this database includes the identifier, the name and the price of one of these products.</p>\n<p>Consider the following fragment of code:</p>\n<pre>&lt;<br/>$basket = new Basket<br/>  …<br/><br/>If $_REQUEST['action'] == 'removeBasketItem' &amp;&amp; !empty($_REQUEST['id']){<br/>remove($basket, $_REQUEST['id']);<br/>header(\"view_basket.php\");<br/>}<br/>&gt;</pre>\n<p>From the code, identify the information that the user has provided.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>HTTPS authenticates the identity of the website;<br/>And encrypts data that are transmitted between web browser and web server/between two parties;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for step that the Domain Name System (DNS) server will take in order to locate the correct IP address for this request from the browser up to <strong>[4 max]</strong></em>.</p>\n<p>The DNS server configured to the browser’s computer / the ISP (Internet Service Provider) checks through its own database to see if the (domain) name is there;<br/>If it is, it will return the <span style=\"text-decoration:underline;\">corresponding</span> IP address to the browser;<br/>If it isn’t the request is passed onto the next DNS server (in the hierarchy);<br/>This continues until the (domain) name is found;<br/>Or the top level / authoritative DNS server if reached;<br/>When IP address is found, it is sent back to the original DNS server;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>The required layout style for the footer is saves as a CSS file;<br/>Each time the footer section/element appears it calls this CCS file;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each comment that indicates the function of the code up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> realising the modelling centred on a session (intuition of what <code>$_SESSION[]</code> is)</em><br/><em>Award <strong>[1]</strong> realising that session user identifier shall be unique. </em><br/><em>Award <strong>[1]</strong> realising the existence of users_db.</em><br/><em>Award <strong>[1]</strong> realising that this database is interrogated with this session identifier to retrieve all fields of the record;</em><br/><em>Award <strong>[1]</strong> to instantiate a variable <code>$userRecord</code>.</em></p>\n<p>The shopping basket is modelled centred on the “session”;<br/>A global array <code>$_SESSION</code> exists, holding a session identifier associated with any user;<br/>And a new <code>sessionUserID</code> is added to the array and instantiated by a unique value for identifier;<br/>That is generated by the function new <code>random_number()</code>;<br/>This element of the array <code>$_SESSION[]</code> is used to retrieve in the user db (users database) the entire record for the user;<br/>Relative to the user and specific to that particular session;<br/>And used to instantiate the variable <code>$userRecord</code> for further processing;<br/>For example the final billing or confirmation of payment/dispatch.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre><em>Award up to <strong>[2 max]</strong></em>.<br/>The <span style=\"text-decoration:underline;\">identifier</span> “id” that is passed in the variable $_REQUEST['id'];<br/>And the <span style=\"text-decoration:underline;\">specific action</span> that uses the value “<code>removeBasketItem</code>”;</pre>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.8",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An airport uses an object-oriented program to keep track of arrivals and departures of planes. There are many objects in this system and some are listed below.</p>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAt8AAACzCAYAAABRo3DMAAAgAElEQVR4nOy9e1iU173o/5nB2rRF4rG2dUbb+hgOmFRSEyhpq60IBuJuTTyYaJuIpPBLbLcaGzcyBTW9aEwQamo0F+NhomJsvTCPxuYYIEywW9PKnrFGPBXmsLNNgzPsHevGgabxMrN+f8w7wzv3GRjwkvV5Hp5EeN91+a7v+q7vu9Z3raURQggkEolEIpFIJBLJkKO91gWQSCQSiUQikUg+KUjnWyKRSCQSiUQiGSak8y2RSCQSiUQikQwT0vmWSCQSiUQikUiGCel8SyQSiUQikUgkw4R0viUSiUQikUgkkmFCOt8SiUQikUgkEskwMSLULzUazXCXQyKRSCQSiUQiuSmIdI1OSOdbCIFGo4n4okRyIyL1WiKRSCQSyVASbRJbhp1IJBKJRCKRSCTDhHS+JRKJRCKRSCSSYUI63xKJRCKRSCQSyTAhnW+JRCKRSCQSiWSYkM63RCKRSCQSiUQyTEjnWyKRSCQSiUQiGSak8y2RSCQSiUQikQwT0vmWSCQSiUQikUiGCel8SyQSiUQikUgkw8R17nz3Ya2ZSWqNlavXuigSiUQikUgkEskgGXbn+6q1hlSNBo1Ggya1BuuQetUe512jSaXY9MFQZiS5KTmP2ZDl0dWwP/Mx2j5OXJbudowFevQGM87EpXrT4rYZKdBkYTCfv9ZFufnos9FU8zQ7fPrtps/WQE3lbmzuSC9+jM04H42+ErPTreh0KgXGdiK+FhOxliEBDElfVGRTYBx8+Z1mDHq9T66ftL5w/dVXaVv1mOA0Y9CHHjv0xTXsN9vou5ZFDtAhyfCRIOf7Kg7TjyM4KP3O74jMMjqFQFyxUD06WrrJZJa9TWdZJiMGVK5kMsuasNd/P8ZqWKnJGuoPAskNh66C5osuhBAhfvZSknbLtS7h0HHVSk3qTGqs13SIkAw7bpyt2yle+S4u3+8u0Fq7ipXWaB+bt5BWshdhX09uSqLnd2ItQwLQTqakwY69KpeUoc9t0GjTSmgQFqpyx17rogwLN1J9deXNXPQbNy7SsjCJQwvnscR4+to64JJrQoIs4wh0hS9zxVJNZrWFK35KdiV251cikQwx3tUgz4fxUId0+Va6ik04hjAfiUQiuXFIIe3eJazfMoOm0p/xsrXnWhdIMsxcpzHfMToIbgfWHQZm+pZxXqDVupviUCEtPW2YDAVKmgUYdrTi8K2zKPl9KouV1pVkfUo1az/koTGSm4fLOKwmaooz+vVnpgFj4NJikN4+R5MtYGG7+wSHa4rRR3pGjcPk0ftwPz7n17OaJITgiqWaqItPIzIp63ybsszkAchDWemy17Mopqf7sNYUD2CW/RLdJ15XyT2D4poGbH2qhVS3A6uphmLfErCemQYjZrVM+2yYjf3totEUYDCa/dPxQwlLKngWU8Nz/WnPNLDDeg6nraG/TPpinmt1qJZ2o+mKG6e5Er1mPtvMLezw2S5V3dzvYyrNCBEWobzrDfsIRUR5uHGaVzM57xkc7KM0/TPoDbvYb7iPvA1WaCwlPSkLg/m/lDIWYNj2rJJWFgbzB/5hJ14unKbJp9OBbaTIMvAdZdneU8fzmIPKcL6/Pqo+Fdzv3PTZzBh9cgzTN/1kpA47iaE9/Nq2DsNMva/ta5rC5ONXv4D28wupCNAXfTE1h0/QrS6uXxhGPOV1Ymvq1199cQ37jQZmRgvpiKVPDai+3n71AubW8HIMDjsJIaOG/dQEtWFgvULpXkAbRrUFA2Ek4+cuZV3+CTbuPRE5tCnqmOHEZjZGKW90HQqVV9R+IhkQQ+Z8X7XWkFVj5Soj0BX+hh2FX47j7VgcBCft26t48cL97HZ5Ztm7np3KseefYeeieuydZWT6YlU+ZmfpJo7fs4leIXDZn2KceQ2/aTnvn98VC9WZ1ViuqGbu/dKRSMLhps/6Ag/POUjSkkZcQiDEJexrPktd3or+mQ33+5geyydrexLLOi4ixEXMM05TnFOJ6dxlX2qOnU2cmFSBTQiEq4td498kf3Et1nDGX1fIjlD6KwQiZuf3RuU0O9/oZNKqYwghcNk3Mv6NJSx+2aIMGD1YNz7GnIOfYYn1kkcmrn9jTdIe8nwy7cH68goWHrmDl3pdSjplJNU9zrK9tsjxkI3Ps9n8VVbZXAhXF83fOsmjWROY/HQn333WihAXObNuBNVzn+bAucvErCsA7OPxtY2MKt0XXDftl5n1yByoM/GWSnfgApaGRiiaRVbIsI9o8oCU3HW0N1eg4yFqO/6BvWohD1a9SXN5JuTX0uFSL/c3UndUR4XNhcu+h8ezPx8iz49oXPlLdic9jtUlEL37eODDjaTP2RRep4MYS26oMih9ao75K1TZLyGEi96X7uDIwnksN73vabs+Cy8vLudI+q/p9ZP3avbGtWcjQnsAcJlzphVkZu2CZWZ6hYtecy5txaqyxI2iL1lb+fCBfZ7y2yqYdKKJnVGXk2IpbyU5xaeZYb6IEC5sFV/g0OoNtERMN5Y+NUgan2Zt/ecoPdTlaa9dk3gjP7B/eAmWkcv6OEm7f8nKxnjX3JQ2nPMW46qsnv7Z+2vSjywnZ/kBziXS/9aOZeJUPY6TZ+kOl27UMcNJu/FJchYe8ZXXZX+KcUeWq/pXjDoUS1+SJISEO989K7P4lEbDp7JWMqQLKU4Lr5ruxLB8GjqlFlrdNJavepL8oIdHkF+7iWcKJ5MMaHX38IMHbqe75x9DWULJzYLjGfJuTQq9acY3g3OB1r2v0VFUTGm2TulYI9HlLKAov52md7s9m6I6m9lq/DTla1ZQmJYCpDD5wUcowsTWhvd8xk1XbmCVoq9ox5Pz6ALyW/7Iu/bLIQo4BFy1UpPqrWe4mO/gGb7nWv/E/uLUEO/8jTOmSmU2Rc9MQx1WR39dPOEpo8hauZOVWaNUMo4l3jxTJU/Q6r7Do0V309J0GrsbcJ5g78ZuiooXkK0b6XklUKbubt5taidjxj2kJWuVdO5l/dudNJRMjmwodY+yZtVcz3taHVmzMtHxEOtWlSr5pZA2fRoZjj9xvMNJrLoSum53MSt7tFI3LSnZc1mR/qaf7uA8RUMdFBXcGTpWORZ5xEUmRcXfY3KyFq3uNm5LDi0tXckvWe+118mTKVxloLzjNfa2XogzPzVunC1bWWq8XSVvLcmTi9i8aw6Hl26lxenGbT9NU8skZkxP9fQpRqLL/Tlvx71nI1J7AO73aNhqAl//1ZI8+XsUF30a49ZmOgfkvSj6orYJXvnpBlle51GeX3qE2Vue4tHJKfTLroKISSdch0Kg7leMRJf1HbJ1JwL6h5dgGYX3B6LgPMrzS01krKtgubd/Jk+hZPMmig6v5/mWIdjg2dZJV5gPlqhjhtPCq6tbmb3lV77yanXTeHLzJso7qqnca8Mdkw7F1pckiSHhzvdoJeY7piXtwfD3Hro/P5pRATXQjhrNuKCHRzBu9Oeu1xgbyfVOhA2X/ZuxxpJbZcFelUW3+SAmkwmTaRuGvFxKGz9SEvqYzqNv0sgk0ieowjhScqmy26M7ebxHR1eMi3/qzcO6QnbsKIw8mAYyIpOyToEQvViqvxLiATd97b9l9YtO5u0+65FH17NMO/YST+ycRb29yT9UZefPWXs8i629LoTLStW4t1jwm6O+pVbPRuxeLNWLqLb0qmQ88JAX34CWkkuV3UJV9gXMJpOnbYwG8tJLafQ+qx3H1++dTOPqVznQasY0pMussehKOJKZkD6pv27Jd3J/0d007nlHcezcOC1vUUc+BVljQicRizwSzmfJmHaHb6LEUxU96Rl26hpODeI0Ec8sv0OXysRxI1W/15I8IZUMRyMNlgto9VO4N+coq2t/T6v595FDI+LCvz3cne+wpxEy0vX0a62nvUVDCWkDGYScp2ioswekiSK/zw6ivFc9uuK4nWlTvqSyPYrsIiVzLXQoWU96BrR12IP7ZhgZaVO/zYL8eCyf0n8ceqZOHOtvjxOir/ESbcxIoy9kG+Ivr5h0KLa+JEkMQ+aPjsgswzLgU0pi4HOjGfe3Hnr9PsTc9Nnf569DladEEhY3fe07KNbfSnreixzvcQNfpOAlE7X5YQaMoaBH2bPwqSxWDunS0wVaX/0j0wylqpkvHdnLK1iXH6LX569j6zOFvtnh7B88wLTuHv4+lEX04aTdWIp+VDp5m497VuRGz+Ylyyvk+z5oRpNZtpuOXffQU1/FvLx0RsUSwwqQkcqEMLO9oUmkrtxCasEPKGl7kdqW83gG0COkr5hLdtiTRmKRxw1GiNWpJLUjmJxN2aEWdt3z39SvfZy89FuHKI438bi7z3LyututfBPqkB9WNuR9wX+lM+l2SuMOYYmRuG2Il8t0n+2MuJndcfIsdnscOhStL0kSwo07GZySxY8KT1G16ZiycdJNn+0Aa8uqo8SqReHSGd7t9Ay2bocVU00xenlSgyQabht7l1fQNLueLlcDVSUPUlj4ALn6j+hoG0btGa3EfMd0lOdg+Ac93bcwelSAo639HKPHhXC+xwWvUg0Xbtt+lpe2Mrv+LK63qygpLKSw8LvoL/4HbX5PppCWW0hJVYMnxtSyhe91P0dezrPhNy4OrEAJ1RXt+O/ySBGeGTnnKRrqxlN0/52EWy+IXR43EPm1dLhCHQWqik1PTiO38DGq3rYjXHYs9ffSvTqPnLUt1/WZ+tpxE5ka17LV0HNT6pAfnr0OkVc7E4DzFA11VnRTJzJuQPZxJOMmpkZc1dRNnYheH4cOxdKXJIMmoed8fyprJVYl5jtkrKY6jjTwZBHVqSLqi3j801SffJLC5JJfYfjqGzycpEGjSSLtaQfzampUm8v6sNbci37eJnbOm6mcNa7+3Vf8T1IZkcEjtXdzJPdWz9eevpLjY4ppeWFufEv2kk8efXY62ghaWnf39tAfIXgLqdPvC54ZUk5VSMjFG15GZFJmGcrNwp9h9LiP6ekNOAqoz8F7f72ejgdy09fVSRuBy7JX6e2J5HKNRJc5l8eL56BzdHK2O4Gx9jHpSjyMIXv+I6TX7WXPntepy7iP6anh4pgHKo/B8lHwjH6fnY42vRKbroRDxM0Ysgry0bWd4LRD3UZu+qzPMTPcJVhaHZmFj1JclBl5s1uceMIcAlcvQly+4iVZT3pGlNEl5U4KivRh5BctTCliaUnJmkWRLnCm2qsj4RiEDsVS34HgldGxv6hOMUORkfeDNoZwGp9MznDs9H/6x5b3tVIzM1GXRQFc5txbJuocD7Gu9NthHPpoY8Z2uu8KV17FzqTrSY5JhwbYlyQDIqHnfPt/JYWI1fTFkYb4UZ0q4ruIJ8SP/4U7KaQVrudt7xfpjiVk69WDTv+pKUJ0KieuqH8XmN5IdNlL2GH35tdAVUmubwOWRBIWxbg11u2hRTFcbscxNlX+HKNqMlObWoCh4vNsWPsCZsdl4DKOlj3UNd5N9frCgcWEXhPGkP2jb3GsqpZWr6Hua8e0dg0rWwbjfDtpe/esZ4DwHWX2Y0yOgabpHUyPUrf9X5WB+TKO1m1ULn2hf0VLuYVxpsHUH4bQZ+OtBiuU/ICCsM7sAIhRV2JHS/JdsynKeJ3HH99LxoJvkxpWj2KUh5+j4uajvo9w+znIH9PXF1+bODZU8bSp3dO2facxLltO3exKnsgZi8/JcBxix/6/KM+0Y3q6ig1+Mgksg5uUnMVsmX2EpZXbFF30roK+ANVlzE+7RTmWrgCDN3/c9Nn+QEMrlCzOiyCvONFOYrZhMekbqnjWfM6zydrxr2yvO0GOUhb/5ycyfcF0HHWvsb/d6Sv702u3q9piLDlPVDK7bjnLvBeyhJTNAEiZzhNbvknd2o2YbOHyD6pkjDoU6tVY6jsQFBkd/jmV28PLyPNxZKduxxu097kBJzbTRtZusPY/lDKdJ7bM4PDSp9jkPR7Ua9tYwvr5aQlwnJzYml6gcukR7q19KlgvVEQdM/5HFj9alx1QXk//2pC+UilvLDqkjakvSRLDDTPUx4T7HObNRv6aMYFR17oskpuHCKedaDTeq3nHkvPTF9me/Q55+k+j0WiY8LM/8uXiF6kvz+xPSzue3HXbsTz6EWv1n0aj+TT6tR/xqGUbKzIHESfiMFEcsJoU6nz82FaV1OfsB55A4l3R8uyC32TQUf/wRM/f0p7h/Xm/ZN+iW/zz089j5855vvAtv9/5naOfzF2PlDH1yA8ZpdGgScqk7PgYils2UKgbxBR+ynR+eqiK7D8Wo0/SoNFk8rM/jKX4wL7+nf7ayTy6fQ/LvnCQnFFKW496iINfWMyhdd9nfEItZYy6Eg/aSRQsLkTHdBZMnxjZsMciD7yD/kVK0z/H5+b9jk73LaTOfoyKy6tJT5rEvL2dqtsvo/FZ8qsfI/e9Z0jTaNCM+iFHMmpo2TTXJ1tt2oNsbiyB1Rme9p/zKr3zynjFb8NccBnc2q9SuKmeXTP+ikH/aTSaJEblHOQLy/awe0W25+SLtIVstyzmCwcf8qTte2Yr6+Z+NYED4Uh0uRXstizEtfYbJGk0JOlrcD2621cWf24hbf46GldcZfXtt6LRTGBO7d+Zt2aV3ykd2vFz2dRSQ4a3b4xazvFvlFCdH++Gy+Dyji9cT0vlFziYc6uygvw+ucueiHxKSIw6FExs9R0IHhltYkbHv/TLKP1/UZ6jVuo05m9+lRXUcPuoJDSah6jtzWfNKw+pUlJksmsG3YZMklS2wLJ7CZkDmIxzbMjjVr9xYzKL30qm8FAjr5ZMCRsiplQsypiRwuSS5wLK+y90zNhEx6HlvvLGpEMx9CX4AFNxqrwDZZBohBAi5B80GsL86TriKg7TUvTztir/nsKijS/zrOr4QYlEzY2h1zcyH2Aq/jU8WzM4h1kSJx9jMy4i59gP+LdthQn+WJB80nDbjMzO6cTQvo7csBt3bwDc7Rhn57J66i7aExmrLZFEIZqvcQP3KggOd2ljx5PS8ZZIrg2XcZiNbP7rV/nqtdpd+QnFE9pwhRX/nCsdb0nsOM0Y9BkUe0MRUEKgnn6RyxFPzJFIJINB9iyJRDJw/K61/zSZO8ZSNcClWckAUDZeJelrcC17hh8PJnRJ8skjZTo/PfTL/lAEjYakzFdwPbA1TJiMRCJJBDd42IlEEh9SryUSiUQikQwlN3nYiUQikUgkEolEcuMgnW+JRCKRSCQSiWSYkM63RCKRSCQSiUQyTEjnWyKRSCQSiUQiGSak8y2RSCQSiUQikQwT0vmWSCQSiUQikUiGiZBX0Gk0Gr//SiQ3E1KvJRKJRCKRXCtCOt9CCHkesuSmROq1RCKRSCSSoSTaJJ8MO5FIJBKJRCKRSIYJ6XxLJBKJRCKRSCTDhHS+JRKJRCKRSCSSYUI63xKJRCKRSCQSyTAhnW+JRCKRSCQSiWSYkM63RCKRSCQSiUQyTEjnWyKRSCQSiUQiGSak8y2RSCQSiUQikQwT0vmWSCQSiUQikUiGCel8SyQSiUQikUgkw4R0viWSsHyMzTgfjWY+RtvH16gMbpzmSvTRyuA0Y9DrKTC24wbcNiMFmiwM5vPeB7A1PU/lDs/ffXXTV2J2uoe8FvTZaKp5mh3XTI5DyUBkqbxTYMQW4RVPOw6X/rnps5kwzNSj0WjCly2wLd3tGAv06A1mnENRrJtadySxMcz26hNC8DghGS6k8y2R3IRo00poEBaqcsd6fuG0UFv8LFaX94lbSCvZi7CvJzdlqM2AG2frdopXvosr+sM3IMMpyyHEbWPvsqXUTdpCl0sgGkpIC6rOcLflza47Xvqw1swktcbK1WtdFEniuGqlJnUmNda+a10SyXXGDTxSSCQSiSThjB3NKDkyDJ6rVmpSNZ5VBM1QO2Ae512jSaXY9MEQ5iO5XrlqrSFVo+hbag1W+RV3XSNNrEQSM/0hINvMLewwFCgDawbFNQ3Y+iIvh7odVkw1xei9BlJTgMFo9n/P7cBqqqFY73lGX7yRwyfOBaR0GYfVRE1xhicdfTE1h0/Qrc5LvZzoNGOYnMcGh4PG0ttJ0ldidn4UYhnXic1s7A87CCrfecyGLDQFL2Buret/Tl9MTZON0K6FG6d5NZPznsHBPkrTP6MKT4iWX1hB+slIo9Ez02DEbAsX9BAuxENpT58MAuSq0aCZacBo9tbN2/4FGLY9q+SfhcH8QbAsYy7jf3G66TlVez9HU9h6eJNuVeletLr317XPZsYY5j23zUhB0u2UNjpwbMjj1pBL0ZHaEug+wWGVfoeqS3xlD5Hfv7zAK6UZIUJc1G35X/16+qcX+ttgpoEdVgf+KuDAusPAzJDtHYqrOEw/7tePoB+V8zsik7JOgRC9WKq/EqlxgGQyy96msyyTEVGeDP9+E/b678f2+FUrNVmJcdBiadOYbB+XcVij2ZVznDi8sb9NI9oeCG4vPTNrWiM8n2BGZFLW+TZlmcnDkFUZnUIgrlioHh3Pm5foPvG6yu6FGM9isWd9NsxGVV+KyZ5Hs7k3L9L5lkjiZh+Pr21kVOk+hBC47BsZ/8YSFr9sCW8w+lrZ+PBiDiY9jtUllPfKSKp7XPVeD9aNj5G1+QIPtFxECBe2VZM48UYTDl9CbvqsL/Bw1lY+fGAfvUIgbBVMOtHETkeYvFNyqWpvplynI7/2DK6Q4RFO2o1PkrPwCOOqrLiEwGV/inFHlpM+ZxNWtQFtfJq19Z+j9FAXQlzCvmsSb+Sv4GVrT4jMtaTkrqO9uQIdD1Hb8Q/sVbmkxJOfHx4ZzTn4GZZYLyGEQLj+jTVJe8hbXBvmvVtInX4f+Y1vcrRTHTd8AUtDIxTNIisFj1znHCRpSSMuITx1W/NZ6vIC69ZI3VEdFTYXLvseHs/+/MDL2FjB0t0jlecu0vLAh6xPf5iakLIE9zkTj2WWYh73FHaXQIh2Xko/xsKcSkznLoeRmZu+9jqW5CzniPc9l5WqcUdYqOSlTSuhwXWG2nwduvJmLqpDlnyEa0sPjp1NnJhUgU0IhKuLXePfJF9V3/jLHiK/X/+EHzwyB+pMvOX3jrotFd1uXMrCl/DJtmNZEtuzHmOjV7bu9zE9ls8c81eosl9CCBe9L93BkYXzWG56n9AaOAJd4ctcsVSTWW3hivD0Zc/Pldid35uEmNo0Jtt3mXOmFWRm7YJlZnqFi15zLm3FAW3haOKNE5NYZXPFYHvA215CXKKrvpzF+17h4Zbf0yrjxlWcZucbnUxadSzMeBaLPevB+vIKFh65g5d6XX5tvGyvLUxfcsdhc29CRBgi/EkiuWGJT6//ITpqHxLwkKjt+IcQwiUuNlcIHZmivPlD1XMfiubyTEF+rehwhUpHeU9XIZovqh9Q0ve+d7FZlOsC0/bm6S2DJy9debO4qM7iYrMo1+lEfu0Z4RJCuDpqRb66nAF/9+XtLdPFZlGumyJK6s8KV9h0lXoG1iMo7TD199VBxJhfCELKyFtfVfpBRTgjavNvEznVx0VvyLTCyFV5z1OecO0fSpbRyuh9Z4mo77qkesq/HP7vhNOzMGUP/HtJvegK8Z4vPdcZUZuvi5COECHbMsx7iSl7iPx6j4vqnNv8dSREW0aWbYh01fkF9VV/rliqRWa1RVzx/62w1y8Xi+r/GvB0r7BULxLVll4RTK+wVOcIQADitqA0vcWyC8v2cpGjPKdbtEUct7wmFoHgtmphuaLKv/YNUV+er6SZL8q3Hxd2V+j8/H586cRKLG0ao+0LqUPq9AP6mE8u6v4ZAdcZUfu9NaL54t9FR+3ioL4pxF9F/aLbPHJYVC+67MfFdq8Mc34hmu2KHtnrPTLnNrGovlPYLTtFeY5OgE7kVDT2y/mKRVTf5pVtTnDb+/3d2+6qMqjaw2W3iAO1/W1PTrmoPWBRtakITjsztrYMGieEELGNSwH9O9Z28CMWm3vjEs3XkDPfEsmgSWZC+iRo66Qr5MyrlpTc9djt68ju/gMmkwmTaT9GwwOkl+5TnnHjtLxFnWMS6ROS/d5NnpBKhvefzlM01NnJSNfjt5CZrCc947MDLL8379uZNuVL/sthyXrSM6Ctwx5+Vj+WZxKVX0ouVXYLVdkXMJtMHlkaDeSll9IYKUvtJAoW30fHxgPKrNdlzr1loi79EeZnjwHGkltlwV6VRbf5oNJG2zDk5VLa+FFMtRpQGTPuZopupFoATEifhKPuLSyBs3POUzTUWdFNncg4f6GFf8f3np2MaXegC/FeeL1NBO/R0dU38LKHIvlO7i+6m8Y979DpBp8+kU9B1pj+5yLK9jyWhkYculQmjlM/o/Q3RyMNlgsxFeeqtYasGitXGYGu8DfsKPxybPVQypRZ9jZCCK5YqgkdLeCkfXsVL164n93KzHHXs1M59vwz7FxUj72zjExfrMrH7CzdxPF7NtHrXU0yr+E3Lef987tioTqzGssV1cy9XzoxEFObEoPtA3fnO+xpJMCuefpk6I2/aj6Kanvcne9g+loOWSmfJe3B+STt/QPn/NTtyxTu6ETY61nUuZmFmWv4yx1rsbtc9G7NhV5lxUxXyA5xBXv9fXRuXkRm2WnuqLHiEu1snXULvR8piUYLORqRSVnnf2OpXkR584dKuNGXKdzxF7rql/O92jO4lPbQfknPV+76Kc3edjpUBKZKVZsOEV67EIs9047j6/dOpnH1qxxoNWOKKWwkgTb3BkQ63xLJcNB3GmPx1xmV/jCbj/8N0DK6YAOW2ocUI/cx3Wc7CRc54sXdfZaT0R6Km8tR83acPEt3wvyzweTnpN1Yin5UOnmbj9MDMHo2L1leId/r6IVkJONnFVKE4lS536Nh65tkFM3mrmQtntCMHRTrbyU970WO97iBL1Lwkona/Hg+LAZTxtjwxGSr44w/4+fIBBJVZxydnO0OF7KSWOIte2huIbXgB5S0vUhty3k8ISdHSF8xl+xYTptxdHK2+5Ly/8+Qd2uSX9x2UrQPOYWelVl8SjsroZAAACAASURBVKPhU1krGdIFcqeFV013Ylg+zffxpNVNY/mqJ8kPengE+bWbeKZwMsmAVncPP3jgdrp7/jFkxYvaplFt31CHgPTw59c7KCz9tidEKuVOZt3aSENnmKMrR+awxnqIquJsdFotyWnfITctJfixb/0Ca3MVxZk6tKSQlvsd0pLjcalGc9cPH+D8xtf796K436PBmMKKB9P6nTOtjrvu0vX/O3kys4e4Tf2JxZ6NJrNsNx277qGnvop5eemMimFPR+Js7o3HwPZ2SCSSOPgY295fUdo0g/qujRSO9860ncdseA9IBUYybmIqOjojpqQdN5GpOjiZ0PJFz9s7u9U1zPkF4rbtZ3lpK7Prz7Kt8KvKgOTGaW6kDZgaKduUOykogoUNp6iYcJY9jXezYPNETxpuG3uXV9A0u56ubYWM9+btNGNoc0RJOIFljIqO/Fozh0smxzxzElVngmZ/h4r4yx4O7fjv8kjRehY2nGJVFjTUjaeo5U5i2tamS2XiuE97/j+/lo7D0WZWQzO62sKVskyw1vDNt+N/P2b+3kP354NPoNGOGs24oIdHMG7054ZxVi1am36MzRjN9g0xzhPs3biBDSs3UKoud/pPeTQtRLm/ksHtumj9YQRfyZocsJIUP9rx3+WRr/0Lr/+5kLLMFJwtu9mT833qVR+RboeVA689z9KVO1UTFrexqP5fBpd5jMRuz1JIyy0kLbeQkqrLOKy/57Xnf05eTifN7euC9xkl0ObeiMiZb4lkyOmjq+O94GVw99/pOa/MwKElJWsWRbrAmVE3fV2dtHn/mXInBUX64FmBPjsdbQNdqvPmfYZjp//Tf3NMn52OtsDl4MEy0Py8sggMV7lKb08s17uMIasgH+pM/G/TIRrz72N66i3++QaEZrh7e4hvcTfOMgbN/Hl0RafeOOjF2/bH/oLDT2g9WGu+H/5SnLDvefUylQlxzdgNgIGWPSxjyJ7/COl1e9mz53XqMlRt6SWibMeSVZCPru0Epx3qWX83fdbnmBnHxUYjMsuwDPiUkhj43GjG/a2H3oCTevrs7/PXocozFmJq01hsH2hTv82CoNnORFxy5sZpaeH/rjujbOhTflxmFph203LNN16OJad0Bi0vmjl31cb+jU4W/3Bqv+1zt7P9sRd57+urPBuZh31j70Bt7kh0mXN5vHgOunArawmzuTcm0vmWSIYcj9Ona9zD9pZzHmfT7aB101MsNZ7ufyxlOk9s+SZ1Cw0Y2514joc7wNNrt6tmPMaS80Qls+uWs8x42jNQ9bVjerqKDZFCC9Qx4R/1EbTSm5LFj9Zlc3jpU2xqVY5j6zuNcdlyNqSvZP38tEEYC3XcupuP+j7CPaD8vE77Ueq2/6sy4F/G0bqNyqUvRA3ZAS0p2XNZkW5iZcVx8hd8m1RvJooj0Vi3hxbFGXM7jrGp8ucY4wrzibOMju2sffqAchyXk3ajgYV132TLE9MJXuhW2v7wz6ncdExJ24nNtIGylVC9vjDMDO4Ysn+0jHsD3ms3Gli4YVyE90LXL6gtY3pvoGUPl5+W5LtmU5TxOo8/vpcMdVt6iShbLSk5i9ky+whLK7fR6riMt7+tLXsBqsuYnxbgzF8rUrL4UeEpqnxy85azmpbBpHvpDO92KkdNeo8CLDbF0I+8xNKmMdo+7SRmGxaTvqGKZ82e59yOf2V73QlyBtMW7g94a+8lSgom+dsT7USmF/5XzHH9Q4k27X5WjH2D3+7cw56v3c+s8f4fKRfe/wyT7pioOORObOZ6Xjv4b8NVutjsmbsdY0EqMw2m/qMF+2y81WCFkh9QEPhhDAm0uTcoA92pKZHciMSn17GedhJmJ76agNMK0C0S1fuaxfH68oCTAC6KjsaNYpHOu7O9QuyrfybgJA+X6O14U1QvmtJ/osG+34lq1Q7x4F3sl4S9+RdK/g+J2o7/DlHmi6KjuVbZva+kW9ssOnq9fw9z2kksp2S4ukRzhXJ6gO90hGj5heKS6oQBBEwRi6p/J5qP7wu5Iz+YcKeMCOFSn3AAQreoWuxr/oOoDzohI1r7x1JG74kCz4j6N/vbW7doo2js6Jdi8CkuLtHb0SxqA8pZb7FHORkg8D2dyCmvFc2qvGI77UQEt+WVSKedqGU1wLKH1B0hhLgkuuqXhDixRNHTnHJRu686rGyFEEL0dojm2oB+WR/hJAlxRdjrFwecFBLiNItIp4qonr9iqRa3hXwm8OSTi6KjviL4tJNF9cIelN9tyokrkU5SuSTsx7f025mgvhdo+8I2TvQ2jdn2BfYb9UktkU47CaOzIU8UCfN73ykmgT+LRb3dK7VQbY/vhBR7nG3vJ0PLRpETUtah2ulN0VxbErFOfj8RTrGJeNpJXPbMcypLffUiofPlPUUsqq4XFvulkHl73olmc29covkaGuWhIDQaDWH+JJHcsEi9lkhuFj7GZlxEzrEf8G/qmFHOYzbcR97Jfx5wPPcNgcNE8c/g2R2F6IYkg/OYDas5W/obSq6XVQCJ5AYhmq9xs5oliUQikdzEeMISrrDin3NVjvcnBPc5zJuN/DVjAqOGKo++9zhx8ovDtBlXIvlk8UkzWRKJRCK5kXG3YyzQk6SvwbXsGX6cGddd2jcoAdekJ93Hji9UsHtFdgI3Qqtx42z9E07DYnJiOb5RIpHEhQw7kXyikHotkUgkEolkKJFhJxKJRCKRSCQSyXWCdL4lEolEIpFIJJJhQjrfEolEIpFIJBLJMCGdb4lEIpFIJBKJZJiQzrdEEhE3fbYW9tcUo/eeNKDRM9OwDZPZ5n/Fe7SUbEYKNFkYzEN0ea7TjEGvp8DYHuOtg/huJovrndAJ4TRXog97FfR5zIas/tMawv3ol1Lz7Fw0+krM1/zq50i46bOZMMzUe8od8Xp07zXZGvQGM5EuZY6Oklai5ROPHvS1UlP8PFbfNamx9JFY219dr2hy81wFX1Bq4lyUQieu7zmxmY397a7RoC9+jiZbhFZ1v4+pNCOmth9yGzEY+toxGQqUeg/myvehxk2frYGayt0R+qTkxkSxCRHt7Y2BdL4lkgi4zx1gec4yDiU9jtUlEEIgRDtbZ/VxcOE8lniveJdEYSy5VRZFfgIhPqS5PBN0FTRfdPX/3r6Fsp8dQNjXk3s9H3HmtrF32VLqJm2hyyUQDREuc3Gf5eiec6yoXkNG3VtYBuU030Jayd5rKJ/zmNeu4cwDD3BXsif/2PpIrO2vqldUuWlJzizCMHYrTx14P+JHgzathAZhoSp37CDqfplzpkpyFh5hXJUVlxAIl50DU09SnFOJ6dzl0O8cqPa/Sv2G5GNse59iXt3/pL7rEkLsvY4v3rlAa+0qVlqv148DiUQ63xJJBM7T8vx6jBlPsmr5NHS+3pJC2r0/YdW629m5ejet1/UMrWRIGTuaURGtqBtny05Wt83ge//fD1iQsZeq/bZBrjJcK9z0WetYe3IBhrlfVQaPoeojscptLDmlC+haupWWoe6H7vdo2GqComJKs3We+mt1ZC+vYF2GiaXPHw2Y2XbT1/5bKsv/TPq3h+YOyuEnhdGjRlzrQkgkNzzS+ZZIwuE+z9mT9jB/DD0D6Xa0ssO3NJtBcU0Dtj61U3CJ7hOvU1OcEeGZyzisdaql7QIMRnOIZ0z96eiLqTl8gu7+kihhIIFL2Mryf6SwBbcD6w4DM72hADMNGANDbNwOrKYaivXepfeNHD5xLqI4YyMwrEIpb8GzmBqe8+WnmWlgh/UcTluDnwyea3X4OWj+7aFnpsGIOVKIgOct+mxmjGHec9uMFCTdTmmjA8eGPG6NGCZwAUtDIxTNImv0JKYvuJvGPe/Q6Sd6pY4zDWzzhm7oy9m/vxy9pgDDtmeVemdhMH+gks9HYZZglbZXtbHbYcXkFxYSSqeiieUDGl98jZELvk2qV+UH0EdiIxa5edCm5rF4dmPEjxr/cA6vTr2AuVXVz/TF1DRFCCXTTqakwY69KpeUEH92nDxLt7oAfRZe/slmRjxbwcNx3YQTi40IDH8JbM8w/dxpxqD3hvF4bUSgjgXosrsdY8Ek0kv3geMZ8m5N6g+h6bNhNkawFUoo3EzD87766A1NfGCuRK/5X9SYVDZMU4BhRysOp40mn65mUPzcMRyqKkTW5fOYDfeRt8EKjaWkJ8UZwhNg12KyGRHrGMX+ekMFtzXRqrK5/aFMocLMwth2pxmD3vu7gPEhoG3c50yU6kPJxlO+cGFe3tDCbeaW8OOcn44FvhvYBwdm1+G/ON30nGr8CRH6FW0cC9lugw0LjB3pfEsk4dBOomBxIbrGUnJ+VMN+U0tEZ8V9zsRjmXPZzmI6el2I3t8yo62MnOUHVDGpp9n5RieTVh1DCIHLvpHxbyxh8csWxShc5pxpBZlz3upf2u79NelHlqvScdNnfYGHs7by4QP76BUCYatg0okmdjoGWWf3+5gey2eO+StU2S8hhIvel+7gyMJ5LDd5l/Z7sG58jKzNF3ig5SJCuLCtmsSJN5oYbPZhaXyezeavssrmQri6aP7WSR7NmsDkpzv57rNWhLjImXUjqJ77NAeU5X9Pe5RiHvcUdpcnFOKl9GMsDBsiAJ7ZyjqW5CzniPc9l5WqcUdYmP4wNdYeTwiD6wy1+Tp05c1cjBTO4DxFQx0UFdxJCreQOv0+8hvf5GhniCXxlv/D0TErsYlL2Ft+wrfGjAAaqTuqo8LmwmXfw+PZn1e9EC49leOaooW+VjY+vJiDqrAQl72MpLrHVXoXHXdnM1uNk1gwfWL/wBFnH4mZeOSm/RJTpk0K65yHpfFp1tZ/jtJDXQhxCfuuSbyRv4KXrT0DKPBnyVd/lNCD9eVfsXFSJb+am0pSXGlFsxFO2o1P+oW/uOxPMe7IctLnbFLF4sdKoI6N8f+zdjIlDe/RUfuQL0zIXpVLSt9pjEvmsfCI11Zcwl71FY4szGFOTatKrxy01L3LmIpjCNd/0vJ4FrcCcICVmy1KPS9hb/42rY/eg37y05z+7rN0CRe9Z8qg+ses9oYVRdXlseRWvekJacqvpcMVT6iRx67NOfgZllgvecKgXP/GmqQ95C2ujSLXcHWMBQeNj9dQP+pHHBIC4epi1/g3yV9ci7VvpEf3HY00WC4oz3v6twM7J8+eV2yyG6flLerIpyBrtGd8mHOQpCWNnjFEXMK+5rPU5Xn0Wzv+uzxSBHWv/cF/v4RfvwvHPh5f28io0n1h9DMOBmDXPe9VsHT3SKWdLtLywIesV+yzRxyxjGMe2Qe2W/h6JxbpfEskYRnJ+ML1tDRu5N6mlTw0bybpo5KUmRZTwGzIx3Q2/A4jj7Jm1VzSkrWQfAcPFs8B4+9o8DkNmZSvWUFhmqeLa3Xf4dGiu2lpOo3dDTiP8vxSExnrKljuXdpOnkLJ5k0UHV7P8y3ngQu07n2NjnIDqwone66XTp5M4SoD5YNa3XbjbNnKUuPtrFtVSrZuJKAleXIRm3fN4bB3ad95gr0bu1X10JKcNpdVax5lyBbXdSq5anVkzcpEx0OqcqaQNn0aGY4/cbzDSehwiBQml1Sxq+hPIUIEvFyg9dXNNM3+Jeu972l1ZD/5a3aVd7Oy0hTHRp+Pse1/mQ3kU5DlcWi0qd9mQf5RVte+E5y/bg7FD95BMiPRpX2VUQBkUlT8PSYna9HqbuO2ZH+T7UnvBHWvn1LN6KgHUDfO1gNs7MinuPRbvrCQIL2Lipu+rk7adKlMHDdS9ft4+kisxCk3RjJuYiq6cM55ONQ6xUh0Wd8hW3eCpne74wgLusy5A1tY3XYfiwsmKQOqmz7rq5S9kcehTXMZH/coG81GWHh1dSuzt/zKZyO0umk8uXkT5R3VVO6NN6wpso6Fxo2zdTerm2awZf1jSh8ciS77J2ze9SgdK2vYq9qQqSt6hAcnp4D2i6Td5nVL1fX0yl9Hvs/2aUlOu4cZGX/j8PF/py9huhwGxa4VFS9Q6gNox5Pz6ALyW/7Iu/ZwH+yR6hgbOrUt99o3JU+P7vf0O9ru85w9CQ8vuoc23wen+oO7xzM+qMOjGIkuZwFF+e2Kfo8he/4jpPuNTWoHfkyoYioE6uddzMoePTD5x23Xve8tUeldCmmFK1hT3s3GvSc8KzqxjGPepPzabbhcb+l8SyRRSCHt3ifZYb+E3fIG9fWvUb3IzobSeeSlT6PYu+HSfZaje45CRioTfIOXlpTc9dhj2ZzU1klX31WP8XPomTpxrH/nTNaTnmGnruEUTucpGursZKTrSQ565rODqKsyoxLkYGlJnpBKhqORBst5pYyTSJ+QHPzMIHJPKM5TNNRZ0U2dyDh/QTIhfRKOcBsfvbKddocqfrn/PU87xTjCKDqh885AA2gnMn3B9PD5x4t2EgWL76Nj4wElrvoy594yUZf+CPOzx+DTQfs6srv/gMlkwmTaj9HwgCeMIGYu0322E4effnuJsY/EStxy8+ree3R0DWL7c7Ke9Axo67DHWF4lpnvp/+PRXRXMHe/pM+5zB1g+p5nv1fyIzJgc2RjxsxG3M23Kl0LYiHjKPxgUW5FxN1N0IWzFYNsiJInS5TCk5FJlt1CVfQGzyeRJ32ggL72UxsGnHgcBMtRO9Au7cne+w562fB5d8k9keO2R3we3Z3OzvSqLbvNBRU7bMOTlUtr4UX8ed82mKP8Ee46eVT7WAlbMYmYAtnGwBOmd2q6fj2EcuxCY4rAjnW+JJCZGosv8JwoLH6ZsRxui9wz15Xp2lv7Kb4YnMVjZkPcF/yPYlBhjAFf3WU4OWXwHvrhOdf5JvgHokscJG8LsQxLS6YuOJyZbfZzdZyIO1O5osnV0crY78gyYL63Od9jjiwsPyD9hA8BIxs8qpAglPfd7NGx9k4yi2b7TSOg7jbH464xKf5jNx/8GaBldsAFL7UNxDJh9dHW8F7UsiegjwyO3waKEJ+XWwLpfU5k73jOYut/nwFPreW/FU/w4c3SYd/uPUOz/ifXovstR+19Q7PlQEDHWH/ALiQhH4Ad8DCREl8PhpN1Yin5UOnmbj9MDMHo2L1leIX9IPiZiRR129RF9XZ38e9Es7sn6DgsyjtBgueCxW74Zazd97Tso1t9Ket6LHO9xA1+k4CUTtfmqjzPlw71t9U5lRfMUDXXjWDH/7mELvRioXY+JiOPYtUc63xJJGCKeuZs8mbmlC8jnqGrmIFE8RG3HP1THsvX/2Kty+R/jJjJ1KA9PyK+lwxWctxAWqnLHe5b4hzD7xKEjv/aMEvMY8BNmE6A2mmyDZlPCcZ6W2hdpDCnLD2kuhw1VryfmrNqUOykogrqGU/R0vsOexrtVcdkfY9v7K0qbZlDfdZa3qx6jsLCQwtzxXIzqTKtRZrcCSHwfGUa5DZjLOFpfUhzv3/JCyRTfCpQnLt5Ky8p7GBXw4ez5mJiP0YZnI6pf3WI9um9k1P4XvNozBGjHMnGqPsIDIVbvBk2idDk0btt+lpe2Mrv+LK63qygpLKSw8LvoL/4HbQko/WDwhJ6co6PrLJaGI9yWridZO5aJU+Hk2Q+wHX2TNu+MtdvG3uUVNM2up8vVQFXJgxQWPkCu/iM62tSfbeoPd2VFM6OQ++8K99F4gxFxHBvMkaOJQTrfEkkYPAZPCfUI+9R0j6OjLIsHzr54nJNYL77RkpI1iyLdGY6d/k//5/taqZmpXIKScicFRfrg5eU+Ox1tqmXFuMNAxpBVkI+u7QSnHerZXc9lJjM18zHaLitlDJwJUmKC48pvCPHK6Nhf/E5KgB6sNd8Pf0lD2PeUmd9YZ2qcp2iou0TJ4jzVJjwvipzjjVEOiyc96kz8b9MhGvPvY3qq15Hzljtgmdb9d3rOX4ojD8XpC9DvuPpILNkMSG5e3RvATGq8uM9hrpyD/p7XGbdln5/jDd7zxAMGe7/NuYM9HzuSjbDT0YYSjhb6YylxRLAVQ9YWidLlUHjLHRjOc5XenoHsW0hwGJ52LBOnXqKutorauvFKfxpDVsEM2upqeKbuXP8mSa8eBITOuXt7CPpETrmb+SvGUffa79jzxhEy/DYND5BkPekZQzw9E7TK4dENT6ja2BjGsWt/BvzN4XxftVKTOpMaq7zuRJJAtGnM3/QM99YtZ1mNCauvI3uOAqxYvJ7L1WXMT7sFuIXU2Y9Rkb6Xtc82exw39zlatu+hMWcl6+enxdbZUqbzxJYZHF76FJu8xyv1tWNau4aVLFHSGUvOE5XMrlvOMm88bV87pqer2KCa2PA5RjveoL3PDTixmTaydoM1XIVJyVnMltlHWFq5jVbHZTzH7h1gbdkL4K1rynSe2PJN6hYaMLY7fc88vXb78IejhEWR0eGfU7nJe1SZE5tpA2UroXp9YZhLccaQ/aNl3BvwXrvRwMIN4yK8p8a7cWkOj8z6coh215KSPZcVOScStGqipJduYmXF8YBTN7wO6x62t5xTNmw5aN30VJwXv3jjJQPCbuLqI9EYqNyUUAy/j46hoAfrxsXkPWOnpP4VnvFukBtuUrL40brsABtxGuOy5WxI99oaJVTBcYgd+/8S1kYMHC0p2Q+z7t4AW9Fex7KF20mPuc3jIVZdVn94fExf39XY6pM1iyLdUeq2/6vS7y/jaN1G5dIXBmTX4re/kVA+sHfvZDfe1TelT7bsZmfHjP5NksoEQmPdHlqU/uh2HGNT5c8xBlVkNHfdX0iGcTmPbxwf+0dyJHz7M15j/1CND47trH36gHKykmKf677JliemkxLrOHaNuTmc7+HkqpWaVG8MkXT4b260JE8u5lXrdgrHHKdM/2ml3T9N5vP/yTfW/B8OlWX7BmCt7l7W7d7No64a9EkaNEnfYK1rIZbdS+LYeKWcHrFrBt2GTJI0GjSjHuLgFxb7paMdP5dNLTVkHPmhZ3l71HKOf6OE6nzVhkttGvM3v8oKarh9VBIazUPU9uaz5pWHIlT5qxRuqmfXjL9i0H8ajSaJUTkH+cKyPexe4a2rUsYdUziSe6vnmcUWvrHsCfLjF/KQ4ZHRJmZ0/8rTHppbyTk4hmWWbawIG4/r2RX/gt97k/lJxzR2deymLOx7Ktw29lftJX3FXLLDbVxKvpP7i+6m0RtvOViS7+T+oumgK1SduuGpT0rOMg5tn8of8yZ49GnCz/jDl4s5UF8eV/iQNu1+DOXnAhzf+PpIRAYqN/dZju45EfDRkXjcNhOVK98ATmOcN9EjS/VPpLPzE0oKk0ueC7AR/0LHjE10HFrebyPSHmRzYwmszvDYiDmv0juvjFfyEzQrmTyFkhcCbMVP/sKMXS2xt3lcxKrLykTI5dWkJ01i3t5O3O52jAX6yNeSp0znp4eqyP5jsdLvM/nZH8ZSfGDfwE6RGoj9jVT3rFkU6fDbiOxx8HUBK3Jjyfnpi2zPfoc8pT9O+Nkf+XLxi9SXZwannJrH4pIpkLCP11tIm7+OxhVXWX37rWg0E5hT+3fmrVmVuPEh/wmW5b7P02lJaDS3kntkCjta1lM43ntKTSzjWAhi0ZMEoRFCiJB/0GgI86fQOEwU6+ex0/vvnI1YVIbg5qMPa80S3p75AmWZ12T+QzIA4tZriUSi4KbPuok5ZVBzHdl2t83I7JxODO3rBnCZj+QTg9OM4eGzlL5eEsPq1ScIdzvG2Qs5triebYVflTOyCdKTaL5G4uSsK2SHELi66lm8eCeND3ewt/V62JUukUgkksGjJTmziDXZb/Ji4wcJ3mQ8UM7TUruHCVsWkyMdb0lY3PT9v3c5+bVh2Ix6Q3EZR8se6i4/wj/nhwr1+qQxfHqS4OQ/prOhkVvnzyZvRjb/N2gTzgeYilM9S3TFJs6pr36e+UvM3nhBh4lijQaNJpVi07+rrtrWM7OyqX8jVLQQEL+/a0itsXJVXQaNBk1qDdareK4iPbgt4Lre36tiGCUSieSTzlhyflrGlw4e5M/DdaZvWNz0WeuoOr+YX82VM3aSSFyg9W0w/HT68B2jd53jOQzg0+jXXmLZ1tLrZiXr2jJ8epK4sBPwLF/cv5uJu39BbsoFzJUb6Vnyi/44HC8OE8UPbuav/zGS7GfW8tOiLEZ1HqOVr5Ob5q3yVRymn/Lg5j/zH0znmZqfUpT5OTrN70L2NOVWMi+RQkB6sNYsZ+/dv1YdL3OZc6ZyFvf8mNdLJitG+zKOP9vg61OUHcKeK3y/3/EIJ6pyQzSEDDu5EZFhJxKJRCKRSIaS4Qs7wU3fnxs5Vviwsvw3hqxZIzA2vBd6eXJkDmush6gqzkan1ZKc9h2V46167Fu/wNpcRXGmDi0ppOV+J8DxjsZo7vrhA5zfqDob1v0eDcYUVjyoPoFiJLq7pqiO5klh8uzvMa27h7/HkZtEIpFIJBKJRBKOBDrfF2jduxNj6e3KLvAkbs1byxvKlahBfCWD23XRLqsYwVeyJgdc8xw/2vHf5ZGvHeH1P/cAbpwtu9mT833/HfVuB1ZTDcV69e511QZSiUQikUgkEolkkCTO+XaeouH//nPAjUL/oGNBK7UtIW4/G1bGklM6g5YXzZy7amP/RieLfzhVddzMx9i2r+b59zJYZXOpbsGrZ9E1LLVEIpFIJBKJ5OYiQc73Zc691cDFksBbyW4hdXqojZfDjzbtflaMfYPf7tzDnq/dzyy/OPSr9F64wIhJk0lN1uI5kL2F/a8dpOlaFVgikUgkEolEcvMhwhDhTwH0Ckt1jgA8P7dVC8uVML//a71Y5P23389iUW+/oqR3RdjrF4d4BsGiemEPl6/fT46otvQGlNMlei0bRQ4PidqOfwTVwmU/KjYumqK8rxM55bWiufkVT3nD1SlqnpLrjdj1+nrhouho3CQqtp8RrmtdlEhcbBblOp3Ir70Jy+k6I2rzbxuGurlEb8eborriNdGhZOTqqBX5ZIry5g+HNOeo9B4X1Ys2CUuvS5GHTujKm8XFa1GWocy/t0M0uhYUVwAAIABJREFUVq8T20OMEYNL97iozl8u6rsuJTbduPlQNJdnCvJrfTo2rCSs7VziYnOF0PnG83+IjtqHBLoK0XwxkRUL7pPXlkTW83qxN4Fj3FC15fASzdcYMXj3PZnMsrcRZbH9focQ7IiY3gh0hS8jxMsDzDccWpIzn+Rt8WTov+qm8eSONp4MKFyueExV3njzlEgGidNCbfGznFx3Pd0dKRkaLtBau4qVJ/+Z+5XfaNNKaBAl17RUcB7z2jWceeAVViRrgcmUNNi5ZqXSDlX+bpyt2yle2cm6+6M/HRfJWTxuOMDdT/2ee7YVMv6TeqrbkLXdLaSV7CXxXSW4T948XCd1+4SOcZ9UEyCRSCSSqHjO0l57cgEGeZb2INCSkrOIyq5f8/w13wMlkUiuNdKWSiSRCDoFR89MgxGzLdouBie2pud87+mLa9hvNDBTk4XB7B18L+Owmqgpzug/YWemAaPZRh94rrmdnMcGh4PG0ttJ0ldidrqVYqkuqIq5TIHvZVBc04DN77IUJzazMeCyKXPAMwHl1hdTc/gE3aFkt8PAzFB1i4socor1vQSW0+2wYqopRu99Ry0n9/uYSjPQG8wBe13cOM2V6FXt2M95zIb7yNtghcZS0pM8euK5CMOrM+cxG7LQFDyLqaFftzQzDeywnsNpa/Cr63OtDr9jXgekM+4PaHzxNUYu+Hb/fh53O8YCvVI/pU6a+Wwzt0TRrQCcZgx6PQU1+2nwyVLPTEMdVsf5gP7zAq3eC88Gkr/TjEGvCWgT77tZGMz/hdO8msl5z+BgH6Xpn1E9e1l10VuYPtFnw2xU6VCoZ7STKFh8F3VVqmNvQ8o8ms3x6sELmFtV5dIXU9Pkr7f+eppBcc3rnOi+FKnFQ7wXoj7ettvWRKuq7+iLn6Mpkk4NVHcCZKIv3sjhE+dUCX+MzTgfjV/fCmi3qPIJrGfoPgkEt3egzVDkM9PwvK9PBtuDUGVW66TqI81pxqDPwmD+UPnFOU4c3tivIyHqFtmuRagbl+g+8brK3obqy9H6hKKjMw1s88o3lN2LMMYNvo7XOQONV5FIbkTi0+v/Fpbq7wndoi3iuF2J1XR1ieaKfEHORk/8a0guia76JUKnKxG1Zy4KIVyi98x2sUiHwBdPp+xB0C0SG4/blVi3S8Le/AuRw/dEteW/PUmFiFF2ddWLEt0UsWjjUWF3CSHERXGmtkTodEsixpR63tOJnPJ60dHrEqK3TdQumiJ0JfWiyy+d/jL59kL46uvdO5EvyuvPiF4hhOg9I+rL8wWoyuk6K+pLpqhk55XBFFFSfzaO2OkY5RTuvUSUMzDmu/e4qM7JVMlfCJe9UVTk3CZyqo+LXm88alB7eOJtw8e7Bsfj+sdgKn9H1YZefQRVHYL1YcA601Er8gP3yfjF7XpjbxHkVIj6josh5BGGi82iXIdA1Uae93QCVGUN1NOB5K/k5S9777v9fdI/jliI/r6s0r+gfuO1E9vFGcUmePMP3CcQUp5+xGJzQuhBqD7Re1xU59ymeuai6KivEDkQOeY7qn6HbruYbOOAdMcjk/5nXKK3o16U5+gEYWO+lXbzlS9Ev46lnqFi5L3t72ujS8J+fItYpNMFy8c7Brj+U3R0Bvf64Bjr/rbt1x2vPakQzRf/7qlntLaPyf6GszfR2iOWPuGtxxSxqLZN9IpLwt5xNrQtCBrj/pHAOl47ovka0vmWfKKIS68vNotyXfDmk6gD6MVmUR5kAAIH+jCOWKCjF2SYwm2YiubYhdrEEuBshCx3YBnC5OP3TCgnRpVfXBtpYpRTrO8NpJx+eYWrgyJfb7sojo9f+cLoU2CZozrfkdpQRHgvbp0JU9eQDlRgvWLY2BfSIQ71XoDuDiT/gTrf4Tboqtsyng250Tb8xmRzQulBYNrh9DRau8So3xHlGcE2DrjtAp+JsuEy5MZOdbox1jNIXuE+rEPZ0kD5hJOHSndcZ0RtfqZ4eNFcoQsogyetMJsRQ9moqPY3mr0JI5NY+kQ4HQ1FOOc7IXW8dkTzNWTYiUQSjpRcquwWqrIvYDaZMJlMmIwG8tJLaQz7khun5S3qHLczbcqXVHFdWpInpJLh+/dYcqss2Kuy6DYf9KRt2oYhL5fSxo/Cl8l5ioY6K7qpExnn13uTmZA+CUfdW1iCQhoA91mO7jkKGalM8N0QqyUldz12sZeStJFhyg0k60nPgLYOO33OUzTU2clI16vOyfc+81nlHxewNDTi0KUycZz6SE9FBo5GGiwXwtfRj8HIaSjKqcjMvo7s7j8o5dmP0fAA6aX7VPncyf1Fd9Pou2RM0QvyKcgaE2PdE8RAdYbLdJ/txOGnM7HiSZu2TroihZ4MGYnI39uX9UydODZEn7BT13AKp3YcX793Mo2rX+VAqxlTpGVvdV8K9fcB2ZxQaSu6HdR2ilzCEqN+h3nXY+Peo6NrMAv/6ra7qrTBJNInqHtyoD31x935DnsaCej/HlsiGkpI0w60nl653s0UXQibEW/dtROZvqDfTrg732FPWz6PLvknMry66zxFQx0UFdxJ8B3gaj7yb/uE2F8Vfu0RpU/En3qMDHEdhxHpfEskYXHSbixFPyqdvM3H6QEYPZuXLK+QP+gBxk1f+w6K9beSnvcix3vcwBcpeMlEbX6EwVnBsSGPW31xiho0ms/EMDhGQnG0IuV58ix2+1lORnrI74VnyLs1SVVGDUmxOBF+DExO7u4hLGffaYzFX2dU+sNsPv43QMvogg1Yah9SOXu3kFrwA0raXlQuGbuApeEI6Svm+t+sOxAG5AwPRGf66Op4b+DljAld8AfSdYeVDXlf8NMPTdLtlDZ6FWw0mWW76dh1Dz31VczLS2dUHPsw/EmQzXGf5+xJe5x5K8Sk38NFdLs0YAZSz6hytXPy7Hlil9AtpE6/j/zGNzna+RF9XZ38e9Es7sn6DgsyjtBgueCxZQP5aE+I/Q1HtD4xTAxpHYeWBBw1KJHcnLht+1le2srs+rNsK/Se9ODGaW6kDZg6uMTZu7yCptn1dKmPHnOaMbQ5oiSuI7/WzOGSyQn8eh7JuImp6OgMn+vUiej1MFUHJ2NJMr+WjsMlpA2mkAOUk3bcxMSV060+neJjbHt/RWnTDOq7NlLou6zrPGbDe0BqfxnGf5dHitazsOEUq7KgoW48RS13XiNHcyA6o8xAxiTEm5mHqO3YSUnaLRGeSSEtt5C03EJKqi7jsP6e157/OXk5nTS3ryM3xg+uhNkc7VgmTtUPoO1i1+/hIbpdGhgDrGdUuXpnhGP/8NGmfpsF+bV0dJ3F0nCE29IXkawdy8SpsOfsB9jOvklb0Y/J+v/bu/vgqKo84ePf20HUMQkUCzt0gyMV8yTRJSMzifGZkWfJy5gMtbLyhAVnEGgnKcnsYsRhQ1oCOrvyoiEZ3Kj4RiWTEORZ0HSB6KME0gZH1iWbZp0JJUkq64MjdDMl44bQvoB03+ePvp10d/o1dJoEf58qStN9z8s959zT55577r3RnrSH7X+v5Kk74Y6JOD3RJxa/MVfJOMyyEPHgwnG6jy78l2Fc5kJ/qNksHcnZP2G53n+WyhOfxmGjpwsy774dvddR6LrQH7rbSv4+RcsNdB39CLvP9Eo/1tp7UYoaAj9JQTeLuffPHTaj436ahoGihl4Ss3/Ccv1Jjp74k+/MjSev6QYSPen7zzg7bPR0eZaBTCG7qBB913FOeJ5Q4SkD6zPkKUto6P061F4OT3uk5RTzfGqzwf6XnV1f0H/O/ykSU8hZ8gDpzXvZs+cNmjN/ytzUUAO4UTLSNuMZ+Fy1pSMxlGggPVMfZSDPsRzomOigNi+VoobuALOcE9FnLWSlcQF6ex+nznq1Le9jaVi4kfY5gXjatn/dhbuaEU37jocI+1P/UKk/5v5hV8a0p4soS2jo/WyE+xmizzjdRxf+y2Mi2cWpzJpzkeb6auqbZ3D/3FnomEJ20Ty6mmt5qvlMBEtOIsxjtP3v8MyO8JiItdHcx/iQwbcQAXk6mfdpbvydNmi5hL1jB1UPbw99GTR5Lo88/z9p3rgNc+8A4MLRu4/NGxuHwmkDotbmPbRrnYfLfpS6ql/T4B259xrlLx04XFPJfaSK+W//mqq6o1q+Bug1b6ViLdRsKQ4yC3ADqfMfYl36XjY+3eYO5zpDe+MeWnPXsmVJGrrkbH6xKYe3H36COs9j6hwnaChfzdZ0bRu09JtXU95wwv3D5ujGvLmarYP51pGcW8bz84/wcNUO7RFx7jLYWLEdaipYEnIW0bssIyynYUYrn1qn37qHxvYz7jJy2emoe4KHG074basj8QfzWZ75BitX7iXT+3F9AXmvx/0ah+Ny2OKJzEjbjGf9pN8AcjzSTj7tza/yeneQY9JnHbGLLx1f4kqeyyPPz/M7Jroxb3yctaxyHxOubhqKUskzmYcetebo5fBBK5T8jCKvEy73cqi52gBrWCZH3ucEiiu3jOfnH2BZeTPdDhfuOt/Gxq3WEOGiad9x4ulPl5loCFp3fnQpzDeVkb61mqct7v1w2X9HY/NxcmsqWJI2I8L99D8mXSTnLGXTPX59Rncz5csaSY+mbxvkLnN272Q3njXMWlts383OnnlRLjmJtF8bYX8TyTERRW6H/8bFch/HLhl8CxFM8lwePVBNzgdGDAkKipLFY+9NxbjvNSpDTqJNZEbxFtqrprE/dxKKkkDa5k/IL3+EoXd4TSX30RdozPk3CgzXoygKMx/7gJuNL9BSmTUUlS6F+ablXCq9jYSbStjb9zW6GQupa69j3tkntXxNInf/FMo7d7Ama3LQXOn097Bp924edNa6wyXcyUbnMjp3ryIrUQckk1HyDO275nHWlEWCoqAk/SM98+roObBa2wYt/Voyj/ycJEVBSVrNsTtLqCn8jldit1Bc18KueX/EZLgeRUkgKXc/08r3sHtNjjbr5z0TFWyWIsJyCrS/McunT6wk55ZzoHEOHxTMdJfRzMd472Yj+1oqGdYsdCkUlRWjJ9iAy5t2gnRpA+kJKSza2xezGaQRt5m0v8VUeYY975+Kw2zWaLqBtCWbaF1zmQ23TUJRZrKg/gsWPb4e7/fq6VKLMK07T2n6Tdy06F/pc2nHss8xsZj908qGjhtdBg827qF82n5yk7T1p9o2Bzbd6/U2y6/pe/8dWgtDXAEZcZ8TgNa2mzIt5CcloCgZlB27jfKahaECRde+40Krg6bZHMl396dJZZ3c6dOfDg+jz1/H7s5lODfeSYKikGCoxfngbu24jnQ/AxyTibMp2e7XZ/z9R8zb1c6BikB9Rjieky7QL//J4PIS9+y9fmT3eETUr420v4ngmIgqr8N/42K3j2OXoj0SZfgXikKQr4QYt65mu3b1NjA/tw9TFGtAr33nsJg2cKr0X8KsqR2vvqa3YQW5R3/Gf4zL14q7cFjrWFABtV4nYGIEXN00zC+lx7Sf6vypVzs3QohRFG6sIT2pELE2YMFkyMToWe6AtlRi8wtcisXTLq4ljo85/uFf+j0u6trhvtT9DWv+IX8cDrwBdCRmLefxnHd4ofXTcT77fTW5GGjfyZaZ/8gjuTLwFuLbblz+HAgxpiXP5dED/zy03EFRSMh6Bed9L4+Ly2Hx42Kg498ZMJWRe62dkGiv0U4w1OIsf4pfhljaMfZNJffRCr67fz//Od5vvLxaHJ28Uv0lW5+8d5yehAkhYkmWnYhvFWnXQgghhBhNsuxECCGEEEKIMUIG30IIIYQQQsSJDL6FEEIIIYSIExl8CyGEEEIIEScy+BbiimgvijFUYRm4widBaG/Ki8/reSPjfv18NiZLyJe5jxKtbIO+/jzWzmExZccmvQELJoNhTNWlEEKIsWHC1c6AEOPbDaSV7EUtudr5EEIIIcR4IDPfQgghhBBCxIkMvoUIxWXHaq7FaHC/LEdRDOSZGrD0Dmgb+C070ZYb5JmepdaYiaIoGEyH+NRShUFZwg7LIZ7RPleUIkxNHdhDrEtw2a2Ya40YFGUoTIOFXs/LTjzLG3YcoqPJRJ62ncH4DIcG8+iJq4MmU1GQ/QjlImePvzG4P4qSibH24FAeIionwNGLpWEoj8P2JRJh0/EsHdmOpaMZU57BvZ3BSO2h3sE3jg4v20yMtW9w/OxFvwQvYbd6xRMwz5ewW81D5WMwUvv2cc5GvldCCCG+RWTwLURQ/Vi3PcSC/TeyynoRVVVRnf/B4wl7KCirxxp00Ginvfn3TFl3FNX5J9pXZjMJgNdYuWwXrGrFqTq50FMGjUtZuq3DZ1A4yNHBtqVl7E9YidWpoqoqTlsFCc0rKXup0yuMndaVtbQk/YIDqorqPM2uGe9Q6JVH1xkzD2WVYpn+BDaniqp282L6UZblVmE+cylMOZxg51t9pKw/quVhGzPeWuWVh0jKqR/rS2tYduR2Xrzg9NmX8r29Ea6LjqI+WjezseUmSg+cRlUvYtuVwluFa3jJ2u9Vtvfz3Gf30X7BiaoeZX1KH2/tPOGV3iXOmNeQteAw06utOFUV9cJvSD+ymtzV+zjjAnDhsG5nafbLfHbfa1xQVdTedaQcP8ROe0Q7JYQQ4ttGDSLEV0KMW1G16/NtaqU+S61s+8znY2dPvVrIYrW+5ytVVb9Se+oXq+jXqW3nnVoYVH1lm3p+KIR6vm2dqme2WtJySnX6f+4J6zyp1hfeqhbWn1Sd/t8N0tIrrFd7nGqY9Dx5/Extq8waCjPI/blvWF/uffUvg0B5CFNOPvsWqRGk49lX/3I736ZW6vVhytavnHzCqH5xefIRpAyDhRVCCHHNCzfWkBsuhQgmOZ9qW6d7uYT5PfoB+o/xXOlW2lnM/VFHeBt3z/6u1+UmHYkzU8m0v8DBzjXk53pvqyM5fws2mwtH73uYD38OuOg/toPSra1Q+NMQ6Wjx0krPaQdM/wMHm63ol89ius+1rkRmpqdg33CYzvW55CdHeSGsq4/TDhdpkZSTbjp33JNB6Ybfsm92EThmUpifRmI06V1JfSQaSM+E5h4bDqbSebAVe+Y/MDPRe5/d5cGHAC4GOg/TbDewfNZU30uEiQbSM21sOPgH1mfDwWYbmZsMvvuSaCA98zvuqIQQQggvsuxEiKAG6G4oxZCUTsFzx9yDvcnzebHzFQr52D2wjQkbH546N3zpheMEDcY7SEpfynPH/gzomFy0lc76xYMD32jYtxYwaXC9tYKi3Eh66WsxyH8k5TSZrIrd9Oy6i/6WahYVpJMU1brzSNOJgOscpz60RZimla0F07zKTEFJuI3SVveaEufZU3woy0uEEEJEQWa+hQjC1fs6q0s7mN9yih3Ft2hnqi4GLK10AXNilpKBObOmosP7Wdpf07v3SUoPzaPl9DaKZ0zUPj+HxfQxkBplGnoK6y28XZIR8zPuyMspmbT8YtLyiympvoTd+iavPvtrCnL7aOveFHbmPWb1oZvKrDkGIpuWXkx9z05K0m4I/PWAhTn6CKMSQgghkJlvIYJw4TjdR9ewpSKXudAf6UytP//ZWS0NfSFF2VP8tnVwuudjyPwhs/UTvYJ8Qf85/ydyhJH8fYqWG+g6+pHfk1X6sdbee4UvlRlpOU1En7WQlcYF6O19nDob7qbPWNbHFLKLCtEPu3qglTkAOpKzf8Jy/UmOnviT71UJRwe1edrLkDxl22PzvWnWYaOn68so8yWEEOLbQAbfQgTkGXy9T3Pj77RB6yXsHTuoeng7I1tpYGXrxm2YewcAF47uZsqXHWD+82XkDpv11QaIrXtobD/jHvy57HTUPcHDDSeGRx3SVHIfqWL+27+mqu6oti8D9Jq3UrEWarYUkzbiniDCctLe3plnMg89ps/Ry+GDVij5GUWpQWaWo00n0jznlvH8/AMsK2+m2+HCXR7b2LjVOrRZ8lweeX4ebz/8BHUddncdOLoxb3yctaxiy5I0dJ6ybV5NecMJ9wDc0Y15czVbZTmKEEKIAGTwLUQwyXN59EA1OR8YMSQoKEoWj703FeO+16jUjyTCQiqX38bHm+9GURJIyreQ2dRC3eASCm86knPLOdA4hw8KZpKgKCgzH+O9m43sa6kk2uR1MxZS117HvLNPavsyidz9Uyjv3MGarMkj2ZkhkZSTLoMHG/dQPm0/uUkJ7rXTSYvZP62MA5vuZUYkPVEs60N3C8V1LTRlWshPSkBRMig7dhvlNQu9NprIjOIttO+ax1lTlrsOtDx37l5Flnazprtsa8k88nOSFAUlaTXH7iyhpvA7Q1G5umkoMsTm1fVCCCHGNUV7JMrwLxSFIF8JMW5dnXbtYsCygYyCPjaFWj8shBBCiHEv3FhDZr6FEEIIIYSIExl8CyGEEEIIESey7ER8q0i7FkIIIcRokmUnQgghhBBCjBEy+BZCCCGEECJOZPAthBBCCCFEnMjgWwghhBBCiDiRwbcQQgghhBBxIoNvIYQQQggh4mRCoA8VRfH5rxDXEmnXQgghhLhaAg6+VVWV5yGLa5K0ayGEEEKMpnCTfLLsRAghhBBCiDiRwbcQQgghhBBxIoNvIYQQQggh4kQG30IIIYQQQsSJDL6FEEIIIYSIExl8CyGEEEIIEScy+BZCCCGEECJOZPAthBBCCCFEnMjgWwghhBBCiDiRwbcQQgghhBBxMsYH3w6stXmk1lq5fLWzIr5lzmExZaMoSuh/hiosA1/S27BE+3/X1c54dFzdNBQZMJgsDIxqQl+7y6iogd6rXkQD9B56lqqmbtxZ+TqG9efC0XuQ2qrdY2A/rwGOXg7Vbqap92v333Frr7Hh6m2gSMnGZDkXw1jHwLE0YMFkMFDU4DmGRjncaPNvZ2PaAL3mKvIUBUW5grJ0ddNQlDoY3t1Wl9AQqgxcZ7BUFWm/f0vYYX7Wp32PpL1HFuba61fjPvi+bK0l1TNwSa3FOqqjavfgXVFSMZo/Hc2ExDVnKvnVnaiqqv37jLbKLNCvo+28c+hz2xbyk8f4OWwougxKDtqwVeeTfLXzEi8DndQbn8bqHI3IP6ejfj1rrePhR3ysczHQ0Yhx7e8ZlaqKA11aCQfVTqrzp17trIigxlc7c/W+TvmiA6S0nMKp2jhYkhGngZyLgfbtLGv8H7Scvoiq7qV0dqLPFqPX3q+9fjVGdXYZu/mXIWYIhwa/E7Iq6FNV1G86qZkcLt5Esirepa8iiwkjylciWRWHsLXcG+FuWKnNDnZC4BnIK8NnHBwd1OYZZJB/xRxYa43UWh1XOyNCCCHEGHU9UyffFOfZ00ucPdWHnWQmJ41sRCaGxKjuJqAvfolvOmvIqunkm8HZQhVV/Sbywe+YlkjWo7XU3Aq0vkB9u+cSiYuBjn1sa7dza80e6otvvpqZFFfVGY6/vQ2jwbMkxUjtoV58TiVcdqxNJu2SoYKSZ6LB4reNDxcDlioMyv+m1mym1pipndAWYWrqwD7Qy6FaIwZFQVEyMT5zFLvLOzkr5sHvtXANFnod2kY+l/E9aS1hh6WdJpPn8mImxtqDQ2GCZtV33wzGZzjUG2JxgMuO1Vw7VF6KgTxTAxbvMI5eLA1e5eWffwbotTRgyjMEj8PbgAVTRgFb7XZaS28jwWepSfj6C12e57CYfkrBViu0lpKeEOxSqrakqehpzAefGUovz0ST9QwDvQeH6tlg5JkOu9dl5UvYrd7twL8NRVGHkZS/53KvV35qX9+BKc9v6UfYdu3C0WuhYTA/4dq+iwHLBjIKnsLOa5Sm3+ib3tnjvO1VD4Hamsve4bX/YdpF1OVbhGnH01rZZWOyfOSu0zwTOzz5MlRxqOOVoUvqfpf5/dvD4P5FVC/hhT32PUtAdhyiI+Rx61cmBiO1bx/nbNgcRBguXNvxyue/P+PZHwN5pmas9kvB04uo7v6KBx5cNLydDVgwGRS/5U2e8J7j+lzAOnf3J5ewW5u9+iX/fisY//7MO5x7uVFCeimtWNlaMC3kUrmw9R8NVzcNRSmkl74G9qcomJQQcOnX8CUkEfYfXOTs8Te86s67vwrWr0bZ948xY/R6udcss6IEX/M97Md+Ox3W3RgDLWnp78I82BFrA5fBNqild102a61ryb7Oa9Z+2NKYEl6pn0fLq+9xxr1Qite3Odn0SonXNpewW9/0+qExkGfawX6r94/op5iNqSjKLzH3/icNIX9sw8Xl2c7rYDcYeabj33ndmIqi5A3NJrvsdDwT/ID0LAsyGP+Zp42ZKIZSGj76SFvnZSCv6pBXuV3C3rE99I+E3eyuD+Pr9HY3DW5rMG6nw6vjdKebRPbanazNTvK6apI3fmbC7Yd463gK63udqOpFbLtSeKtwDS9Z+93fuz7B/FAhCyzfo9p2EVV1cuHF2zmybBGrzZ+EWbe3j7XPdZKy/qg77rYf0/HgXRgyNnPir5/mtOrkwskKqPklG/ZpcTk62La0jP0JK7E63SfDTlsFCc0rKXupM8SA/zVWbmwlqfQ1Lcw2Zry1KnQYbd+yGxMo7zmPqp7HMu8ExtwqzGcuBQjQj3XbQyzYfyOrrBfdJ+rO/+DxhD0UlNVjdbjc27y0hmVHbufFC06f/Jfv7cWFC4e1nrJlR0l/sds3jvLXA68NTM6nuruNSr2ewvqTOL2XDYWrv7DlOZX86nfcy5MK6+lxhrn82vosz1lucafnPE3bjz7kweyZZGzu46+ftqKq5zm5aQI1Czez78wlwIXDup2lC/aTsKoVp6q68/n4d2gu8MpnRHUYSfmD68w+VudW0DXv/3BBVVF71zLlwLNsbbcPq/uQ7drRyUtllRxJ/407nsF8b2BvwDWmOpLzN9Hdtg49i6nv+cpnaZR95yGOp6yjV1VRnafZNeMdCn3ybeahrFIs05/A5lRR1W5eTD/KsqDtkSjLt5Xm9/Ws63XitO1hZY5Wz+3/l/enrKVXvYitvYy7JnvNEOpmMff+H9K659/o826bA39k2yYwAAARoklEQVTgYDMsL/o+yRHWS1gRH/t2WlfW0pL0Cw4ELEutTLJf5rP7XtPawDpSjh9ipz148hGHi7hPtNO6cg0vou3PBQvl7CJ76XbffEZdd2/w5POvBW1nEfGr85zky5wxryFrwWGmV1vdebnwG9KPrCZ39T73uCGgAbobfkXusiOD4Zy2J5h+ZDXpC+qwOiaSVrIXZ089hWRR2fZZ8GWPI+77g9BlUHLwY3rqFw8uvYyknCLqPwA4wc63+rTfN//+KlC/OiX6vn+sUYMI8VVQ33TWqFk1neo3fv8fZGO1JqtG7Qy6wfA4fZ1XT9avVku2va/anO5PnLb31W0rZqusaFFtQzGotpYyFQrVypaT6oXB7QrVyrbPosvTN51qza1lasvpLrW+cKFa0/ln9UJnnbqi5n21p6VMvdUrn05bl3rcdtHzl3rhZKO64tZ1att5p1eEf1RbVuSqubkL1XVtp1VnkO3Cx+X+rKTkefWYZzunTT22bYWqp0xtsXly9d9qZ81SdYVXmanqebWndZtaUtainnYOlfmtFKrr2k6r3/TUq4XMVlfUd6kXLhxTa3JXafE51Qud29TCFV5pqk71Qs87ak1Jpdpy2vOZqqq2FnXFj3PV3MJ/UttsF7W6K1FvrWxTz/sU8AW1s2aFWtN5IUgFXLmRtGu3z9S2yiwVvX8dqqqqfqX21C8e/p3zpFpfeKtaWH9SdapO9XzbOlXPYrW+5yuvsNrnAeP1+p4s3/Z6vk2t1Ou1uL3T06v6yjb1fNB4tbwW1qs9ziBh/NPy7LsnTKBc9tSrhSHz6Jfu+Ta1Uu+fjicerYx8yi8QvzgjNazsoqi/cOUZQVkFbkuB24dvubrD6f2Pm4DtLEwdRlL+nvRKhvqGofLDr82Ebte+8UYqQNw+7TV0vofXQZDyC/d9NOXr1z78jwv333+j1nT+d8ByiqxewrX7CNuqTz0GK/MgZRKo/4mkLH3CRdgnevIZsB3G4tgIkI+QZeOb5rB+I1jZBKlb3+9nqyUtpwKEG4ovYF/rI5q+/1a/eEMdo8P7ycDt269OQvYfwfYnXL86wr4/jsKNNWI+892/NpvrFIXrstfSH37zkRvo5Lfm72NafTd6bS90+rtZvf5XFA7beAKF9XU8VZxBIqDT38XP7ruNs/1fjSxtXQpFZTN46dnneP7Z3zPvb+8gyX8T/Wx+oJ/o+YvEjHzuu/tL+r/wPyW7REr5v7Apfwa6INuFj+tzOn77AXebSsnxbKfTk7N6HZsKvWZeBo6zt72A9V5lBsmk3fP3mHI+5GDf0AzU5JotPJk/gwkpd3BPlpFHVswmMVFPyvcmDKW5t4f713uliY7EtCLWmG6n9eDHvjO5/++vKG9YR75+IpBMxvy/4e6z/XwRSXmPa1/S1WPDwed0HmzFrk9l1vSJXt/rSJyZSqa9lYOdn8cwXR3J+Vuw2TaRc/Y9zGYzZvPrNJjuc186jEoiM9NToKuP0wFn3r6m7/13aCWF9JleN+Ak51NtC3JDUHI+1bZOqnM+x2I2u/PXYKIgvZTWwV2Yzh33ZNC64bfs67BgHrZEYSKGO35EbusL1O/7HRZz+8guqYbkqb9YludIuW8CtlVnc9ayX8vDDkwF+ZS2fhkmrF8dRlL+A3/gYLONzLtv9+ovgEQD6Zl67Y/I2rXOMJt7ct9nQ/2bdFjeHKVLwx/Tc9qh5duKfs4spvs0PHcZ2JsP0xnwUv2VlG9kdKkFlJV8wra9x7UlJp9y+NVW0tcsJCdZF1m9hE/lCtqqVm8+ZWkjM92Az611iQbSM78TPJqIwkXTJ+qDtEMbzQf/wEAc6i4yLgY6D9NsNzBn1lTffs8nv8HC3cbds78bIBxaPxSJsdBXEWH/EUbQ35x49P2jK+aD78namu9vOmsIez/llfiin7N/MZkkvz3QJU1m+rCNJzA9pjcnTGRG4Qp++ceXeXb6A/xd2g1+3wdYe6Z8j0U7A11evZO/+dGMEHmLJK6v6D97w/CbIHQ3MXm612df9HP2rVLSE/xviL2R9NLdHDnxWRRl8BX9Zw9Tmn7jsBtsE9If5OUjH/En783vyedHMyYGi+zbQ1sv51tekfyw+g1sI+E4QYPxDpLSl/LcsT8DOiYXbaWzfnGITi1eBuhuKMWQlE7Bc8fcJ+qT5/Ni5ysUen74mUxWxW56dt1Ff0s1iwrSSfJZ2qQjMWs1B3pquav/TTYuyiM9KSGCdfQjFOvyzExlZmI0vZILR3cTRsMk0gte4Fi/C/hLil40U18YzY8zRFb+UQjXrhNzqDjQzq67/puWjSspSJ90ZWtQI8nS1gImDevnQg0+Ylm+Qehu5icPLADtBMDV18bLDSks/9vva4PUGNVLjNqq6+wpPgy5vCQG4UbcJ2rBPzzFWVcc6i4q2pps7/aXcBulrcEKxXMzY3Du/Yww+THd98dCnPv+UTBqt6xOyKqgM2u0Ygdumsz0P5/igguvsyoXDtsn/JFbRjFhTWIOFe/aqADgss8Mrqt3Fw89e57V64+iNnlWRX2K2fibqJOJLK4bmTz9a05duAx6ryp12Pn4j5dJ8fz93duZV9bI+heMpF3xmcg0Zs9bSv36KkqGnXyIoArr6Xm7JAblH87X9O59ktJD82g5vY3iwROfc1hMHwOpo52BkFy9r7O6tIP5LafYUXyLdvLpYsDSShcwZ3DLZNLyi0nLL6ak2n3/w6vP/pqC3D7aujeRn6wjMS2X4rRcikuqcdmt7Hv1WR4uWEpP2zsxfOTVGChPVy97V6/j0PwWTu8oZoanDQ1YMHXZvQstgqgiLf8IRdKuE9PIL04jv/ghql12rPte5dmHC8jtaaM75o+61FNYb+HtaB7DFsPyDU5HcvZPWE4lBzv/npmn3qG18Kc8l3qDloVY1Evs2qpu+izm6OHDKPYw6nDh2k6YiyT6ObOYTjzqLhqLqe/ZGcVv40Smz0pFT1/QLYZfyQlmDPRVcRGvvn90jNEbLiOQnM0viv9AdZ3n6Q4uHL372FhRQ/uVxHvxJL/vcx/tg3cLG80hz0j9uS58zicTbuH2VO3nxNGL5fU97D8U/TMqI4trCjm/+BFHq+uHbmJ0dGPe+Dhr273uFtWlMH/JZ9RvM/veJe7oxmx6IMoXQdxA6vz/xan6FzH73PzpfgFA0YhfgjFA1+9Puc9cB+/6/yVm+3h/zdIUsosK0Xcd54TPHfouHNZnyAv3coOoOTjd8zFk/pDZeq8rDq4v6D93MYbpANxA6tyfDp+Z056kMvxlIC4cp/vowv8S62Uu9IdqNRPRZy1kpXEBensfp84Ov3FOp8+ieKWR5XobH546F8MXecSzPINlwUZPF8Mu47ou9BPdK1wiLP/k71O03DB81tBho6fL0yOOsF3r9GQVP4hxeVZ0M3qR8OT76Ec+T/6Bfqy19wZ/OU3MyjeS/EHzW69i3nOcwvt/TKoORn5c+IthWw3ZBkIs54goXDRtxx4kLoP7RtVY1100SyN8aCdX+pMcPfEn3/7H0UFtXqCn3YQLp+2b/xKeoMZAXwUR9h+xM3p9/+iI6XO+r8tei1Vb8x3wKRWXrdSmapdg/J8s4vVUEe8X8fjG6f3kk2QySp7EdMtbLE1QUJQE0jbbWVRby4rBBB1Ya+/BsKiOnYvytGdwe3/2Pd8nqUzI5IH6H3Ikf5L70pehimNTjLRvX4j+spXajGzW/tfLLDJcNxTuspXa1OswLHqZ/1qbTUatFX7wAPWz93OnZ3nHgnpOTclk3j2HWWS4RyuXTzEb81i0s27os8tWalO/5/PZhIji0pGYsZw6k56WpbPc26U9xSeL/pnXVnifeU9En/8Ij+bBu49lDV4OM6x6E5bU8lT+VC5ba8nwlLnnpKN/LdkZnvp5czBdnf4e/unRv4Z3H2Pm4Bsfy3mDRTQ+pc1k2c0YDYvYuXMRBq2OL1trSfX7zC2RHzxQwZwjPydJUVASsqg4NgVj+1aK9eP9uaI6knPLeH7+ER6u2qGdJHlOGLdDTQVLYnoFQftha91DY/sZd0fkstNR9wQPN5yIYTpuutQiTOv+gq0bt2OxXwIuYW/fQ3PrD6nZUuw3q+X5oXmf5sbfaQOkS9g7dlD18PahE13t0Wx5JvPQ0gRHL4cPWqHkZxSl4n7TX14V5sE1xAP0Hj5MB8WUFaUE7uC8151+6SCyK7CRlqe2thqAr3E4YnjSqP2YtTbvoV0brLjsR6mr+jUNUf2WRVj+TCX3kSrmN1ez2dzt/gF1dGPeXM1Wu1dcEbRr9yPIijB54sGFo/c9DnZASVmBNvgcnk/3+mP39l86vozwB1XL99u/pmpwcmaAXvNWKtYSoD1qYla+4UwhZ8kDpG9bx7rWH3L/3FlaO420XsLHH7tj39MGVlPecCJIGxhpuOj6RPtW73Z4goby1TTPr+KR3KlXWHcB2pluFnPvn4u9+VVe7x4YzNfmjY3h6yF5Lo88P4+3H36COs+TyzyTYaxiy5K0wP1Scja/2JTjF869n1vT1wYPN0x8+/7gIuk/IuXfrzpG1vePITF9zrfq83zvd6nI8jtPm5BFRZ/qt532r6+CrAmezbQX8QT45/vCnWTSirfwrvadrWkVOQbvAYz7JT3usH00Fd/s95l/fBPR56yiyeZJ7yDVJfmkJeqG5X0wXKDPdXpyftWEzZPvd6spyS+ipKnPq1xupripz7esfOLSPosoLndVJqYVU/2uTXvzYhO/ygm0lnwi+qxiKpq6BvNsa6qgOEuPzr/sm4rRe/LUV0HWBE+eh9LV6bMorvDKn62JiuKsodkHfTFNfnXsk4ZXvbvju5tfDebNxrvVJeSnXSPvXtTdQnFdC7vm/RGT4XoUJYGk3P1MK9/D7jU5Ec5qRJwYybnlHGicwwcFM0lQFJSZj/HezUb2tVQykjmd0MnNIH9TI50PfslGw/UoyvUYNn7Jg507WJMV4O6P5Lk8eqCanA+MGBIUFCWLx96binHfa1R6MqfL4MHGPZRP209ukrYmNGkx+6eVcWDTvczQ3UDag3V0lk9hf+4k7WRyErn7p1B+YD0Lg91joEthvmk5l0pvI+GmEvb2RXLFIdLyvIHU+Q+x7tIG0hNSWLS3L4YzMFPJffQFGnP+jQLD9SiKwszHPuBm4wu0VEa5xi+S8gd0MxZS176GafsXu0+I057i4/yHqCn03ih8u9alLaOxs2wonsFtXmbTwluC/hC5T+rOU5p+Ezct+lffR/SF4M53HfPOPqntn9YugrVHIKblGzp3JP5gPssL9ehLfkZRqtdvVoT1Ei7+WB777rKsJdMzKZK0mmN3llBTGOKGy0jDRdwn6smt/Dvu/Pgp0hQFJennHMmspb1uobbE5Mrqbng7u4G0JZtoXXOZDbdNQlFmsqD+CxY9vj7AAx38TWRG8Rbad83jrCnLXf5av9W5exVZQe/zSCaj5Bm/cP9Iz7w6eg6sDhFu2N7Et+8PlZNI+o+I+Perp0kN2/e7n4uuxPyqcmwo2iNRhn+hKAT5auxyncGyoYyNU57gQEWsBzPjzSXslqdYujGZ2qgO3GvbuGzXQowlrm4a5pfSY9o/5tdVimvAgAVTxjI+3BTlGn4xNn1L+o9wY41x3o79Xmuf8FOapq0bhVnEccLzQhtFQVGuJ6tpKtUhz7SFECIY7c2Lxia6B9+Iaqej7ik2XPo7luRMubrZE0KMYdJ/hDLOR2X+y126aPrV3b7PlPw28V7i4VmGo5fH+wkhRmIquY++zPOZFvI9y34SCtnuvI8DclIvhAhJ+o9Qrq1lJ0KEIe1aCCGEEKPpGl92IoQQQgghxPghg28hhBBCCCHiRAbfQgghhBBCxIkMvoUQQgghhIgTGXwLIYQQQggRJzL4FkIIIYQQIk4mBPpQURSf/wpxLZF2LYQQQoirJeDgW56DLIQQQgghROzJshMhhBBCCCHiRAbfQgghhBBCxIkMvoUQQgghhIgTGXwLIYQQQggRJzL4FkIIIYQQIk5k8C2EEEIIIUScyOBbCCGEEEKIOPn/N/CuSPBvag4AAAAASUVORK5CYII=\"/></p>\n<p>The code below outlines the <code>Arrival</code> class used in this program.</p>\n<pre><strong>public class</strong> Flight<br/>{ <strong>private</strong> String id;<br/><strong>  public</strong> String getId() {<strong>return this</strong>.id;}<br/>  // ... more variables, accessor and mutator methods<br/>}<br/><strong><br/>public class</strong> Arrival<br/>{ <strong>private</strong> Flight myFlight;<br/><strong>  private</strong> String sta;     // Scheduled Time of Arrival (\"hh:mm\")<br/><strong>  private</strong> <strong>int</strong> runway;<br/><strong>  private</strong> String gate;<br/><strong>  private int</strong> delay;<br/><strong>  private boolean</strong> landed;<br/><br/><strong>  public</strong> Arrival(Flight myFlight, String sta)<br/>  { <strong>this.</strong>myFlight = myFlight;<br/><strong>    this.</strong>sta = sta;<br/><strong>    this.</strong>runway = 0;<br/><strong>    this.</strong>gate = <strong>null</strong>;<br/><strong>    this.</strong>delay = 0;<br/><strong>    this.</strong>landed = <strong>false</strong>;<br/>  }<br/><br/><strong>  public void</strong> addDelay(<strong>int</strong> newDelay)<br/>  { <strong>this.</strong>delay = newDelay;<br/>  }<br/><br/><strong>  public</strong> String getETA()<br/>  { // calculates the Estimated Time of Arrival (ETA) of the flight<br/>    // by adding the delay to the sta and returning the result as a<br/>    // String (\"hh:mm\")<br/>  }<br/><br/><strong>  public int</strong> compareWith(String flightID)<br/>  { <strong>if</strong> (myFlight.getID().equals(flightID)) { <strong>return</strong> 0; }<br/><strong>    else</strong> { <strong>return</strong> 1; }<br/>  }<br/><br/><strong>  public int</strong> compareWith(Arrival anotherArrival)<br/>  { // missing code<br/>  }<br/><br/>  // ... plus accessor and mutator methods<br/>}</pre>\n</div><div class=\"specification\">\n<p>The code below outlines part of the <code>FlightManagement</code> class used in this program.</p>\n<p>For the purposes of this exam only arriving flights are being considered.</p>\n<pre><strong>public class</strong> FlightManagement<br/>{<br/><strong>  private</strong> Arrival[] inbound;      // array of inbound airplanes<br/><strong>  private int</strong> last = -1;          // index of last used entry<br/><strong><br/>  public</strong> FlightManagement()<br/>  { inbound = <strong>new</strong> Arrival[200];<br/>  }<br/><strong><br/>  public void</strong> add(Arrival newArrival)<br/>  { // missing code that adds the newArrival to the array inbound<br/>    // sorted by ETA, and updates last<br/>  }<br/><br/>  <strong>private int</strong> search (String flightID)<br/>  { // missing code that searches the array inbound and<br/>    // returns the index of the Arrival object with flightID<br/>  }<br/><br/>  <strong>public</strong> Arrival remove(String flightID)<br/>  { Arrival result;<br/>    <strong>int</strong> index = search(flightID);<br/>    result = inbound[index];<br/><strong>    while</strong> (index &lt; last)<br/>    { inbound[index] = inbound[index + 1];<br/>      index++;<br/>    }<br/>    last--;<br/>    <strong>return</strong> result;<br/>  }<br/><br/>  // ... many more methods<br/>}</pre>\n<p>The method <code>search</code> in the <code>FlightManagement</code> class searches the array <code>inbound</code> and returns the index of the <code>Arrival</code> object with the given <code>flightID</code>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the general nature of an object.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> disadvantages of using Object Oriented Programming (OOP).</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of using modularity in program development.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the relationship between the <code>Flight</code> object and the <code>Arrival</code> object.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a UML diagram to represent the <code>Arrival</code> object.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>search</code> implementing a linear search. Use the first <code>compareWith()</code> method in the <code>Arrival</code> object.</p>\n<p>You may assume that an <code>Arrival</code> object with the given <code>flightID</code> exists in the array <code>inbound</code>.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of using a binary search.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> disadvantage of using a binary search.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1]</strong> for each part of a suitable definition up to <strong>[2 max]</strong></em>.<br/>An object is an abstract entity;<br/>and its components are data and/or actions;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1]</strong> for identifying each disadvantage and <strong>[1]</strong> for an elaboration of this disadvantage, up to <strong>[2 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.<br/>Unsuitable for minor projects;<br/>since OOP increases complexity for little gain;</p>\n<p>OOP programs are larger than other programs;<br/>and therefore slower;</p>\n<p>OOP programs take more effort to construct;<br/>because of the decomposition needed to achieve abstraction;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying an advantage and <strong>[1]</strong> for an elaboration of this advantage up to <strong>[2 max]</strong></em>.</p>\n<p><em><strong>Example answers</strong></em>:<br/>Faster development;<br/>Because different programming teams can work on different modules;</p>\n<p>Easier to debug;<br/>Because the smaller modules will have fewer mistakes than one big program;</p>\n<p>Easier to update (in the future);<br/>Because it is easier to update a module than the full program;</p>\n<p>Re-usability;<br/>Modules can be stored in libraries and reused for different programs;</p>\n<div class=\"question_part_label\">a.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1]</strong> for any indication of aggregation</em>.</p>\n<p><em><strong>Example answer</strong></em>:<br/>The <code>Arrival</code> object has/stores a <code>Flight</code> object;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for including 3 distinct sections.</em><br/><em>Award <strong>[1]</strong> for including a component with all variables.</em><br/><em>Award <strong>[1]</strong> for including a component with all listed methods.</em><br/><em>Award <strong>[1]</strong> for indicating private/public using + / −.</em><br/><em>Don't penalize the absence of accessor and mutator methods.</em></p>\n<p><em><strong>Example answer</strong></em>:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for a while loop.</em><br/><em>Award <strong>[1]</strong> for correctly testing the <code>flightID</code> using <code>compareWith()</code>.</em><br/><em>Award <strong>[1]</strong> for incrementing the loop counter.</em><br/><em>Award <strong>[1]</strong> for a return statement.</em></p>\n<p><em><strong>Example answer</strong></em>:</p>\n<pre><strong>private int</strong> search(String flightID)<br/>{   <strong>int</strong> i = 0;<br/><strong>    while</strong> (inbound[i].compareWith(flightID) != 0)<br/>    { i++; }<br/><strong>    return</strong> i;<br/>}</pre>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for identifying an advantage of using a binary search and <strong>[1]</strong> for an elaboration of the advantage up to <strong>[2 max]</strong></em>.</p>\n<p>Binary search is much faster than sequential search;<br/>Because it halves the search range for every comparison;</p>\n<div class=\"question_part_label\">e.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for identifying a disadvantage of using a binary search and <strong>[1]</strong> for an elaboration of the disadvantage up to <strong>[2 max]</strong></em>.</p>\n<p>However, it is not applicable to unsorted data sets;<br/>Because the data must be sorted first which adds to computational cost;</p>\n<div class=\"question_part_label\">e.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.ii.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.10",
    "topics": [
      "option-d-object-oriented-programming",
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "d-2-features-of-oop",
      "d-1-objects-as-a-programming-concept",
      "d-3-program-development",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Weather forecasters use computer models which are able to simulate future weather patterns. These forecasts were originally limited to the near future. However, modern systems can now produce long range forecasts.</p>\n</div><div class=\"specification\">\n<p>The simulation of the weather forecasting models produces specific data which can be output in a variety of ways.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest <strong>two</strong> reasons why these simulations have improved both in their accuracy and their range.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest why forecasts become less accurate the more long range they become.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss whether historical data can be accurately used to forecast future weather.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>visualization</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With the help of examples, discuss how the development in the way such data is visualized has made the results of these simulations more accessible to the general public.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Modern computer systems have become increasingly more powerful;<br/>Which allows more complicated systems to be simulated;<br/>In a short/acceptable period;<br/>The understanding of (the science of) weather has steadily improved/more historic weather patterns can be accessed;<br/>Allowing more accurate modelling to take place;<br/>More data can now be retrieved;<br/>Through satellites, ground stations etc.;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Only the major/known variables can be input into the model;<br/>Approximations have to be made to represent complex processes;<br/>These or other minor/unknown variables will not have a significant effect in the short term;<br/>But will have (unknown) effects in the long-term/small errors have a cumulative effect over the long-term/butterfly effect;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each valid point discussed, and a further <strong>[1]</strong> up to <strong>[5 max]</strong> for a good expansion of this point</em>.</p>\n<p>Investigate past weather patterns in order to see if past forecasts were correct or not and make adjustments to your model appropriately;<br/>Look at specific events in the past (e.g. appearance of El Nino) to see how they affected future weather patterns in different areas, and then apply this to new occurrences of these events;<br/>However, historical data does not take into account new factors, such as how carbon emissions are contributing to global warming;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The representation of (abstract) data;<br/>In a way that is understandable by humans;</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Standard simulations produce numerical values (which are not easily understood by the general public);<br/><em>Modern simulations include</em><br/>Graphics that are related to the data output;<br/>e.g. rain drops/a smiling sun;<br/>CGI/animation can produce motion;<br/>e.g. showing a belt of rain crossing the country;<br/>Which are more easily understood by the general public;</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.ii.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.6",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations",
      "b-3-visualization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The following flowchart represents a standard algorithm:</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the table that traces the algorithm in the flowchart using an input value of 19.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the purpose of the algorithm.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the algorithm from the flowchart using pseudocode. Add additional pseudocode to ensure that input is validated to only allow positive integers to be entered.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Efficiency is an important consideration when designing algorithms to ensure they don’t waste computer resources such as memory or processing time.</p>\n<p>Suggest <strong>two</strong> design considerations that could be made to an algorithm that would make it more efficient.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p><em><strong><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></strong></em></p>\n<p><em>Award up to <strong>[4 max]</strong></em>:<br/>Correct table with correct first row;<br/>Rest of NUMBER column correct;<br/>Rest of DIGIT column correct;<br/>Rest of OUTPUT column correct;</p>\n<p> </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>Converts denary number to (reverse) binary.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max] </strong></em><br/>Validation to check for positive numbers;<br/>Validation to check for integers;<br/>Loop for validation rules;<br/>Loop structure to continue while NUMBER &gt; 1;<br/>Correct calculations/outputs inside loop;<br/>Correct output of last digit outside loop;</p>\n<p><em>Example answer</em>:</p>\n<pre>input NUMBER <br/>loop while NUMBER &lt;= 0 OR NUMBER ≠ NUMBER div 1 <br/>    output \"Please input another number, your entry is invalid \" <br/>    input number <br/>end loop <br/>loop while NUMBER &gt; 1<br/>    DIGIT = NUMBER mod 2<br/>    output DIGIT<br/>    NUMBER = NUMBER div 2<br/>end loop<br/>output NUMBER</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>The algorithm could use loops;<br/>To remove the necessity to process extra lines of code;</p>\n<p>Use of arrays/data structures;<br/>So data can be stored/re-used/re-entered;</p>\n<p>Use of flags;<br/>To stop a search routine when an item has been found so that all elements don’t have to be searched;</p>\n<p><em>Mark as 2 and 2</em><em> </em></p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>A mixed set of responses was seen for this question, which was a trace table for a given algorithm presented as a flowchart. Most candidates achieved at least one mark, but many marks were lost through incomplete trace tables, or candidates not writing the complete output that the algorithm would produce.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A significant minority of candidates recognised that the algorithm was converting a denary number to binary. Other candidates described the process, almost line by line, that the algorithm followed, which is not the purpose of the algorithm.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A mixed set of results was seen for this question with most candidates achieving some of the marks. Many of these candidates provided a working loop with the correct calculations and outputs inside and outside of the loop. Some of these candidates also demonstrated some of the validation that was required, either to only allow positive numbers into the algorithm or making sure the number was an integer. Very few candidates checked for both of these things, and even fewer allowed for the number to be re-input if it wasn’t a positive integer, but simply stopped the algorithm.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates performed less well on this question which required candidates to describe aspects of program design that would make an algorithm more efficient, such as use of loops to avoid repeated lines of code, or use of flags to stop a loop, to prevent unnecessary iterations.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "21M.1.SL.TZ0.14",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Smart technologies can be used to illuminate the night. Street light intensity and coverage can both be controlled, depending on physical parameters, such as the time of the day, or weather conditions.</p>\n<p>The amount of pedestrians in the street or their activities could be further parameters used by algorithms for these type of applications.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss the technologies that ambient intelligence uses in this specific scenario.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With a focus on privacy, discuss how traffic data from WiFi communication through smartphones can be used in this scenario.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Sensors;<br/>Used to receive input for a physical parameter, such as daylight;<br/>Transceivers/microcontrollers;<br/>Directly on the lamppost, to be controlled remotely;<br/>Wireless network/remote control antenna/RFID;<br/>Position in each lamp along a street (or just the first), to communicate with both the central service centre (or all neighbouring lamps having a RF and communicate pairwise);<br/>Internet/servers;<br/>To connect the monitoring centre to the lamps; </p>\n<p><em><strong>Note</strong>: Do not accept: </em>LED light and permanent supply of power, as they are not directly related to CS/IT.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[4 max]</strong></em>.<br/><em>Award up to <strong>[2 max]</strong> for describing how the smartphones/WiFi could be used for street illumination</em>.<br/><em>Award up to <strong>[2 max]</strong> for a discussion on privacy/monitoring based on the data/algorithms used</em>.</p>\n<p><em><strong>For example</strong></em></p>\n<p><em><strong>How smartphones/WiFi is used in the scenario</strong></em><br/>Hot-spot traffic may provide an indication of how large the crowd moving in the street/zone is;<br/>Hopping from one connection to another may provide an indication of how and where the crowd may move;<br/>Therefore, this data may contribute to optimize the algorithm, which in turn could impact on the cost of public illumination, so it is an advantage for the collectivity;</p>\n<p><em><strong>Discussion on privacy/monitoring</strong></em><br/>Data on hotspots are provided by telecom companies;<br/>These data should already be the result of some analysis and be stripped of any private (personal) information;</p>\n<p>However, the company that controls the illumination would still have information on where the crowd is (in addition to the telecom) and this poses an issue of crowd monitoring;</p>\n<p>Worse, hotspots integrated directly on lampposts would provide a very fine-grained anchoring net, and presence/movement of individuals rather than crowds could be monitored (not their private information, necessarily);</p>\n<p>In both cases, private data shall not be made available and authorities and protocols should be used to guarantee which level of service these ubiquitous solutions provide;</p>\n<p>Governments may obtain this information to track the whereabouts of individuals;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "17N.2.HL.TZ0.14",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-6-the-intelligent-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>WineForAll</em> is a retailer that sells wine in its stores. Each store sells wine from a number of vineyards.</p>\n<p>The following extract from the <strong>Wine</strong> file contains unnormalized data.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>record</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the steps to create a query to find the vineyards and names of fruity wines where the quantity in stock is between 25 and 35 bottles.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the steps to create a non-persistent derived field called <code>TotalPrice</code>, which would hold the total value of wine stored for each record.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why the inclusion of a derived field will not affect the normalization of a database.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the 3rd Normal Form (3NF) of the unnormalized <strong>Wine</strong> file.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why a single-field primary key is not always an appropriate solution for normalized databases.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>A record is one row in a table;<br/>All of the fields relating to item of information;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Fields and Table: Vineyard and NameOfWine FROM Wine;<br/>Criteria: StockQty &gt;= 25 AND StockQty &lt;= 35; <em>note: allow &gt;25, &lt;35</em><br/>Description: Fruit is extracted from the field.<br/>(<em>Do not award the mark if Description = Fruity</em>)</p>\n<p>SELECT Vineyard, NameOfWine FROM Wine<br/>WHERE (StockQty &gt;= 25 AND StockQty &lt;= 35)<br/>AND Description LIKE ‘*Fruity*’;</p>\n<p><em>Accept other versions including</em>:</p>\n<p>SELECT Vineyard, NameOfWine FROM Wine<br/>WHERE (StockQty BETWEEN 25 AND 35)<br/>AND Description LIKE ‘*Fruity*’;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>All three points needed for <strong>[2]</strong>. Two points needed for <strong>[1]</strong></em>.<br/>UnitPrice * StockQty;<br/>Totalprice named;<br/>WINE table named;</p>\n<p><em>Accept versions similar to the one shown below</em>:</p>\n<p>SELECT UnitPrice * StockQty AS totalprice<br/>FROM Wine;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>A derived field is created / does not exist in the table/ is temporary;<br/>So the rules of normalization do not apply / is not affected by duplication / redundancy;<br/>No new dependencies are created;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p>Example of solution (accept different names for tables)</p>\n<p>Vineyard (<span style=\"text-decoration:underline;\">Vineyard</span>, Region)<br/>Wine (<span style=\"text-decoration:underline;\">WineID</span>, Wine, Vineyard<sup>(fk)</sup>, Year, Flavour, APV, UnitPrice)<br/>Stock (<span style=\"text-decoration:underline;\">StoreID</span>, <span style=\"text-decoration:underline;\">WineID</span><sup>(fk)</sup>, StockQty)</p>\n<p><strong>Vineyard table</strong> e.g. Vineyard (<span style=\"text-decoration:underline;\">Vineyard</span>, Region)<br/>Award <strong>[1]</strong> for primary key. Either Vineyard or VineyardID.</p>\n<p><strong>Wine table</strong> e.g. Wine (<span style=\"text-decoration:underline;\">WineID</span>, Wine, Vineyard<sup><span style=\"text-decoration:underline;\">(fk)</span></sup>, Year, Flavour, APV, UnitPrice)<br/>Award <strong>[1]</strong> for primary key WineID or composite Wine/Vineyard<br/>Award <strong>[1]</strong> for identifying the foreign key (Vineyard)<br/>Award <strong>[1]</strong> for splitting the three description fields</p>\n<p><strong>Stock table</strong> e.g. Stock (<span style=\"text-decoration:underline;\">StoreID</span>, <span style=\"text-decoration:underline;\">WineID</span><sup>(fk)</sup>, StockQty)<br/>Award <strong>[1]</strong> for composite key StoreID/WineID or Store/Wine/Vineyard<br/>Award <strong>[1]</strong> for WineID or equivalent key shown as foreign key</p>\n<p><em><strong>Note</strong>: Should candidates provide other reasonable solutions, please contact your team leader</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Relationship 1-1 might not exist;<br/>Which means a single-field PK might not uniquely identify a record;<br/>So a composite key is needed made up of 2 or more fields;<br/>Allow any suitable example;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a very straight-forward question.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were well-prepared with many providing actual queries. However, many did not achieve full marks for incorrectly identifying the adequate step to filter <em>fruity wines</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was generally well-answered as many candidates were able to identify the steps for creating the derived field. </p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In the second part of this question, many students failed to relate derived fields to normalization.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students were able to split the tables but only a few identified the foreign keys and correctly relate the tables to each other. Many students also identified that data needed to be atomic and were able to include this in the response. Candidates were not required to re-write the table.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some responses were reasonable, but many candidates were not aware of the use of composite keys/limitations of primary keys in normalized databases.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.3",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A charitable organisation, <em>Harvest</em>, which operates in a less economically developed country (LEDC), uses old computers that have been donated to them. The organization relies on WiFi and satellite-based transmission for access to the Internet.</p>\n</div><div class=\"specification\">\n<p><em>Harvest</em> is considering using a cloud-based service provided by a large IT company based in one of the more economically developed countries (MEDC).</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> technical challenge that <em>Harvest</em> may encounter in setting up its own website with the technologies that it has available.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the use of cloud-based services may help <em>Harvest</em> to overcome the limitations of the available technologies.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> problems this may cause for an organizsation such as <em>Harvest</em>.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p><em>Harvest</em> wishes to improve its ranking in the major search engines.<br/><br/>Discuss whether <em>Harvest</em> should use black hat search engine optimization techniques in order to improve its ranking.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for describing the challenge resulting from some limitation and <strong>[1]</strong> expansion, possibly with example up to <strong>[2 max]</strong></em>.<br/>Old computers impose <span style=\"text-decoration:underline;\">memory and processing constraints</span>;<br/>It is likely that they cannot make sophisticated web pages;<br/>For example, they may need to reduce the number of interactions/transmissions;<br/>By deciding not to have dynamic web pages;<br/>To the purpose of not generating loss of processing power on their side;</p>\n<p><span style=\"text-decoration:underline;\">The software might not be up-to-date/the OS is old</span>;<br/>This poses a threat for viruses or deliberate attacks (attack the weakest nodes);<br/>To the point that not even the charities will link to their website, rather just listing the name of the association;<br/>To limit the risk of propagating malware;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each reason how the use of cloud-based services may help Harvest to overcome the limitations of the available technologies up to <strong>[4 max]</strong></em>.<br/>They can host the website on the cloud;<br/>Enabling a sophisticated site to be created / bypassing their own limitations;<br/>The cloud could store all of their data;<br/>As they will have limited storage on their own equipment;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying how the use of cloud-based services may cause problems and <strong>[1]</strong> for an expansion / development up to <strong>[2 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>. <br/>The costs are likely to be high;<br/>And <em>Harvest</em> is clearly a company with limited funds;</p>\n<p>The cloud company will be bound by rules and regulations in the country in which it is registered;<br/>These may cause conflicts with regard to the type of information that <em>Harvest</em> deals with;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[5 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying an advantage of using black hat techniques and an additional <strong>[1]</strong> for each development of that advantage up to<strong> [2 max]</strong>.</em><br/><em>Award [1] for identifying a disadvantage of using black hat techniques and an additional <strong>[1]</strong> for each development of that disadvantage up to<strong> [2 max]</strong>.</em></p>\n<p><em>Award <strong>[1]</strong> for a valid conclusion.</em></p>\n<p><strong>Advantages</strong>:<br/>The use of black hat techniques + an example (e.g. hidden links, or reciprocal links, keyword stuffing, hidden content, content theft, etc.);<br/>May temporarily improve its ranking;</p>\n<p><strong>Disadvantages</strong>:<br/>However, this is unethical;<br/>This may not sit well with its image as a charity;</p>\n<p><strong>Conclusion</strong>:<br/>Search engines will eventually discover that these techniques are being used;<br/>This may lead to the site being banned;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.9",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-4-the-evolving-web",
      "c-2-searching-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>BuildYourWebSite</em> is an online company that provides a number of common templates for building your own website. Each template includes one HTML file, one CSS file, a folder of web images, and a folder of special sound effects.</p>\n</div><div class=\"specification\">\n<p>Each template can be downloaded as a single compressed file, using a web browser.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:left;\">Identify <strong>two</strong> characteristics of HTML.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:left;\">Discuss the benefits and disadvantages of the template including a CSS file in addition to the HTML page.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why TCP/IP is a reliable protocol in relation to downloading operations.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate lossy compression and lossless compression when used to download files.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Mark-up language;<br/>Can contain scripting commands/code;<br/>Can imbed objects;<br/>Uses tags to structure page;<br/>Provides constructs to build hyperlinks;<br/>Can be integrated with CSS and JavaScript;<br/><em><strong>Note</strong>: Accept other reasonable characteristics</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying an advantage, <strong>[1]</strong> for identifying a disadvantage, and a further <strong>[4 max]</strong> for expanding on these advantages/disadvantages</em>.</p>\n<p><em><strong>Advantages</strong></em><br/>It saves time (in web development);<br/>Because modification of style needs to only be made to the CSS;<br/>It saves space;<br/>Because the code (of the style) is not repeated in each HTML file;<br/>Quicker load of many pages / Quicker download of many pages from the same site;<br/>HTML files are smaller;<br/>It gives a uniform appearance to the entire website;<br/>Which makes it more attractive/appealing/easy to navigate;<br/>It can be integrated with outside sources (e.g. RSS feeds);<br/>Which makes the website more interactive/dynamic for the user;<br/>Quicker to make changes to layout/formatting/page positioning;<br/>Therefore, it helps separating the jobs/tasks of illustrators (designer) from those that produce content;<br/>Increases accessibility of authoring to non-experts;<br/>Supported by most browsers;<br/>Increasing the number of users;</p>\n<p><em><strong>Disadvantages</strong></em>:<br/>Downloading pages from different sites can be slow (general browsing);<br/>Because each page may have its own CSS;<br/>(However, downloading many pages from the same site is instead faster;)<br/>CSS syntax is different from HTML and can be slightly ambiguous;<br/>Not a user-friendly language (but available as IDE) that the developer needs to learn;<br/>(e.g. the same cascade style name may have slightly different effects when used by different browsers;)<br/>An HTML page saved without CSS, and seen offline, will not display nicely;<br/>Because it would require access to external files, including CSS (including images, video, sound);<br/>Anybody with read/write permission to the CSS can easily override just the CSS;<br/>Showing unwanted information to an entire web-site (risk of hacking in just one point/internal threat with high risk);<br/>Some browsers do not support exactly the same CSS (some styles/fonts for example);<br/>The view can be slightly different using different browsers/risk of slight incompatibility;<br/>(For large, corporate sites) Integration of HTML + CSS with other content management systems (CMS) may be tricky;<br/>Because CMS also use their own CSS, and there can be collisions, and it requires technical competence;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>(When a request of download is received at the Application level (e.g. HTTP with a browser), appropriate data packets are generated and sent to the transport layer/TCP;)</em></p>\n<p>TCP uses the header of the data packets to order them and to <strong>check</strong> their contents;<br/>And <strong>sends an acknowledgement</strong> signal to the transmitter upon correct reception, before preparing a new data packet with a further header for the IP/Internet level;<br/>If the transmitter does not receive the acknowledgement from the TCP, the <strong>transmitter re-sends</strong> the packet;<br/>Therefore, all correct packets will be available to the TCP at some point to be made available to the IP and specifically to the IP destination/receiver (in the header);<br/>IP ensures that the package reaches the correct address;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1 max]</strong> each for outlining the <strong>two</strong> techniques (lossy and lossless)</em>.<br/><em>Award <strong>[up to 2 max]</strong> for relating both to actual applications (<strong>[1]</strong> if related to file </em><em>types instead)</em>.</p>\n<p>Images and audio can be compressed using lossy techniques;<br/>This will create a smaller file/faster transmission time;<br/>But the data loss will not seriously affect quality;</p>\n<p>However, CSS and HTML files might lose possible scripting;<br/>As you need to recover the original data;<br/>Lossless compression is more suitable;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.7",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The programming team have decided to replace the array of <code>Item</code> objects <code>(pl)</code> and the array of <code>Payment</code> objects <code>(tables)</code> with either a linked list or an ArrayList.</p>\n</div><div class=\"specification\">\n<p>The array of <code>Item</code> objects <code>(pl)</code> is replaced by <code>priceLL</code>, an object of the LinkedList library class. The <code>Item</code> objects can be created, deleted or amended.</p>\n<p>The method <code>changePrice()</code> allows an item, identified by its item code, to have its price changed. All data required by this method are passed as parameters. If the item is not found then an appropriate message is displayed.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> benefit that the use of either of these choices will have over the original array structures.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>changePrice()</code>.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how a binary tree structure would allow a more efficient search for the <code>Item</code> object.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Memory space for the exact number of objects can be assigned;<br/>Whereas the array will (inevitably) waste space/allot more memory than is needed/may run out of allotted memory/there is no need to determine array size;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows, up to <strong>[6 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for correct signature.</em><br/><em>Award <strong>[1]</strong> for correct initialization of the length of the loop/size of the linked list/ </em><em>Boolean/Iterator object as appropriate to the solution and appropriate message </em><em>displayed at end;</em><br/><em>Award <strong>[1]</strong> for correct loop.</em><br/><em>Award <strong>[1]</strong> for correct comparison, with or without <code>get</code> methods.</em><br/><em>Award <strong>[1]</strong> for correct updating, with or without <code>get</code> methods.</em><br/><em>Award <strong>[1]</strong> for early exit if found.</em><br/><em>Award <strong>[1]</strong> for answer completely correct.</em></p>\n<p><strong><em>Example 1</em></strong></p>\n<pre>public void changePrice(LinkedList&lt;Item&gt; pll, String c,<br/>                          double newPrice)<br/>{<br/>  int i = 0;<br/>  boolean found = false;<br/>  int size = pll.size();<br/>  while (i &lt; size &amp;&amp; !found)<br/>  {<br/>    if (pll.get(i).getCode().equals(c)) // allow “=”<br/>    {<br/>      pll.get(i).setPrice(newPrice);<br/>      found = true;<br/>    }<br/>    i = i + 1;<br/>  }<br/>}</pre>\n<p><strong><em>Example 2</em></strong></p>\n<pre>public void changePrice(LinkedList&lt;Item&gt; pll, String c,<br/>                          double newPrice)<br/>{<br/>  Iterator&lt;Item&gt; iter = pll.iterator();<br/>  boolean found = false;<br/>  while (iter.hasNext() &amp;&amp; !found)<br/>  {<br/>    Item current = iter.next();<br/>    if (current.getCode().equals(c)) // allow “=”<br/>    {<br/>      current.setPrice(newPrice);<br/>      found = true;<br/>    }<br/>  }<br/>}</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The <code>pl</code> array/<code>Item</code> objects could be read into a binary tree;<br/>And placed in order of the <code>Item</code> code;<br/>(Successive) comparisons between the search item and tree item will reduce the search space by a half (each time);<br/>Which results in a faster search than a linear search/<code>Olog(n)</code> is better than <code>O(n)</code>;<br/>As a linear search might have to loop through the whole list;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "17N.2.HL.TZ0.18",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>one</strong> advantage of using a dedicated operating system on a mobile phone.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max]</strong></em><br/>Makes maximum use of the available (limited) memory to provide the features needed for the phone;<br/>Does not waste memory space with unwanted/inappropriate features;<br/>Ensures a higher level of security;<br/>Makes efficient use of hardware equipment;<br/>Suited to available hardware equipment;</p>\n<p><em><strong>Note</strong>: Accept examples, GUI will fit right with the screen resolution /can make running the device easier to use / better suited to their audience/ it is faster than universal OS's, etc</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Many candidates correctly answered this question.</p>\n</div>",
    "question_id": "21M.1.HL.TZ0.9",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the use of data-hiding as a security feature in OOP.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The <code>Arrival</code> object has two methods named <code>compareWith</code>.</p>\n<p>Outline why calling the <code>compareWith</code> method does not cause a conflict.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the name of this OOP property.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for stating encapsulation as an OOP property.</em><br/><em>Award <strong>[1]</strong> for an elaboration</em>.</p>\n<p><em><strong>Example answer:</strong></em><br/>Encapsulation allows to make instance variables (and methods) of a class private to that class;<br/>So that the main program / other classes can't accidentally access / change the data in an object;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em><br/>The two methods have different parameters;<br/>allowing the compiler to choose the correct one.</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Polymorphism</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.11",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-2-features-of-oop"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A company that provides training for teachers plans to set up a training room in its offices with a network of 15 computers. Each computer has 1 TB of storage and 16 GB of random access memory (RAM).</p>\n</div><div class=\"specification\">\n<p>In order to minimize costs, the company decided only to install general application software on the training computers.</p>\n</div><div class=\"specification\">\n<p>The company has decided to allow the teachers to use their own devices in its training room by adding wireless networking.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics of RAM.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the purpose of persistent storage on the computers.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> types of general application software that would be installed on the training computers.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage to the company of implementing this change.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> disadvantage to the company of implementing this change.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>one</strong> method of security that may be used on this wireless network.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the speed of data transmission on the wireless network in the training room may vary.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>RAM is volatile / contents erased when power is switched off;<br/>Access speed is fast / faster than hard drive;<br/>Data / instructions can be read from and written to it / /RAM can be overwritten;<br/>Size is limited;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>To store programs / files / data in a non-volatile device so it isn’t lost;<br/>Stores more data as it has a larger capacity;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Word processor;<br/>Spreadsheet;<br/>Database management system;<br/>Email;<br/>Web browser;</p>\n<p><em>Allow any general purpose application that is appropriate for a ‘training room computer’</em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>May save money;<br/>Due to not having to supply all the training computers;</p>\n<p>May be able to increase the size of the training group;<br/>Which may generate more income;</p>\n<p>Trainees / teachers likely to be more familiar with software on own machine (and how new training software interacts with OS / user interface);<br/>Making training sessions more efficient / allowing trainer to concentrate on the training rather than using generic applications;</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em><br/>May cause security issues;<br/>Due to multiple users having network access from their “unsecured” devices;</p>\n<p>May interfere with running of training sessions;<br/>As some machines may not be compatible;</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award </em><strong><em>[2 max]</em> </strong><br/>Encryption;<strong><br/></strong>Scrambles the contents of the network transmissions so that if they are intercepted they can’t be understood (without the decryption key);</p>\n<p>User ID (and password);<br/>Only allows authorized users to access the network;</p>\n<p>Media Access Control (MAC) addresses;<br/>Unique identification codes embedded in networkable equipment so that only authorized equipment may access the network;</p>\n<p>Firewall;<br/>Checks traffic coming into the network and leaving the network, and can block suspicious data;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>The speed of data transmission (on a wireless network) slows down;<br/>The further the receiver is from the transmitter;</p>\n<p>Passing through obstructions such as solid walls;<br/>Can slow down transmissions (on a wireless network);</p>\n<p>The bandwidth available for transmission on a wireless network is finite;<br/>So, transmission speeds can be affected if the number of users on the network increases;</p>\n<p><em>Note to examiners: Answers must relate to wireless networks and not be a comparison between cabled and wireless networks</em>.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates often named uses of RAM, but the question asked for characteristics of RAM, so therefore lost out as a consequence. However, many recognised it is volatile and can be overwritten.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to state the purpose of persistent storage, but a significant number mistook this for ROM, so gave the purpose of that instead.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This part was not answered as well as it could have been. Candidates were asked to name two types of general-purpose applications software suitable for a training room, with the most likely answers being word processor and spreadsheet. However, candidates provided a wide range of incorrect answers that were, for example, utilities, system software or translation software.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates recognised that if the teachers could bring their own devices to the training room it would save the training company money, as they wouldn’t need to provide so many training computers. Or, that their training sessions may be more efficient, as the teachers were already familiar with their own machines, so they could get straight into the training session rather than learning how to use the machine. Unfortunately, many candidates gave responses that were benefits to the teacher and generally within their teaching environment rather than in the training room, such as being able to connect anywhere in their classroom.</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates often gave responses that were generic disadvantages of wireless networks, and sometimes, when compared to cabled networks, rather than relating their answers to the given scenario. Some candidates did, however, state that one of the teacher’s devices may be infected with malware which could be uploaded, or they may be incompatible with the training software.</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Well answered, with the full range of possible answers seen.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Generally well answered, with the full range of possible answers seen.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19M.1.SL.TZ0.15",
    "topics": [
      "topic-2-computer-organization",
      "topic-3-networks"
    ],
    "subtopics": [
      "2-1-computer-organization",
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> characteristics of a dynamic data structure.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max] </strong></em><br/>Dynamic data structure does not have predetermined size/ allows memory use to change as needed (no fixed size) / if more space is required to store more data, it can therefore increase;<em><strong><br/></strong></em>Can be expanded until all the available RAM is used;<br/>There is no unused/wasted memory;<br/>Memory is allocated to the data structure as the program executes (run-time);<br/>Elements of a dynamic data structure are stored in memory locations that are chained together but not necessarily physically contiguous;<br/>Elements of a dynamic data structure are sequentially accessed;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates were able to identify two characteristics of a dynamic data structure.</p>\n<p>Most candidates were able to identify two characteristics of a dynamic data structure. </p>\n</div>",
    "question_id": "21M.1.HL.TZ0.10",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Sketch a balanced binary tree that would allow the following output when traversed using in order traversal:</p>\n<p>Zebra, Tango, Hotel, Foxtrot, Delta, Bravo, Alpha. </p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></em></p>\n<p><em>Award <strong>[3 max] </strong></em><br/>Correct root;<br/>Correct left sub-tree;<br/>Correct right sub-tree;<br/><em><strong>Note</strong></em>: Award 1 mark for any binary tree with the same number of nodes in the left and right subtree;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A balanced binary tree was not always familiar to candidates.</p>\n</div>",
    "question_id": "21M.1.HL.TZ0.11",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>EnviroBuild</em> is a construction company that recently purchased land. It has permission to build either small houses <strong>or</strong> large houses but not both types of house. The maximum number of small houses that can be built is 10. The maximum number of large houses that can be built is five.</p>\n<p>The cost of building the houses is calculated using a model with the following three variables:</p>\n<p><code>House_Type</code>: The type of house (small or large).</p>\n<p><code>House_Num</code>: The number of houses to be built.</p>\n<p><code>Profit</code>: The total sales revenue minus the land costs, labour costs and material costs.</p>\n</div><div class=\"specification\">\n<p>The decision whether to build small houses or large houses is a purely financial one and is based on the following information:</p>\n<ul>\n<li>A single payment of $500 000 for the land, regardless of whether 10 small houses or five large houses are to be built.</li>\n<li>The revenue from each house sale will be $220 000 for a small house and $400 000 for a large house.</li>\n<li>The labour costs will be $2500 per day, regardless of the type of house built.</li>\n<li>It will take 17 days to build each small house and 23 days to build each large house.<br/>Houses are built sequentially so that they can be sold as soon as they are completed.</li>\n<li>The material costs will be $100 000 for each small house and $190 000 for each large house.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the data types for the <code>House_Type</code>, <code>House_Num</code> and <code>Profit</code> variables.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a spreadsheet model that shows the total profit for the chosen type of house. The user must input the <code>House_Type</code> and <code>House_Num</code> to calculate the total profit.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> validation tests that should be included in the test plan for this spreadsheet model.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>To finance this project, <em>EnviroBuild</em> took out a bank loan of $400 000 and will be required to pay interest on this loan. The project starts on 1 January 2021.</p>\n<p>The following steps are used to calculate the total profit:</p>\n<ul>\n<li>Read the <code>Profit</code> variable and the <code>No_of_Days</code> variable from the spreadsheet model in (b).</li>\n<li>Calculate the number of months that the project will take.\n<ul>\n<li>There are no partial months.</li>\n<li>For example, if a project finished on 1 July 2021, the loan interest rates will include July: the project will last for 7 months.</li>\n</ul>\n</li>\n<li>The rate of interest on the bank loan of $400 000 is 1 % per month.</li>\n<li>The land tax is $500 per month.</li>\n</ul>\n<p>Construct the pseudocode that will calculate the profit after these additional costs have been considered. You can introduce any new variables, if necessary.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><code>House_Type</code>: char/string/Boolean;<br/><code>House_Num</code>: integer;<br/><code>Profit</code>: fixed-point decimal/currency/float/double;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for including</em> <code>HOUSE_TYPE</code> <em>and</em> <code>HOUSE_NUM</code> <em>as input</em><br/><em>Award <strong>[1]</strong> for calculating total revenue;</em><br/><em>Award <strong>[1]</strong> for calculating the number of days for each project</em><br/><em>Award <strong>[1]</strong> for calculating the labour costs</em><br/><em>Award <strong>[1]</strong> for calculating material costs</em><br/><em>Award <strong>[1]</strong> for calculating the profit</em></p>\n<p><code>REVENUE</code>:<em> Award <strong>[1]</strong> for Sales_price * House_Num based on the If statement<br/></em><code>NUM_DAYS</code>:<em> Award <strong>[1]</strong> for the correct number of days * house_num.<br/></em><code>LABOUR_COSTS</code>:<em> Award <strong>[1]</strong> for NUM_DAYS * 2500<br/></em><code>MATERIAL_COSTS:</code><em> Award <strong>[1]</strong> for checking the HOUSE_TYPE and then multiplying HOUSE_NUM by the correct material cost.<br/></em><code>PROFIT:</code><em> Award <strong>[1]</strong> for deducting all of the costs from the revenue.<br/></em></p>\n<p><strong>Example answer</strong>:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><strong>Alternative solution</strong>:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Note: accept cell references such as B3 instead of variables</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p><em>Award <strong>[2 max]</strong> for each variable tested.</em><br/><em>Award <strong>[1]</strong> for the type of test and <strong>[1]</strong> for example test data</em>.</p>\n<p>Type test for <code>HOUSE_TYPE</code><br/>Normal: s or l / small or large; Abnormal: m / medium.<br/>If Boolean has been used Normal: True or False; Abnormal: “small”</p>\n<p>Range test for <code>NUM_HOUSES</code><br/>Normal <code>HOUSE_NUM</code> = 5 / Abnormal <code>HOUSE_NUM</code> = 12 / Extreme <code>HOUSE_NUM</code> = 6 or <code>HOUSE_NUM</code> = 10</p>\n<p><em>Accept any testing that relates to</em>:</p>\n<ul>\n<li><em>normal data // <strong>Note</strong>: an example will get this mark</em></li>\n<li><em>extreme data</em></li>\n<li>\n<em>abnormal data</em>.</li>\n</ul>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for reading the profit, number of days from csv file / cell range.</em><br/><em>Award <strong>[1]</strong> for adding the number of days to the date.</em><br/><em>Award <strong>[1]</strong> for extracting the month from the date.</em><br/><em>Award <strong>[1]</strong> for calculating interest and rates.</em><br/><em>Award <strong>[1]</strong> for deducting the costs from the profit and displaying result.</em></p>\n<p><strong>Example pseudocode</strong>:</p>\n<pre>import FILE.CSV As SS<br/>read PROFIT, NUM_DAYS from SS<br/>END_DATE = #01/01/2021 + NUM_DAYS<br/>NUM_MONTHS = MONTH(END_DATE)<br/>COSTS = (NUM_MONTHS * 500) + (NUM_MONTHS * (400000 * .01))<br/>output (PROFIT – COSTS)</pre>\n<p><strong>Alternative pseudocode</strong>:</p>\n<pre>input Profit, NumDays from spreadsheet<br/>endDate = date(dateval(\"1/1/21\") + NumDays)<br/>monthsTaken = month(endDate)<br/>interest = 400000 * 0.01 * monthsTaken<br/>output \"Net profit = $\", (Profit – interest)</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.4",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-1-the-basic-model",
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A company provides car parking services in large cities and needs a computer system to keep track of the number of vehicles using its parking areas.</p>\n<p>When a vehicle arrives, its registration plate is recorded on the system and it is allocated a number that identifies where it should park. When the vehicle leaves, that space is made available for other vehicles to use.</p>\n<p>Vehicles are identified by their unique registration plate, which is an alphanumeric code of eight characters (e.g. <code>X1234567</code>). This is clearly displayed on the vehicle.</p>\n<p>A programmer created the classes <code>ParkingArea</code> and <code>Vehicle</code> to model the above situation.</p>\n<pre>public class ParkingArea {<br/>    private Vehicle vehicles[];<br/>    private String name;<br/><br/>    ParkingArea(String name, int capacity) {<br/>        this.name = name;<br/>        if (capacity &gt; 300) capacity = 300;<br/>        this.vehicles = new Vehicle[capacity];<br/>    }<br/><br/>    String getName() {<br/>        return name;<br/>    }<br/>    <br/>    public int getCapacity() {<br/>        return vehicles.length;<br/>    }<br/><br/>    public int findVehicle(String reg) {<br/>        //find where the vehicle is located in the array and<br/>        //return the index not yet written<br/>    }<br/>}</pre>\n<pre>public class Vehicle {<br/>    private String registration;<br/>    private byte colour;<br/>    private boolean broken;<br/><br/>    public final static byte BLACK=1;<br/>    public final static byte WHITE=2;<br/>    public final static byte BLUE=3;<br/>    public final static byte RED=4;<br/>    public final static byte GREEN=5;<br/>    private final static double ADMIN_FEE = 3;<br/><br/>    public Vehicle() {}<br/><br/>    public Vehicle(String registration) {<br/>        this.registration = registration;<br/>    }<br/>    public Vehicle(String registration, byte colour) {<br/>        this.registration = registration;<br/>        this.colour=colour;<br/>    }<br/>    public void setBroken(boolean broken) {<br/>        this.broken=broken;<br/>    }<br/>    public void setColour(byte colour) {<br/>        this.colour=colour;<br/>    }<br/>    public boolean getBroken() {<br/>        return broken;<br/>    }<br/>    public String getRegistration() {<br/>        return registration;<br/>    }<br/>    public double pay(int hours) {<br/>        // code to return admin fee - only if applicable<br/>    }<br/>}</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> effect of using the modifier <code>static</code> when declaring a variable.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the relationship between the classes <code>Vehicle</code> and <code>ParkingArea</code>.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why it is necessary to use the keyword <code>this</code> in the <code>setBroken</code> method of the <code>Vehicle</code> class.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct code to create an instance of the <code>Vehicle</code> class that has a registration of <code>X1234567</code>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct code that sets the colour of the object created in part (i) as black.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Static means it is the same for all instances of the class;<br/>because it is contained in the class rather than in an instance of the class/object / it is defined at the class level;<br/>Static means that less memory is taken up;<br/>as only 1 memory allocation it created for all instances rather than 1 per instance;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Award <em><strong>[1]</strong></em> for aggregation (accept Composition also);<br/>Award <em><strong>[1]</strong></em> for An instance of ParkingArea can contain Vehicles;<br/>Award<em> <strong>[1]</strong></em> for A single ParkingArea can contain between 0 and 300 instances of Vehicle;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>To distinguish between 2 variables with the same name;<br/>Allows the local/instance variable to be set to the parameter/class variable;</p>\n<p><em><strong>Note</strong>: Award <strong>[1]</strong> if the incorrect technical terms for variables are given but the idea of distinguishing between 2 types of variables with the same name is expressed</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.</p>\n<p>Award <em><strong>[1]</strong></em> for correctly declaring the variable as type Vehicle;<br/>Award <em><strong>[1]</strong></em> for using the correct constructor;</p>\n<p>e.g.</p>\n<p><code>Vehicle v=new Vehicle(\"X1234567\")</code>;</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Award <em><strong>[1]</strong></em> for calling the correct method on the correct instance variable name;<br/>Award <em><strong>[1]</strong></em> for using the constant correctly, i.e. Vehicle.BLACK;</p>\n<p><code>v.setColour(Vehicle.BLACK)</code>;</p>\n<p><em><strong>Note</strong>: accept the constant prefixed by the name of the instance instead of the class</em></p>\n<div class=\"question_part_label\">d.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students associated the modifier <em>static</em> with the idea of permanence in some form, but not all clearly understood the difference between class variables and instance variables.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was well-answered.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The <em>this</em> modifier clearly causes confusion. Students realized that it in some way clarified the difference between the two occurrences of the variable <em>broken</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many full marks for part (i), although some failed to put the <em>String</em> data in speech marks.</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Part (ii) showed a lack of experience with the <em>byte</em> type.</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.10",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development",
      "d-1-objects-as-a-programming-concept"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A palindrome is a word that spells the same backwards. For example, the words “kayak” and “rotor” are palindromes.</p>\n<p>A word can be checked to see if it is a palindrome by comparing the first letter with the last letter and then the second letter with the next-to-last letter and so on.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why recursion is a suitable tool to use when performing this check.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The method <code>palindrome()</code> is called by <code><strong>boolean</strong> t = palindrome(word)</code>;<br/>where <code>word</code> is a String variable.</p>\n<p>Without writing code, describe the recursive method <code>palindrome()</code> that returns whether or not a word is a palindrome.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The solution repeats the same algorithm/series of steps/code;<br/>With a changing/different parameter set;<br/>Until a base/terminating case is reached; </p>\n<p><em><strong>Note</strong>: Do not award marks for “the method calls itself”</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The base case will return true if the length of word = 0 or 1 (<em>must have both values</em>) / The index of last character &lt;= index of the first character;<br/>Otherwise the first and last letters will be compared;<br/>Returns false if they are not equal;<br/>If they are equal, calls the method again;<br/>With, as its parameter, the word stripped of its first and last letters (can use indices);</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "17N.2.HL.TZ0.19",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Open source code is made available by a community of developers and is frequently updated. The code can be downloaded for free, but users must register with the website and have their access authenticated.</p>\n</div><div class=\"specification\">\n<p>Authentication is based on signing in to an established third-party company, for example a user’s existing email or a social network account. The third-party company then verifies the user, granting them access to the open source code website.</p>\n</div><div class=\"specification\">\n<p>The URL of this website is www.OpenSourceDev.org. Any new pieces of code that the developers make available become new resources on the website. A script generates weekly automatic notifications of new code available on the site, and sends this notification to users as an email.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of server-side scripting to provide the mechanism for registration.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the user’s privacy can be maintained whilst using this method of authentication.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline, with an example, how the URL for these new pieces of code will be generated.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the steps that the script could perform for sending out these notifications.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A fragment of a script and a web form are provided below.<br/>In the script some functions are not implemented, and only their specification is provided.</p>\n<pre>&lt;?php<br/>  //include a database of urls<br/>  include('url_db.php');<br/>  $url = $short = \"\";<br/><br/>  if ($_SERVER[\"REQUEST_METHOD\"] == \"POST\")<br/>  { $url = $_POST['url'];<br/>    $short = make_short($url);<br/>    function make_short($u)<br/>    { $x = make_alpha_string($u);<br/>      $y = first4_last4($x);<br/>      $z = limits($x);<br/>      $v = $y. \".\".$z;   //string concatenation<br/>      return $v;<br/>  }<br/><br/>  function make_alpha_string($u)<br/>  { // It removes, in this order: substrings corresponding to<br/>    // protocol names, the substring www, and all characters<br/>    // except for letters<br/>  }<br/><br/>  function first4_last4($u)<br/>  { // It returns the string made of the first 4 characters<br/>    // followed by the last 4 characters of $u<br/>  }<br/><br/>  function limits($u)<br/>  { // It returns the string made of the first character and<br/>    // last character of $u<br/>  }<br/><br/>  mysql_query(\"<br/>    INSERT INTO url_db(orig_url, short_url, url_ip) VALUES<br/>    ( '\".$_POST['url'].\"',<br/>      '\".$short.\"',<br/>      '\".$_SERVER['REMOTE_ADDR'].\"'<br/>    )<br/>  \");<br/> }<br/>?&gt;<br/><br/>&lt;form method=\"post\" action=\"\"&gt;<br/>URL:<br/>&lt;input type=\"text\" name=\"url\" /&gt;<br/>&lt;br&gt;&lt;br&gt;<br/>&lt;input type=\"submit\" name=\"Submit\" value=\"Submit\" /&gt;<br/>&lt;/form&gt;</pre>\n<p>Describe the processing that occurs when the form is filled with the URL https://www.the2nd.org/bin.php?id=70 that the server discovers is associated with the IP address 172.16.254.1.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4]</strong></em>.<br/>Server-side scripting does not require the installation of extra resources on a computer, for example the latest plug-in to run the script;<br/>The log-in process will generally be slower as a round trip has to be made;<br/>However, for regular visitors of the site, it speeds up the login, as the server script might be linked to data storage and recognize their access for further reference;<br/>The server script is independent from the version of the browsers that is used, the user just sees the HTML interface (this is the typical problem with client-side scripting);<br/>A security breach on the server could lead to <span style=\"text-decoration:underline;\">all</span> log-ins being compromised;<br/>However, server-side is more secure as client-side is more easily hacked into;<br/>There can be occasional downgrade in performance if too many users are running simultaneously their scripts on the server;<br/>However, for the nature of the files, this should not generate bottlenecks in the network (small files, not intensive streaming);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Your email address and associated password are private;<br/>Both are needed to register with the initial website, but at most only the email address is stored there;<br/>They are forwarded to the third-party website for identification;<br/>The third-party identifies/confirms whether or not the person is who they say they are, based on their email/social network services that the third party provides;<br/>This yes–no answer/confirmation provides the authentication for the open-source developers;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating how the URL is generated, and <strong>[1]</strong> for giving an example, up to <strong>[2 max]</strong></em>.<br/>It is generated by taking the path of the website extended with the path for the code;<br/>e.g. www.OpenSourceDev.org/newcode/json/fakecode007;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The script accesses the file of new URLs (in a file);<br/>And adds the URLs to an email template;<br/>Then accesses the file of email addresses of subscribers (and sends email);</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows, up to <strong>[3 max]</strong>. (<strong>Note</strong>: there are <strong>[5]</strong> marking points)</em><br/><em>Award <strong>[1]</strong> for the <span style=\"text-decoration:underline;\">value passing</span> from the form to php via <code>POST</code>.</em><br/><em>Award <strong>[1]</strong> for the <span style=\"text-decoration:underline;\">operation</span> of the code to generate the short URL with final <code>$v</code>.</em><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">access to update</span> database.</em><br/><em>Award <strong>[1]</strong> for a <span style=\"text-decoration:underline;\">new correct triple</span> in database.</em><br/><em>Award <strong>[1]</strong> for <span style=\"text-decoration:underline;\">remote access</span> operated by server through the database to retrieve the URL.</em></p>\n<p><em><strong>Note</strong>: Competent use of terminology is needed to award full marks. Generic answers that show general good understanding of the process, but lack use of competent language should not be awarded full marks</em>.</p>\n<p><em><strong>Example</strong></em><br/>The <span style=\"text-decoration:underline;\">value of the URL</span> that is inputted (and submitted) via the form is stored in the <span style=\"text-decoration:underline;\">variable URL</span> which Interacts with the php code, because the <span style=\"text-decoration:underline;\">method <code>POST</code></span> is specified in the form, therefore starting a communication process;</p>\n<p>The process generates a <span style=\"text-decoration:underline;\">different value</span> for the name for the URL that is <span style=\"text-decoration:underline;\">used to update</span> a database of URLs <span style=\"text-decoration:underline;\">together with the IP address</span>;</p>\n<p>The IP address is retrieved by the server through a <span style=\"text-decoration:underline;\">remote call</span> requested from the insertion in database;</p>\n<pre>The new value for the URL that is generated, given the one in input, is <code><span style=\"text-decoration:underline;\">thenhpid.td</span></code> and is stored in <code>$v</code>;<br/>(detail of intermediate operations:<br/>$x = thendorgbinphpid<br/>$y = thenhpid<br/>$z = td<br/>$v = thenhpid.td)<br/><br/>The new record inserted in the database url_db will be the following triple www.the2nd.org/bin.php?id=70, thenhpid.td, 172.16.254.1;</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.8",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-4-the-evolving-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The array <code>inbound</code> in the <code>FlightManagement</code> class is sorted by Estimated Time of Arrival (ETA).</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>method signature</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a method <code>showDelayed()</code> that outputs the IDs of all delayed flights in the array <code>inbound</code> that have not yet landed and that have an ETA before a given time <code>t</code>. The time <code>t</code> is passed as a <code>String</code> parameter.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Without using a sorting algorithm, construct the method <code>add(Arrival newArrival)</code> that inserts a <code>newArrival</code> in the sorted array <code>inbound</code>. You may assume that <code>newArrival</code> has been instantiated and that the array <code>inbound</code> is not full.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>When a flight is delayed, a method in <code>FlightManagement</code> is used to find and update the flight with the delay and to reorganize the array so that it remains sorted for the ETA.<br/><br/>Describe how this method would be implemented using the methods provided.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>. <br/><strong>[1 max]</strong> for any two out of three underlined key components.<br/><strong>[2 max]</strong> for all three key components.<br/><strong>Note</strong>: the return type is <span style=\"text-decoration:underline;\">not</span> part of the method signature.<br/></em></p>\n<p><em><strong>Example answers</strong>:<br/></em>The <span style=\"text-decoration:underline;\">method name</span> and <span style=\"text-decoration:underline;\">all of its parameters</span> and the <span style=\"text-decoration:underline;\">type of these parameters</span>.<br/>The <span style=\"text-decoration:underline;\">method name</span> and the <span style=\"text-decoration:underline;\">type</span> of <span style=\"text-decoration:underline;\">all of its parameters</span>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for a correct loop until last Arrival.</em><br/><em>Award <strong>[1]</strong> for including correct test for ETA.</em><br/><em>Award <strong>[1]</strong> for including correct test landed.</em><br/><em>Award <strong>[1]</strong> for correctly using double dot notation in output statement.</em><br/><em>Do not penalize incorrect use of accessor methods</em>.</p>\n<p><em><strong>Example answers</strong></em>:</p>\n<pre><strong>public void</strong> showDelayed(String t)<br/>{   <strong>int</strong> i = 0;<br/><strong>    while</strong> (i &lt;= last)<br/>    {   <strong>if</strong> (inbound[i].getETA().compareTo(t) &lt; 0)<br/>        {   <strong>if</strong> (!inbound[i].getLanded())<br/>            {    <strong>output</strong>(inbound[i].getMyFlight().getID());<br/>            }<br/>    i = i + 1<br/>         }<br/>    }<br/>}<br/><strong><br/>public void</strong> showDelayed(String t)<br/>{   int i = 0;<br/>    while ((i &lt;= last) &amp;&amp; (inbound[i].getETA().compareTo(t) &lt; 0))<br/>    {   if (!inbound[i].landed)<br/>        {   output(inbound[i].myFlight.id);<br/>        }<br/>    i = i + 1<br/>    }<br/>}<br/><br/><strong>public void</strong> showDelayed(String t)<br/>{   <strong>for</strong> (<strong>int</strong> i = 0; i &lt;= last; i++)<br/>    {   <strong>if</strong> ((inbound[i].getETA().compareTo(t) &lt; 0) &amp;&amp;<br/>             (!inbound[i].landed))<br/>        {   <strong> output</strong>(inbound[i].myFlight.id);<br/>        }<br/>    }<br/>}</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for a loop comparing <code>newArrival</code> to entries in inbound.</em><br/><em>Award <strong>[1]</strong> for testing for the last entry.</em><br/><em>Award <strong>[1]</strong> for good attempt a for loop in reverse order.</em><br/><em>Award <strong>[1]</strong> for a fully correct for loop in reverse order.</em><br/><em>Award <strong>[1]</strong> for correctly shifting elements in the array.<br/>Award <strong>[1]</strong> for assigning <code>newArrival</code>.<br/>Award <strong>[1]</strong> for incrementing last.</em></p>\n<p><em><strong>Example answer</strong></em>:</p>\n<pre><strong>public void</strong> add(Arrival newArrival)<br/>{   <strong>int</strong> i = 0;<br/><strong>    while</strong> ((i &lt;= last)&amp;&amp;(inbound[i].compareWith(newArrival) &lt; 0))<br/>    { i++;<br/>    }<br/><strong>    for</strong> (<strong>int</strong> j = last + 1; j&gt;i; j--)<br/>    {   inbound[j] = inbound[j - 1];<br/>    }<br/>    inbound[i] = newArrival;<br/>    last++;<br/>}</pre>\n<p><em><strong>Alternative answer</strong></em>:</p>\n<pre><strong>public void</strong> add(Arrival newArrival)<br/>{<br/><strong>    int</strong> i = 0;<br/><strong>    while</strong> (i &lt;= last)&amp;&amp;<br/>        (inbound[i].getETA().compareTo(newArrival.getETA()) &lt; 0)<br/>    {   i++;<br/>    }<br/><strong>    for</strong> (<strong>int</strong> j = last; j &gt;= i; j--)<br/>    {   inbound[j + 1] = inbound[j];<br/>    }<br/>    inbound[i] = newArrival;<br/>    last++;<br/>}</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for declaring a variable of type <code>Arrival</code>.</em><br/><em>Award <strong>[1]</strong> for calling remove (<code>flightID</code>).</em><br/><em>Award <strong>[1]</strong> for calling <code>addDelay(minutes)</code>.</em><br/><em>Award <strong>[1]</strong> for adding the updated <code>Arrival</code> object to array inbound.</em><br/><em>Award <strong>[1]</strong> for adding the update to the array inbound by calling <code>add(update)</code>.</em></p>\n<pre><strong>public void</strong> delay(String flightID, <strong>int</strong> minutes)<br/>{<br/>    Arrival update = remove(flightID);<br/>    update.addDelay(minutes);<br/>    add(update);<br/>}</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.12",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Two further classes, <code>Car</code> and <code>Motorbike</code>, are created.</p>\n<pre>public class Car extends Vehicle{<br/>    public static double hourlyFee=3.5;<br/>    public double pay(int hours) {<br/>        //code to calculate and return the complete price<br/>    }<br/>}<br/><br/>public class Motorbike extends Vehicle{<br/>    public static double hourlyFee=2.5;<br/>    public double pay(int hours) {<br/>        //code to calculate and return the complete price<br/>    }<br/>}</pre>\n</div><div class=\"specification\">\n<p>The method <code>pay</code> in the <code>Vehicle</code> class returns the administration fee (which is only part of the total price), while the method <code>pay</code> of the <code>Car</code> class calculates the total price for a car staying in the parking area.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> differences between inheritance and aggregation.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a UML diagram that shows the relationships between the <code>ParkingArea</code>, <code>Vehicle</code>, <code>Motorbike</code> and <code>Car</code> classes. There is no need to include the attributes or methods of each class.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>pay</code> in the <code>Vehicle</code> class that returns the admin fee stored in the variable <code>AdminFee</code> if the vehicle has stayed for five hours or less; otherwise, it returns 0.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>pay</code> in the <code>Car</code> class, where it uses the <code>vehicle</code> method <code>pay</code> but adds the charge for the amount of time spent in the parking area.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The array <code>vehicles[]</code> in the <code>ParkingArea</code> class is used to store instances of the <code>Car</code> or <code>Motorbike</code> class.</p>\n<p>Outline why <code>Vehicle</code> is a valid type for this array.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Inheritance is when the behaviour of a class is transferred to another class;<br/>whereas aggregation is when a class is contained inside another and used;</p>\n<p>Aggregation is useful for modelling a real life situation of containment aka. “has a” (e.g. contents of a box);<br/>whereas Inheritance is useful for modelling real life situation that one entity is a particular (more specific) type of another entity, aka. “is a”;</p>\n<p>While both can achieve the same result, Aggregation (composition) can lead to more robust and safer code;<br/>whereas inheritance is a more flexible solution but prone to errors due to the behaviour of subclasses being changed;</p>\n<p>Inheritance allows for code reuse / lowers maintenance cost;<br/>whereas aggregation does neither of the above;</p>\n<p>Can have more than 1 aggregation;<br/>but only 1 superclass (in Java);</p>\n<p>Inheritance allows for code reuse as in the creation of sub-classes;<br/>whereas with aggregation each class must be written out in full;</p>\n<p><em>Mark as [2] + [2]</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Award <strong>[1]</strong> for all four classes shown;<br/>Award <strong>[1]</strong> for showing the links in the correct place (any arrow or line is acceptable);<br/>Award <strong>[1]</strong> for distinguishing the relationship ParkingArea - Vehicle;<br/>Award <strong>[1]</strong> for distinguishing the relationships Car/Motorbike -Vehicle;</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for returning admin fee if &lt;=5 hours;</em><br/><em>Award <strong>[1]</strong> for returning 0 if &gt;5 hours;</em></p>\n<pre>public double pay(int hours) {<br/>    if (hours &lt;= 5) {<br/>        return ADMIN_FEE;<br/>    } else return 0;<br/>}</pre>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for for super.pay(hours);</em><br/><em>Award <strong>[1]</strong> for returning result of correct formula (i.e. adding on hourlyFee*hours);</em></p>\n<p><strong>Example</strong>:</p>\n<pre>public double pay(int hours) {<br/>    return super.pay(hours)+hourlyFee*hours;<br/>}</pre>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong>.</em><br/>Cars and Motorbikes are also Vehicles as they extend the Vehicle class / Vehicle is the superclass and Car/Motorbike are subclasses;<br/>The subclasses can be cast as/considered to be/referred to as Vehicle which is its superclass;<br/>As Cars and Motorbikes inherit from Vehicles they are also Vehicles and can therefore be stored in an array of type Vehicle;<br/>Arrays must be declared as 1 type, and the state and behaviour of Vehicle is what both Car and Motorbike have in common. Therefore they can be referenced as Vehicles and stored in an array of Vehicles, even though they are subclasses;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This tested the level of understanding of OOP principles. Not many could clearly put into words the specific differences and tended to provide more in the line of definitions. The better students clarified the dependency levels between the different classes.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was well-answered. Most students either used the correct type of arrows or indicated the relationships with the correct expressions (has a etc.).</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Part (i) was well-answered, but few students understood the use of the super modifier in part (ii). All aspects of the OOP course (including inheritance) should be subject to comprehensive practical work in order to promote full understanding.</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Part (i) was well-answered, but few students understood the use of the super modifier in part (ii). All aspects of the OOP course (including inheritance) should be subject to comprehensive practical work in order to promote full understanding.</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The comments for part (d) are also appropriate here.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.11",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-2-features-of-oop",
      "d-1-objects-as-a-programming-concept",
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the difference between an information system and a database.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A bank maintains a database that stores details of clients and their accounts.</p>\n<p>A client wants to transfer money between two accounts held at the same bank.</p>\n<p>Explain how the ACID (Atomicity, Consistency, Isolation, Durability) properties would apply in the context of this database transaction.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A bank holds large volumes of financial and personal information about its clients in its database.<br/><br/>Discuss whether this database should be open to interrogation by the police or the Government.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for a definition of an information system, <strong>[1]</strong> for a definition of a database and <strong>[1]</strong> for the relationship between the two</em>.<br/>An information system is the collection of software, hardware, networking infrastructure, human resources and databases that provide the storage, processing and communication/distribution of information;<br/><em><strong>A database</strong></em> is an organized collection of data (organized so that its contents can easily be accessed, processed, and updated);<br/>A database is a subset of the information system;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[8 max]</strong></em>.<br/><em>Award up to <strong>[2 max]</strong> for each of the four properties,</em><br/><em>Award <strong>[1]</strong> for an explanation of the property and <strong>[1]</strong> in relation to the money transfer transaction</em>.</p>\n<p><em>Example answers</em>:</p>\n<p><strong>Atomicity</strong>:<br/>Ensures that either all changes to data are performed as if they are a single operation or none of them are performed;<br/>In moving money from one account to another, it ensures that if a debit is made successfully from one account then the corresponding credit is made to the other account;</p>\n<p><strong>Consistency</strong>:<br/>Ensures that data is in a consistent state when a transaction begins and when it ends / ensures that only valid data is written in the database;<br/>In transferring money from one account to another, it ensures that the total value of money in both the accounts is the same at the beginning and at the end of each transaction;</p>\n<p><strong>Isolation</strong>:<br/>Ensures that intermediate state of a transaction is invisible to other transactions;<br/>In moving money from one account to another the isolation property ensures that another transaction might see one account or the other but cannot see both accounts;</p>\n<p><strong>Durability</strong>:<br/>Ensures that after a transaction successfully completes, changes to data persist and are not undone even in the event of a system failure;<br/>In moving money from one account to another durability property ensures that the changes made to each account are permanent;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying an advantage of these databases being open to interrogation and an additional <strong>[1]</strong> for each development of that advantage up to <strong>[3 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for identifying a disadvantage of these databases being open to interrogation and an additional <strong>[1]</strong> for each development of that disadvantage up to <strong>[3 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for a valid conclusion.</em><br/><em>If there is no conclusion, award <strong>[5 max]</strong></em>.</p>\n<p><strong>Advantages</strong>:<br/>Sharing the information may enable it to be made accessible to organizations such as the Police or the Government that may not have access to it;<br/>This means that it could be used to detect fraudulent financial activities;<br/>Leading to reduced insurance or health insurance premiums as monies are not lost from the system that create artificially high premiums;</p>\n<p><strong>Disadvantages</strong>:<br/>Misuse of information / security issues;<br/>Information from this database may enable the account details of the clients to be shared with third parties;<br/>Who may use this information to take money from their accounts;</p>\n<p><strong>Conclusion</strong>:<br/>Although the information may be shared, the overall benefits from a reduction in fraudulent crime or inflated insurance premiums is greater than the potential for this information to get into the wrong hands;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.1",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-1-basic-concepts",
      "a-3-further-aspects-of-database-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Global warming is a term used to describe the increase in mean global temperatures.</p>\n<p>There have been numerous computer simulations developed to predict the effects of global warming. One simulation is NASA’s Virtual Earth System Laboratory (VESL), which allows users to see how climate change affects glacier size, global sea level and changes to the coastline.</p>\n<p>The VESL runs simulations in real time and is an abstraction of reality.</p>\n</div><div class=\"specification\">\n<p>A recent study reported that previous simulations of predicted global sea levels for 2100 were highly inaccurate.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by a real-time simulation in the context of a glacier size simulation.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by the statement “the VESL simulation is an abstraction of reality”.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> reasons why predictions of global sea levels from simulations may not be accurate.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>NASA has decided to make its simulation software available for other scientists as well as members of the public.</p>\n<p>Evaluate the social and ethical implications of this decision.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>A real-time simulation runs at the same rate as the actual physical system;<br/>The change in the glacier size occurs at the same rate as the real change / a virtual glacier will be the same size as the real glacier after the same period of time;</p>\n<p>A real time simulation operations it can mean that it happens without a delay/action carried out before the next input;<br/>Changes to the simulation inputs will be displayed immediately;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Abstraction removes specific detail that will not the accuracy of the simulation / climate change variables that have little influence;<br/>Abstractions helps create the model / reduces the complexity of the model so that it works effectively;<br/>Symbols may be used to represent patterns (e.g. temperature change);</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Sea levels are based on so many factors (e.g. CO<sub>2</sub> emissions, cattle); that it is difficult to accurate predict all of these variables;</p>\n<p>Global events (eg nuclear war, volcanic eruptions);<br/>may cause for an under-prediction;</p>\n<p>Unknown variables (confounding factors) may be affecting rising sea levels;<br/>Population growth may increase or decrease and this may impact upon emissions;</p>\n<p><em>Accept any reasonable argument</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[3 max]</strong> for positive reasons.</em><br/><em>Award <strong>[3 max]</strong> for negative reasons.</em><br/><em>Award <strong>[1 max]</strong> for a reasonable conclusion.</em></p>\n<p><em><strong>Positive</strong></em><br/>Making it free means more people may use it;<br/>Educating the public may encourage more people to be environmental friendly (e.g. boycott plastics);<br/>This may lead to pressure on governments to reduce emissions;<br/>May provide valuable data for scientists who don’t have access to NASA resources;</p>\n<p><em><strong>Negative</strong></em><br/>Users may not fully understand the simulation results and may misinterpret findings;<br/>May change the perception of the accuracy of the simulation software / Users may not value predictions when the simulator seems so simple;<br/>Predictions that don’t reflect the real change may do damage to public perception of climate change;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.5",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The management of the company will launch a new scheme to give every 50th car driver and every 60th motorcyclist a free coffee voucher. The code for printing this voucher has already been created and is activated by calling the static method <code>Vouchers.printCoffeeVoucher()</code>.</p>\n<p>A <code>getKind()</code> method has already been added to the <code>Vehicle</code> class, which returns a <code>char</code> value indicating whether it is a car (<code>c</code>) or a motorbike (<code>m</code>).</p>\n</div><div class=\"specification\">\n<p>One test performed on the finished code was defined as follows:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe, <strong>without writing code</strong>, any changes required to the <code>addVehicle</code> method and the <code>ParkingArea</code> class to make the new voucher scheme work.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> other tests you might perform on the completed code to prove that it functions correctly.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The <code>removeVehicle </code>method of the <code>ParkingArea</code> class searches in the array for a <code>Vehicle</code> object with a specified registration plate, then removes it by setting that array index to <code>null</code>.</p>\n<p>The method returns a reference to the <code>Vehicle</code> object that has been removed from the array, or <code>null</code> if no matching registration plate was found.<br/><br/>Construct the <code>removeVehicle</code> method.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong>.</em><br/>Award <strong>[1]</strong> for declaring both the variable for total cars and the variable for total motorbikes at class level (private);<br/>Award <strong>[1]</strong> for initializing the variables, either by calling a mutator method, while declaring, or in the constructor;<br/>Award <strong>[1]</strong> for deciding whether the car or motorbike is 50th or 60th in the daily additions;<br/>Award <strong>[1]</strong> for printing the message if so;<br/>Award <strong>[1]</strong> for incrementing the appropriate counter for the total cars / motorbikes added based on the value returned by <code>getKind()</code>;</p>\n<p><em><strong>Note</strong>: Do not award marks for simply adding mutator or accessor methods for the motorbike / car totals</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong>.</em></p>\n<p><strong>Car</strong>:<br/><em>Award <strong>[1]</strong> for Car count mod 50 is 0, <code>coffee voucher</code>;</em><br/><em>Award <strong>[1]</strong> for Car count mod 50 is n, n <code>coffee vouchers</code>;</em></p>\n<p><strong>Motorbike</strong>:<br/><em>Award <strong>[1]</strong> for Motorbike count mod 60 is not 0, <code>No coffee vouchers</code>;</em><br/><em>Award <strong>[1]</strong> for Motorbike count mod 60 is n, n <code>coffee voucher</code>;</em></p>\n<p><strong>Non Car/Motorbike</strong><br/>Award <strong>[1]</strong> for Any amount of instances of the Vehicle class which are neither Car nor Motorbike, <code>No coffee voucher</code></p>\n<p><em><strong>Note</strong>: do not award a mark for first test case above which is already give in question stem</em></p>\n<p> </p>\n<p><strong>Examples</strong>:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong>. </em><br/><em>Award <strong>[1]</strong> for correct loop;</em><br/><em>Award <strong>[1]</strong> for checking if not null;</em><br/><em>Award <strong>[1]</strong> for checking registration using the getRegistration().equals() method;</em><br/><em>Award <strong>[1]</strong> for setting vehicles[i] to null;</em><br/><em>Award <strong>[1]</strong> for returning the reference (not the null element of the array), by making another variable;</em><br/><em>Award <strong>[1]</strong> for returning null if it is not found;</em></p>\n<p><strong>Example 1</strong>:</p>\n<pre>public Vehicle removeVehicle(String registration) {<br/>for (int i = 0; i &lt; vehicles.length; i++) {<br/>    if (vehicles[i] != null &amp;&amp;vehicles[i].getRegistration().equals(registration)) { //question - why<br/>do it in this order?<br/>         Vehicle leaving = vehicles[i];<br/>         vehicles[i] = null;<br/>         return leaving;<br/>    }<br/>}<br/>return null;<br/>}</pre>\n<p><strong>Example 2</strong>:</p>\n<pre>public Vehicle removeVehicle(String registration)<br/>  { Vehicle v = null;<br/>    boolean found = false;<br/>    int i = 0;<br/>    while (!found &amp;&amp; i &lt; Vehicles.length)<br/>    { found = Vehicles[i] != null &amp;&amp;<br/>              registration.equals(Vehicles[i].getRegistration());<br/>      if (found)<br/>      { v = Vehicles[i];<br/>        Vehicles[i] = null;<br/>      }<br/>      else i = i + 1;<br/>    }<br/>    return v;<br/>  }</pre>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a good test of logical thinking. Students need to be precise: \"Include two new integer variables in the <em>ParkingArea </em>class to hold the total number of cars and motorcycles\" is clearly better than \"keep count of the different vehicles\". </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was poorly answered, with many answers being too vague. Similar to the requirements in the IA-section B, the tests need to give both specific test data and the expected results.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was similar to 11(a) and students who answered the first one successfully also tended to answer this one well. Care needs to be taken when methods return something that types match with the return type indicated in the method signature.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.12",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A museum has an online catalogue of its pieces. No new pieces have been acquired over the past 20 years, and the museum has already been at risk of closure for lack of funding. Its website consists only of static web pages.</p>\n<p>It is suggested that the museum’s static web pages may be contributing to the museum’s lack of success.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> differences between a static web page and dynamic web page.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest <strong>two</strong> services, and the benefits that they would provide, if the museum were to redesign its website to be dynamic.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the provision of services on the museum’s website can increase its rankings in search engines.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>It is also suggested that the museum’s basic catalogue be revised and upgraded with all of the museum’s pieces being catalogued using meta-tags.</p>\n<p>Suggest how the use of meta-tags can be relevant for search engine optimization.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong> for <strong>three</strong> differences stated</em>.<br/>Dynamic allows user interaction;<br/>Allows parts of the content to be changed without uploading the complete page;<br/>Can connect with server-side databases;<br/>Gives different views for different users;<br/>Includes the use of scripts / server side scripting, examples of;<br/>Dynamic makes use of templates;</p>\n<p>Static webpages are changed only by administrator;<br/>It gives the same view to all users;<br/>It displays exactly the information that is stored in the html file (and it may still include multimedia);<br/>Simpler to develop/maintain, because it does not require major design/knowledge of web applications;<br/>Source code edited directly in HTML generally shorter/less cumbersome than the one developed with web applications;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying a service and <strong>[1]</strong> for stating a benefit, for <strong>two</strong> services up to <strong>[4 max]</strong></em>.</p>\n<p>Book/pre-pay online for tickets;<br/>More convenient for visitors (avoids closures/queues etc.);</p>\n<p>Add a virtual tour of the museum online;<br/>Increases the visibility of the museum / It becomes better known;</p>\n<p>Add an audio-guide for smartphone to be downloaded from the portal;<br/>So that users are already equipped before they visit;</p>\n<p>(Targeted) pop up adverts/links;<br/>Can increase revenue to the museum;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>More possibility for the users to interact will make the website more popular;<br/>More services online provides more ways for the museum to be linked from/to other external organizations/institutions (e.g. bank/tourist office);<br/>The two combined would generate more traffic and more authoritative in-links;<br/>This increases the possibility for the site to be found in a variety of ways when searched;<br/>And this increases the website’s ranking;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for an example/idea of a meta-tag and its purpose.</em><br/><em>Award <strong>[1]</strong> for understanding the link between meta-tags and indexing via the search operation by crawlers.</em><br/><em>Award <strong>[1]</strong> for understanding the link between indexing and ranking via optimization/better classification by SEO</em>.</p>\n<p>Use of correct keywords allows the search engine to search and find those terms;</p>\n<p><em><strong>For Example</strong></em><br/>Title tags / keywords associated to the title of the page;<br/>A description with tags may induce more visitors to visit the page;<br/>Other keywords that appropriately refer to the content of the web;<br/>(Even irrelevant keywords or hidden content however keyword stuffing might actually be punished by more recent algorithms for ranking);<br/>Meta attributes for robots (such as follow/nofollow, index/noindex) that give indication about whether or not to index the page;<br/>Meta language descriptors (for example to recognize in some language rather than English) to appropriately index content based on the dictionary of that language;<br/>This is related to indexing, because web crawlers visit pages and inspect their content based on the terms that they have to search;<br/>The ranking algorithms give value to the pages once that they are indexed, based on parameters that depend also on the ease of retrieving the pages/number of authoritative in-links/number of hits (or visitors);<br/>The Search Engine can be optimized so that the best use of meta-tags can be made with the objective of indexing and ultimately ranking;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.9",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-2-searching-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The machine instruction cycle is a sequence of actions that a central processing unit (CPU) performs to execute each machine code instruction in a program.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State where the program is held.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the part of the central processing unit (CPU) that performs the decoding.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the function of the memory address register (MAR).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>RAM;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>CU/Control Unit;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>MAR (is a register in the CPU that) stores the address of the (next) instruction/data;<br/>to be read from/written to RAM;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>A small number of candidates recognised that a program is held in random access memory (RAM) when it is being run. However, a large number of candidates gave less precise or incorrect answers, such as 'in the computer' or 'on the drive'.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates correctly identified the control unit as the part of the central processing unit (CPU) that decodes instructions. Some candidates incorrectly stated the ALU or the CPU, the latter being taken from the question.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A good range of answers was seen for this question with candidates recognising that the memory address register stores the address of the data to be read from or written to the RAM. Candidates who gave a correct answer either achieved one or both marks, depending on whether they covered part or all of the answer.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "20N.1.SL.TZ0.4",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>database management system</em> (<em>DBMS</em>).</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> characteristics of logical schema.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the purpose of a data definition language (DDL).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> tasks that a database administrator carries out to ensure the security of the database.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1 max]</strong></em>.<br/>A database management system (DBMS) is a set of programs that allows to read, store, change /extract data in a database;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each characteristic of logical schema identified up to <strong>[3 max]</strong></em>.<br/>All attributes for each entity in database are specified; <br/>The primary key for each entity is identified; <br/>Includes all entities and relationships among them; <br/>Keys identifying the relationship between different entities (foreign keys) are specified; <br/>Normalization occurs at logical level;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each identifying the role of Data Definition Language (DDL) and <strong>[1] </strong>for an expansion or explains how it may be used up to <strong>[2 max]</strong></em>.<br/>The Data Definition Language (DDL) are commands which define the different structures/objects in a database;<br/>Database administrators use these commands during the setup and/or removal of a database and database objects (such as tables, indexes, and users);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each task that a database administrator carries out to ensure the security of the database up to <strong>[2 max]</strong></em>.<br/>DBA sets access levels and passwords;<br/>DBA manages back-up procedures (allow examples of this);<br/>DBA establishes a recovery plan for the database in disaster case;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.2.SL.TZ0.2",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model",
      "a-3-further-aspects-of-database-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A school teacher decides to write a program to store class records and marks. Part of this program involves using a sort algorithm. The algorithm shown is a selection sort and to test it, the teacher has set up an array <code>VALUES[]</code> with 5 elements of test data.</p>\n<pre>LIMIT = 4<br/><br/>loop COUNTER1 from 0 to LIMIT – 1<br/>MINIMUM = COUNTER1<br/><br/>  loop COUNTER2 from COUNTER1 + 1 to LIMIT<br/>    if VALUES[COUNTER2] &lt; VALUES[MINIMUM] then<br/>      MINIMUM = COUNTER2<br/>    end if<br/>  end loop<br/><br/>  if MINIMUM ≠ COUNTER1 then<br/>    TEMPORARY = VALUES[MINIMUM]<br/>    VALUES[MINIMUM] = VALUES[COUNTER1]<br/>    VALUES[COUNTER1] = TEMPORARY<br/>  end if<br/><br/>end loop</pre>\n</div><div class=\"specification\">\n<p> <img src=\"data:image/png;base64,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\"/><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> variables that have been used in the algorithm.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the table below to trace the algorithm using the data set:</p>\n<p>20, 6, 38, 50, 40</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the algorithm in the flow chart, construct this algorithm in pseudocode so that it performs the same function.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the type of sort in the algorithm constructed in c(i).</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm fragment to output the data in the array <code>VALUES[].</code></p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The sorting algorithm could be part of a sub-program within a larger program.<br/><br/>Explain the benefits of using sub-programs when constructing a larger program.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for any <strong>two</strong> from</em>:</p>\n<pre>LIMIT;<br/>MINIMUM;<br/>COUNTER1;<br/>COUNTER2;<br/>TEMPORARY.</pre>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award <strong>[5 max]</strong></em>.<br/>Both <code>COUNTER1</code> and <code>COUNTER2</code> correct;<br/><code>MINIMUM</code> column correct;<br/>Final <code>VALUES[]</code> 0, 1, 2 correct;<br/>Final <code>VALUES[]</code> 3, 4 correct;<br/><code>TEMPORARY</code> column correct;</p>\n<p><em>Note to examiners</em>:<br/><em>Allow follow through (FT)</em>.<br/><em>In case of different representation of values in columns COUNTER1 and COUNTER2, then FT, award marks for the correct values of the variables MINIMUM and TEMPORARY</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Use of correct nested loops;<br/>Correct use of flag;<br/>Inner loop checking adjacent cells;<br/>Values being swapped if necessary;</p>\n<p><em>Example algorithm 1</em>:</p>\n<pre>LIMIT = 4<br/>FLAG = TRUE<br/>loop while FLAG = TRUE<br/>  FLAG = FALSE<br/>  loop COUNTER from 0 to LIMIT - 1<br/>    if VALUES[COUNTER] &gt; VALUES[COUNTER + 1] then<br/>      TEMPORARY = VALUES[COUNTER]<br/>      VALUES[COUNTER] = VALUES[COUNTER + 1]<br/>      VALUES[COUNTER + 1] = TEMPORARY<br/>      FLAG = TRUE<br/>    end if<br/>  end loop<br/>end loop</pre>\n<p><em><strong>A recursive solution is allowed at HL. SL candidates who submit an above level recursive solution should also receive credit</strong></em>.</p>\n<p><em>Version 1 – basic recursive solution</em></p>\n<p><em>Award <strong>[3 max]</strong></em>.<br/>BUBBLESORT defined as a procedure with correct pass through parameters and end/return statement;<br/>Correct loop with values swapped if necessary inside procedure;<br/>Recursive call of BUBBLESORT with parameters passed;<br/>Correct condition for recursive call;</p>\n<p><em>Example algorithm 2</em></p>\n<pre>LIMIT = 4<br/>BUBBLESORT(VALUES, LIMIT)<br/>    loop COUNTER from 0 to LIMIT - 1<br/>      if VALUES[COUNTER] &gt; VALUES[COUNTER + 1] then<br/>        TEMPORARY = VALUES[COUNTER]<br/>        VALUES[COUNTER] = VALUES[COUNTER + 1]<br/>        VALUES[COUNTER + 1] = TEMPORARY<br/>    end if<br/>  end loop<br/>  if LIMIT – 1 &gt; 1 then<br/>    call BUBBLESORT(VALUES, LIMIT - 1)<br/>  end if<br/>end BUBBLESORT</pre>\n<p><em>Version 2 – more efficient recursive solution</em></p>\n<p><em>Award <strong>[3 max]</strong>.<br/></em>BUBBLESORT defined as a procedure with correct pass through<br/>parameters and end/return statement;<br/>Correct loop with values swapped if necessary inside procedure;<br/>Recursive call of BUBBLESORT with parameters passed;<br/>Correct condition for recursive call;<br/>Correct use of flag;</p>\n<p><em>Example algorithm 2</em></p>\n<pre>LIMIT = 4<br/>BUBBLESORT(VALUES, LIMIT)<br/>    FLAG = FALSE<br/>    loop COUNTER from 0 to LIMIT - 1<br/>      if VALUES[COUNTER] &gt; VALUES[COUNTER + 1] then<br/>        TEMPORARY = VALUES[COUNTER]<br/>        VALUES[COUNTER] = VALUES[COUNTER + 1]<br/>        VALUES[COUNTER + 1] = TEMPORARY<br/>        FLAG = TRUE<br/>      end if<br/>    end loop<br/>    if LIMIT – 1 &gt; 1 and FLAG = TRUE then<br/>      call BUBBLESORT(VALUES, LIMIT - 1)<br/>    end if<br/>end BUBBLESORT</pre>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Bubblesort;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Use of (any type of) loop;<br/>Correct output statement;</p>\n<p><em>Example algorithm</em>:</p>\n<pre>loop COUNTER from 0 to LIMIT<br/>  output VALUES[COUNTER]<br/>end loop</pre>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Sub-programs contain reusable code (for use in other programs);<br/>A large programming project can be divided into sub-programs;<br/>Different sub-programs can be constructed by different programmers;<br/>Future maintenance is easier due to the organisation of the large program (into sub-programs);</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>No real problems noted with this question other than a few candidates did not accurately copying the variable names as provided in the algorithm.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many fully correct or partially correct responses to this question were seen, however, a significant number of candidates did not fully trace the algorithm to its conclusion, therefore limiting their mark for the question.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates attempted to construct this algorithm and the vast majority made use of pseudocode, rather than a programming language, unfortunately, very few achieved full marks. The usual reason was that candidates did not give a nested loop, which made the awarding of full marks difficult, as the outer loop also makes use of a flag as its terminal condition, therefore also affecting one of the other marking points. Many candidates incorrectly made use of the conditional IF statement as though it was a loop, thereby creating an ‘IF loop’.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates recognised that the algorithm was a sorting routine, but not all specified the correct type. Other candidates thought it was a search routine.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A number of versions of correctly operating responses were seen for this question, some of which were given in pseudocode, others were in programming code. However, a small number of responses were not given inside a loop, so therefore would only print one of the values.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many responses seen were quite brief making it difficult to award all the marks, as the responses did not cover sufficient distinct marking points. However, the responses seen demonstrated that candidates understood the benefits of using sub-programs when constructing a larger program, and with a bit more detail in their answer, could have achieved full marks.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19M.1.SL.TZ0.16",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>The Ministry of Tourism</em> intends to include data from a number of sources in a data warehouse in an attempt to improve the services the tourism companies offer to their customers.</p>\n</div><div class=\"specification\">\n<p><em>The Ministry of Tourism</em> will use data mining to provide the Government with information that it can use to promote and support tourism in the country.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>four</strong> sources of information that could be incorporated in the data warehouse.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> differences between a data warehouse and database.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the Extract, Transform, Load (ETL) processes can be used to address the problems related to data migration.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> ethical problem that may result from data mining.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how cluster analysis can be used to improve the advertising strategy of the tourism companies.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the importance of link analysis in exploring patterns in data mining.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for each source of information up to <strong>[4 max]</strong></em>.</p>\n<p>For example,<br/>Sources of travel information such as airports, ports, bus/train;<br/>Sources of personal data about tourists (such as age, gender, etc.);<br/>Sources of social data about tourists (such as family status, occupation, economic circumstances, etc.);<br/>Sources of information about the tourism products and services these tourists booked such as:</p>\n<ul>\n<li>accommodation (hotels, camps, apartments);</li>\n<li>attractions (natural parks, palaces, museums);</li>\n<li>activities (yachting, biking, diving, fishing, surfing);</li>\n</ul>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for each identifying a difference between a data warehouse and a database and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong>.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Database is based on operational processing;<br/>Whilst data warehouse is based on informational processing;</p>\n<p>The operations on databases consist of transactions;<br/>Whilst operations on data warehouse consist of queries;</p>\n<p>Database is used for everyday transactions;<br/>Whilst data warehouse is used for decision support;</p>\n<p>Database mainly stores current data;<br/>Whilst data warehouse stores historical data;</p>\n<p>Data in the database is always up to date/accurate;<br/>Whilst the data in data warehouse is maintained over time;</p>\n<p>Data in the database is simple/primitive/detailed;<br/>Whilst data warehouse holds summarized/consolidated data;</p>\n<p>The type of access to data in database is read/write;<br/>Whilst mostly read only access for the data stored in data warehouse;</p>\n<p>Database size is smaller (for example in up to 10GB);<br/>Than the size of data warehouse (for example 100GB to TB);</p>\n<p>View of data in database is flat/relational;<br/>Whilst the view of data in data warehouse is multidimensional;</p>\n<p>Users (end-users) of the database are common users (for example customers, tourists, clerks, etc.);<br/>Whilst end-users of data warehouse are knowledge users (for example, analysts, managers, executives);</p>\n<p>The number of database users is greater;<br/>Whilst the data in data warehouse is used by smaller number of people;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong>.</em><br/><strong>Extract data</strong><br/><em>Award <strong>[1 max]</strong> for an explanation of how the extraction process can be used to address the problems related to data migration</em>.</p>\n<p>Pulling data from different source databases/ from various source systems (e.g. MS Access, SQL Server, Oracle, etc.);</p>\n<p><strong>(Cleanse and) transform data (so that data is clean and useful for a purpose)</strong><br/><em>Award <strong>[1 max]</strong> for an explanation of how the transformation process can be used to address the problems related to data migration</em>.</p>\n<p>Trimming for white space/ proper data type/do some validation, etc.;</p>\n<p><strong>Load data</strong><br/><em>Award <strong>[1 max]</strong> for an explanation of how the load process can be used to address the problems related to data migration</em>.</p>\n<p>Transfer data into data warehouse/ data mart/operational data store so it can be used for the mining processes within the data warehouse;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for each identifying an ethical problem that may result from data mining and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong></em>.</p>\n<p>Data mining raises privacy concerns when people are traced;<br/>Their actions are analyzed without their knowledge;</p>\n<p>Data mining creates people/tourist/customer files with a tendency of judging and treating people on the basis of group characteristics;<br/>Instead of on their own individual characteristics;</p>\n<p>Data mining can provide information that could be used unethically;<br/>For example, increasing profits by selling this information to others / or by targeting return tourists with information obtained on their previous visits;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[2 max]</strong> for evidence that candidates understand how cluster analysis is used and <strong>[2 max]</strong> for an explanation related to tourism products/services</em>.</p>\n<p><strong>Cluster analysis</strong>:<br/>Subdivide tourists into distinct subsets;<br/>Where any subset may be selected as a market target;</p>\n<p><strong>Explanation related to tourism products/services</strong>:<br/>Subsets could be “young tourists with limited budget”;<br/>For this subset music festivals, sport could be advertised to ensure the hotel can maximize its revenue;</p>\n<p>Subset of tourists traveling with young children;<br/>Children activities / sports / games available / “kids hotels” could be advertised to ensure the hotel can maximize its revenue;</p>\n<p>Subset of older tourists influenced by cost–value considerations and/or secure/or quiet environment;<br/>So these hotels can be configured to specifically meet their needs and promoted only to these tourists such as advertise “stay 7 days and pay only 6 days” / advertise hotel located in the quiet place/secure place / at some distance from the center;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for each identifying the importance of link analysis in exploring patterns in data mining and <strong>[1]</strong> for each subsequent expansion up to <strong>[3 max]</strong></em>.</p>\n<p>Link analysis technique is used to analyse connections/links between small instances of relational databases / only on required datasets of database/;<br/>(But) it can also be applied on large number of databases to show the relationships between these databases;<br/>Link analysis is important because it discovers new relations in relational databases / new patterns of interest / checks the similarity between the datasets / finds anomalies where old/known patterns are violated;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "18N.2.HL.TZ0.4",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-4-further-database-models-and-database-analysis"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p style=\"text-align: left;\">Simulation software can be used to produce a 3D visualization of rising sea levels that change as the user alters the percentage of ice that has melted.</p>\n<p style=\"text-align: center;\"><strong>Figure 3: 3D visualization of rising sea levels</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: center;\">[Source: Courtesy NASA/JPL-Caltech.]</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>visualization</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify how a 2D visualization could be used in this scenario.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the benefits of using visualization when simulating rising sea levels.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Once the 3D visualization has been rendered, when the user drags a slider bar to simulate the amount of ice that has melted, the visualization is re-rendered without any delay.</p>\n<p style=\"text-align:center;\"><strong>Figure 4: Slider bar to simulate different percentages of sea ice</strong></p>\n<p style=\"text-align:center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align:center;\">[Source: Courtesy NASA/JPL-Caltech.]</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>A visualisation is graphical software technique that creates an image / diagram / graph to communicate data;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>2D map of a coastline;<br/>standard flat map;<br/>cross-section of the land;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/>Visualizations provides a quick way of seeing where the sea levels are now and where they will be if the glaciers melt;<br/>People can understand a visual image more easily than raw data;<br/>No need to interpret figures so time is saved;<br/>A visual of land being claimed by the water has greater emotional impact that raw data;<br/>Public / politicians are more likely to be influenced by a visualization than raw data;</p>\n<p><em>Accept other reasonable reasons</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>Wireframe model is created with polygons;<br/>The 3D rendering process automatically converts a 3D wireframe model into 2D images on a computer;<br/>2D images are combined to create a 3D image;<br/>3D renders may, or may not, use photorealistic effects;<br/>It is likely that scanline rendering would be used because it uses less processing / system resources than ray tracing;<br/>it is unlikely that ray tracing would be used because of the time taken for rendering;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.6",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-3-visualization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Genetic algorithms are used to influence the behaviour of non-playing characters (NPCs) in role-playing games (RPGs). NPCs are computer-controlled characters that the player interacts with but does not control.</p>\n</div><div class=\"specification\">\n<p>A series of generic NPCs are being developed for use in any multi-player RPG. The environmental and social interactions of these NPCs are being developed using supervised learning and will continue to develop as the game is played.</p>\n</div><div class=\"specification\">\n<p>Once NPCs have completed their supervised training, they are placed in a dynamic game environment. Each NPC learns to adapt its behaviour to its environment.</p>\n<p>At this stage of the training, NPCs will not be verbally communicating with other characters in their environment.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> advantages of developing NPCs using genetic algorithms.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how supervised learning of a genetic algorithm could be used to support the way NPCs learn.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the way an NPC in a game could adapt its behaviour when moving and interacting with its environment.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The last stage of NPC development is verbal communication with player characters and other NPCs. To assist with this process, it was decided to explore research related to chatbots.</p>\n<p>Explain the benefits of giving NPCs the functionality of chatbots.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>No need to anticipate and code in advance the reactions that NPCs need to display;<br/>since the supervised training will ensure the characters can adapt their responses;</p>\n<p>Save programmers development time;<br/>because NPCs can be used in similar RPG games;</p>\n<p>Provides greater game realism;<br/>NPC characters would evolve to behave more like real people;</p>\n<p>NPCs would remember previous encounters with players;<br/>thus, altering their gameplay rather than running through a loop;</p>\n<p><em>Mark as 2 + 2</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>Start with a set of “units” (here, NPCs) with some predetermined code/behaviour with added randomness / genetic algorithms work successively towards a solution from a starting point;<br/>Apply a process of selection to the units, so that the probability of survival depends on their success against some predetermined criteria;<br/>Supervised learning has training data with a known set of responses to a known set of problems;<br/>The fitness function (defined by the programmers) limits the adaptability of a given population of NPCs to the skills or tasks defined by the fitness function;<br/>A <em>(fitness) function</em> is used to measure the effectiveness of a solution / offspring (respawned NPCs) are evaluated using a fitness function;<br/>The best solutions are retained / worse solutions are discarded / the most adaptive NPCs will remain in the population and others removed (die);<br/>The retained solutions are mutated (randomness) to generate another set of solutions / retained NPCs are mutated and respawned / recloned;<br/>This process is repeated until the best solution is identified / repeated until the most adaptable NPC model is identified;</p>\n<p><em>Accept other reasonable reasons or examples</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>The NPC makes a random set of moves;<br/>Records the location/identity of objects relative to its current position;<br/>Building up a map of identifyable surrounding objects;<br/>This is repeated until all objects in the space have been placed in distance and direction from a starting point;<br/>The NPC would learn how to react to the object (e.g. swim in a river / walk on a path);<br/>NPC would learn where it could walk and where it could not (e.g. river would need to be avoided);<br/>NPCs that die could be regenerated from ones that had avoided dying up to that point in the game;<br/>NPC would adapt to a changing environment e.g. a house built in a game;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Chatbots are designed to output language in response to human language;<br/>Most NPC conversations are scriptive speeches / repetitive…<br/>so using chabots will add more realism to the game;<br/>NPCs could verbally communicate in script or sound form;<br/>There would still need to be some hardcoded responses that relate to the game storyline;<br/>Natural language processing (NLP) could be applied to chatbot conversations;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.2.HL.TZ0.8",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Describe the steps involved in using the bubble sort algorithm to sort an array.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for looping through array;</em><br/><em>Award <strong>[1]</strong> for comparing each pair of adjacent items (in the array);</em><br/><em>Award <strong>[1]</strong> for swapping them if they are in the wrong order;</em><br/><em>Award <strong>[1]</strong> for repeating this until no swaps is required (which indicates that the array is sorted);</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were required to write the main steps involved in a bubble sort algorithm. Candidates who wrote their answer in algorithmic form achieved better results than those who simply described the process. The full range of marks, from zero to four were achieved for this question. Some candidates lost out because they described a different sort routine than the bubble sort required in the question. Other candidates were able to demonstrate well how the bubble sort operated for one pass of the algorithm but forgot to mention that it would repeat until no swaps had been required.</p>\n</div>",
    "question_id": "20N.1.SL.TZ0.5",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A restaurant uses an object-oriented program to manage the cost of the food and drink consumed by customers. Everytime a table is occupied a <code>Payment</code> object is instantiated which will contain details of the items ordered. As each item is ordered, a <code>FoodItem</code> or a <code>DrinkItem</code> object is added to the <code>Payment</code> object as appropriate.</p>\n<pre><strong>public class</strong> Payment<br/>{<br/><strong>  private</strong> FoodItem[] fi = <strong>new</strong> FoodItem[100];<br/><strong>  private int</strong> fiCount;<br/><strong>  private static double</strong> foodTax = 0.2;   // 20 % sales tax added to<br/>                                         // all food prices<br/><strong>  private</strong> DrinkItem[] di = new DrinkItem[100];<br/><strong>  private int</strong> diCount;<br/><strong>  private static double</strong> drinkTax = 0.1;  // 10 % sales tax added to<br/>                                         // all drink prices<br/><strong>  public</strong> Payment()<br/>  {<br/>    fiCount = 0;<br/>    diCount = 0;<br/>  }<br/><strong>  <br/>  public</strong> DrinkItem getDi(int x)<br/>  {<br/><strong>    return</strong> di[x];<br/>  }<br/><br/>  // all other accessor and mutator methods are included<br/>  <br/>  // addFoodItem() – this method adds a new FoodItem object<br/>  // addDrinkItem() – this method adds a new DrinkItem object<br/><br/><strong>  public static double</strong> findPrice(Item[] pl, String c)<br/>  { //code missing }<br/><br/>  // calculateBill() – This method returns the bill (the total value of<br/>  // the items consumed for a particular table)<br/>}<br/><br/><strong>public class</strong> FoodItem<br/>{<br/>  <strong>private</strong> String itemCode;<br/>  <strong>private int</strong> quantity;<br/><br/>  <strong>public</strong> FoodItem(String x, int y)<br/>  {<br/>    itemCode = x;<br/>    quantity = y;<br/>  }<br/>  // all accessor and mutator methods are included<br/>}</pre>\n<pre>The DrinkItem class is defined in a similar way.</pre>\n</div><div class=\"specification\">\n<p>Whenever a <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>Payment</mi></math> object is instantiated, the variables <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>fiCount</mi></math> and <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>diCount</mi></math> are initialized to 0 through the code in the constructor.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline an alternative method of initializing these variables that would not require the use of the code in the constructor.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the implication of the use of the term <code>static</code> in the <code>Payment</code> class.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to <strong>two</strong> examples from the classes in the resource, explain the benefits gained by the use of different data types.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:center;\">Describe the purpose of the following statement:<br/><br/><code><strong>private</strong> FoodItem[] fi = <strong>new</strong> FoodItem[100]</code></p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The <code>Payment</code> class method <code>addFoodItem()</code> is passed a <code>FoodItem</code> object as a parameter.</p>\n<p>Construct the method <code>addFoodItem()</code>.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying the code and <strong>[1]</strong> for identifying the new position</em>.<br/><code>private int fiCount = 0</code> and <code>private int diCount = 0</code>;<br/>In the variable/attribute section of the class and not in the constructor);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em><strong>EITHER</strong></em><br/>The <span style=\"text-decoration:underline;\">values</span> of these (static) variables;<br/>Are the same for all objects;<br/>They belong to the class (not to the objects);<br/>And are only created/declared once;<br/><em><strong>Note</strong>: Do not accept “cannot be changed”</em>.</p>\n<p><em><strong>OR</strong></em><br/>The static methods;<br/>Are class methods (not object methods);<br/>Are independent of the objects in the class;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 × 2]</strong> for each clear example. Only award <strong>[2]</strong> for an example if the benefit is clearly shown</em>;</p>\n<p>Having different data types allow different operations to be carried out depending upon the type;<br/>e.g.<br/>Calculations in the integer “quantity” variable;<br/>which are not possible for a String;</p>\n<p>Double instead of integer allows the use of decimals;<br/>which mirrors real-life scenarios/allows more precise calculations;</p>\n<p>Using an array (of objects);<br/>Allows individual items to be accessed/processed more easily;</p>\n<p>Memory usage can be reduced;<br/>e.g. “<code>fiCount</code>” takes up less space as an integer than it would as a double (assuming it’s just a number);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The variable <code>fi</code> is declared as an array of <code>FoodItem</code> objects / of type <code>FoodItem</code>;<br/>With (a maximum of) 100 values;<br/>It cannot be directly accessed/it is encapsulated within the Payment class;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each correct line of code</em>.</p>\n<pre>public void addFoodItem(FoodItem f)<br/>{<br/>  fi[fiCount] = f;<br/>  fiCount++;<br/>}</pre>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.10",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Pugh University has a website that allows computer science students to enter their name and customize the web pages.</p>\n<p>Consider the section of HTML and CSS code shown below.</p>\n<p>File name: index.html</p>\n<pre>&lt;!DOCTYPE html&gt;<br/>&lt;html&gt;<br/>&lt;body&gt;<br/>&lt;script&gt;<br/>    function setVar(){<br/>    var subject=document.getElementById(\"kippers\").value;<br/>    document.getElementById(\"subj\").innerHTML = subject;<br/>    }<br/>&lt;/script&gt;<br/>&lt;h2&gt;My First Page&lt;/h2&gt;<br/>&lt;p&gt;Welcome to the &lt;span id=\"subj\"&gt;&lt;/span&gt; Faculty&lt;/p&gt;<br/><br/>&lt;input type=\"text\" id=\"kippers\"/&gt;<br/>&lt;button onclick=\"setVar();\"/&gt;clickme&lt;/button&gt;<br/>&lt;/body&gt;<br/>&lt;/html&gt;</pre>\n</div><div class=\"specification\">\n<p>When a student enrolls, they must enter their name on a web page. Before being added to the database, the system should check that the name typed is not blank and that it has not already been added to the database.</p>\n</div><div class=\"specification\">\n<p>Consider the following URL:</p>\n<p style=\"text-align: center;\">https://www.follettibstore.com</p>\n</div><div class=\"specification\">\n<p>The common gateway interface (CGI) offers a standard protocol for web servers.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the output of the <code>index.html</code> file in the web browser.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the processing that takes place when the user inputs “Pugh” into the text box and then uses the <code>clickme</code> button.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why these <strong>two</strong> validation checks occur on different computer systems.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how website certificates are used to authenticate a user’s browser through secure protocol communications like HTTPS.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the working of the common gateway interface (CGI).</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Pugh University uses cloud computing services, such as Google Docs or Office 365.</p>\n<p>Describe how cloud computing is different to a client–server architecture.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for the text within the &lt;p&gt; tags (My First Page large font/Welcome to the Faculty in a smaller font;</em><br/><em>Award <strong>[1]</strong> for the text box and correctly labelled button;</em></p>\n<p><em><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANsAAAB2CAYAAABBAeZwAAAgAElEQVR4nO2deVRT1/r3vyQEQSDMoIAFLaAWkVprr0UrgygWBUGGqwIizkoVLata68/W5VjtLa21VOqAVSi3F2zVWhSxDkWtKDigojgCChSvzITBQPK8f3A5LyEJkxgr7s9aWeTs8dkn53vO3s/enK1GRAQGg/Hc4b1oAxiMVwUmNgZDRTCxMRgqgomNwVARTGwMhopgYgNQWlqKAwcOIDs7+0WbwujBKBSbj48P1NTUFH5SU1PbLTQlJUVhXnV1dVy5cqXbjC8uLoaWlpZSW9v6jB8/HgBQUVGBN998E1OmTMGQIUPwyy+/dJt93YmXl1eb7dHQ0ICFhQWGDh0KX19fJCYmor6+/kWbzWgJKeH+/fvUr18/AiDz8fb2VpaFY+LEiXL5IiMjqb6+vt28nUUikdB3330nV98bb7xBt27dooKCAsrOzqYff/yR3NzcuHg3NzciIjp58qRMvhkzZnS7jcqIiIig9evXdyitVCqlc+fOkUAgkLH3n//8J92+fZt+/vlnevvtt2Xihg4dStXV1c+5FYyOolRsRERTp06Vu4gBUG1trdI8IpFIYZ5Lly51u/HNVFVVydUXFBQkl66xsZEsLS1lxJaeni6Tb/Hixc/NzpZcv36dAFBERESn8jk4OMjYGxMTw8VJpVKytraWif/000+723RGF2lzzKajowMA6N27t0z4r7/+qjTPb7/9Bn19ffTp00cmXF9fv62qngldXV25MDU1NbkwPp+PDRs2yIQNGzYMY8aMAQAYGxsjLCzs+RjZgqdPnyI8PLxLeds6j2pqahg7dqxM2Pnz57tUD6P76ZCDZObMmTLH3333ndK03377LWbOnAmhUCgTrq6u3gXzuoeGhgasW7cOADBt2jT079+fi9PQ0MDp06dRWFiIgoICDBs27Lnacv36dTg6OiItLa1L+ds7j2+88YbMcUZGBoityPtb0CEFhIWFIT4+HlVVVQCAtLQ0ZGVlwdHRUSbd3r17cfbsWcTGxuLIkSMKy6qvr0dKSorCOCsrKwwePFgufvTo0TA2Nu6IqQrJzMzE1q1bsXr1aggEAuzbtw8ZGRkoLCxERkaGTNo33ngDdnZ2XHxLrK2tMXToUCQmJuLPP/+EpaUlwsPDoa2tDQCorq7GL7/8gmvXroHH48He3h6Ojo44dOgQ1qxZg8uXL+P999/Hf//7X67MO3fu4ODBg9DT04Orq2uX29hMsy3N1NXVcd+lUilOnjyJjIwMlJWVob6+HmZmZhgxYgTc3d3B5/MVlpmWlobz58+joKAAWlpasLa2xrhx42R6Pnp6elz6xsZGHD9+HBcuXEBZWRkGDx6MoKAguRvwK0dbfcw5c+YQAMrJyaElS5bIjAXCwsJk0ubl5REA8vDwICIiOzs7mfT5+flERFReXk7BwcHUu3dvmXhbW1vat28flZeX04wZMwgA8fl88vb2ppycnHb7w2g1ZgsODubiZs6cSUZGRnJ5Lly4QLa2tgrHONHR0WRgYCATFxISQgEBATJhCxYsICKimJgY0tDQIAA0duxYWrZsGVlZWREAMjExISKi8ePHKxzP4n8OnY7g6uqqdMxGRPThhx/KxL/33ntERHT37l2yt7fnnFwrVqygoKAgLp21tTX3GzWzY8cOzkkmEAho2bJlNHLkSDnbfXx8uDy5ubnk5OTE1f3BBx8QAOrTpw+dP3+ezp8/36F29kQ6LLa7d+/KnGBNTU0qLS3l0i5fvpwA0KFDh4hIudia+eKLL5Q6JqRSKRkbG9OHH37Y8Ya0ugCmTZtG+fn5tHHjRgKgUGxERBkZGUodCmvXrpUrV19fX+ZG8d5779Fff/1Fmpqa3Hmpq6sjoibHjaOjIyc2qVRKq1evlilvyZIlJJVKSSqVdqidbYktNzeXLCwsZOKPHDlCRETu7u4EgIRCIWVlZXF5pk2bxqUdO3YsF37ixAmZchYtWkRERCUlJTLhK1eupF9//ZWIiGprazlBCwQCKi8vJyIib29vAkDm5uZkbm7Ohb9qdHhS28bGBpMmTeKO6+vrsWvXLgBNXZXdu3fD2dkZEydO7FB5CxYsgJGREXf8008/QSwWAwCuXLmCkpISLFy4sKPmyfHvf/8bVlZW+OSTT9pM17L705rWzgg+n4+zZ8/i6NGjXL4pU6bg4sWL3JxWfX09Tp06BaDJcRMdHc3lV+S0aTlX1hV+//13rF27FpMmTUL//v25rq+ZmRkSEhLw/vvvAwAuX74MAKiqqoK7uzuX397envv+559/QiKRAGgaErRkxIgRAAAjIyPY2dlx4erq6vDy8kJdXR1cXFy4hQHDhw/nzp+DgwMAoKioCEVFRYiLi+tSW192OrWCZPHixTLH27dvh0QiwU8//QSpVIr9+/cr7fe3RkdHB3PmzOGOnzx5gsOHDwMA4uLiMHr0aNjY2HTGPBkmTJiA06dPIygoqMtltGbYsGGwt7fHmDFj8Ndff6G4uBhLly6VS+fp6YmgoCDcvn0bTk5OSE9P7zYbWnPr1i1cunQJWlpaCAkJwfLly5GSkoJ79+5h2rRpXDoXFxcATTeMjz76iAvX0NDgvtfV1aG8vBwAuL/N9OrVi/veclx448YNAEB2djYuXrzIhRsZGUEkEkEkEsnkBfBcz8ffmU65CN3d3TFw4EDcvn0bAJCXl4fffvsN0dHRCA0N7bQTY+HChdi8eTN3HBsbCx8fH/z000+c97CrGBsbw9nZGc7Ozqirq8Mff/zxTOUBsk86LS0taGlpAWhy4Ojp6aGyspKLT0hIQEJCAsLCwhAVFfXMdStj8eLFmD9/frvpEhMTceXKFbz22mvo1asX9u/fj/379yt1ZLV8egGASCTivpeWlnLfDQ0NAYC7JppJTk5WOCUDAA8fPmzX3p5Ip55sPB4PH3zwgUxYREQELl26hEWLFnW6cisrK3h6enLHR44cwY8//ojKykoEBgZ2ujxlREREdFtZijA0NERSUhL69esnF7dnzx54eHigsbHxudrQHnw+H/X19ZgxYwb09fUREBAAiUSCCRMmKEy/bNkyTkhAk3gAICcnR0YszU/Plh5WoGnYUVBQoPBz8ODB7m7eS0GnFyL7+fnJHOfn58PBwQG2trZdMqDlOBAAli5ditGjR3erm7j13NPzYNy4ccjLy8PUqVPl4i5evPjCJ5dPnz6N9957D8eOHQMATJw4EUlJSUrPjYWFBbZv384dHzp0CMuXL0dISAgXNmvWLG4SvWV3FGh6ellYWCj8tByrv0p0Wmx9+/aFk5OTTNjkyZO7bEBrsZWXl3OLhLsLIyOj5zoov3PnDtLS0sDj8RAfH4+4uDgMGjRIJs3NmzefW/3tUV1djdDQUJmwL7/8st18/v7+SEhIgJ6eHnr16oVDhw6BiODj44Pt27cjJiaGS2tqaiqTVywWo6ioqHsa0ENoU2xSqRRA0/KilrTsSvL5/A6NGZR1o/r16wdvb2+Z8oKDg9strzOoqalxXrnnwR9//MGdg2b7r127JnNelLW/+dxeuHABubm57dbVupyGhoZ285w5c0am66etrc05n5p/49bU1dXB29sb06dPR2VlJW7cuIHbt28jMzMTBw4cwIIFCyAQCLj0o0aNkitj586dMsfp6elIT0/HggUL2rW5J9Km2Jp//MzMTJlwf39/9O3bF0DTU83S0lImXiwWo6CgQCbs3r17SutpeVF6enrKratsj0ePHsmFta5fGTU1NTLHLS/elg4PoMljqoycnBz4+PigoqICACAQCGS6XM7OzgDkpxpSU1ORmJiIyZMnK2xHSyQSidx5bOu8NtP6X21qamqwdetW7Ny5E9u2bZNrR0VFBb7++mtunAYAu3btQnR0NHbu3IkffvgBKSkpePz4MRdvbm6O/fv3ywhw3bp12LBhA86dO4dNmzZh7NixGDt27HNfEve3RdHkW0JCAreKAwDp6upSREQEnT59mkuzbt06AkC///47FyaVSikqKkpu4hX/W6GwZs0aKi4ulquvsbGRW22RlJTU4UnC6upq2rx5s9xK+ObPjBkzaNu2bfT48WOF+XNycsjZ2VnOztTUVDp8+DC9/vrrcmUuXrxYZlKYqGmlRcv8W7dupaSkJBo+fLjchP3Vq1e5lSYtP+Hh4W22NTExkTw9PeXyaWlp0ZIlSyg9PV1p3kePHpFQKJTLy+fzyd/fXy78559/pvnz5ytd7dLyM2nSJKqqqpKxU19fX2Ha1atX0+rVqzvy0/ZIFLr+a2tr0b9/f3z22Wcy4S3vkPPnzwePx4Obm5tMmsrKSowZM4ZbSd9K2NykaUv4fD6mTZuGHTt2wMvLS5FJCiEi1NbWYsqUKZgyZYrCNCUlJaitrVUY19DQABcXF24OqpmnT5+itrZWaXe2urpa5tjY2Bhz5sxBSEgIzpw5g+TkZJSUlKB///4IDw+XWcjt6OiIhw8f4ssvv0RGRgaEQiG8vLwwe/bsNtsqFosxYsQIbnK5NcraCACWlpY4ceIEPv/8c+Tl5aFPnz6wtbXFokWLYG1tDUtLS5w9exYWFhaYO3cuJk6cCKFQiNjY2Ha7qb/99hs2b96M9evXAwACAgIwatQo/Otf/8LVq1dRU1MDW1tbzJs3T+E18SqhRvRiloQXFBSgqqqK84b5+vrC0tJSrlvDeDE8evQIgYGBSE9Ph6mpqYzDp7GxEQ8fPuS66l5eXm3+2xXjf6j6USqRSGj69Olc12Lnzp2Un59PfD6fbt68qWpzGEp46623uN8oOztbLl4qlXJDjYULF74AC18+VP7Cn7KyMiQkJHDHhw8fRlBQEDw9PTF48GBVm8NQQkvnULPTpyUikQj5+fkQCARy/+/IUIKq1S2RSMjU1FRu8NzS0cJ48TT/FwcA0tDQoLVr19K5c+fowoULFB0dzb1e4qOPPnrRpr40vJAxW35+PiIjI3H27FmYmppixowZiIyM7PLKd0b30/yfHGlpaXj48CHu3r0LkUgEbW1tmJiY4K233sKUKVPg5+cHHo+9EbEjvDAHCYPxqsFuSQyGimBiYzBUBBMbg6EimNgYDBXBxMZgqAgmNgZDRTCxMRgqgomNwVARTGwMhopgYmMwVAQTG4OhIpjYGAwVwcTGYKgIJjYGQ0UwsTEYKoKJjcFQEUxsDIaKYGJjMFQEExuDoSKY2BgMFcHExmCoCCY2BkNFMLExGCrihYitpqZGbieYV5XWG032BKqqql74HuJ/R+S2jJJKpZg3bx63yZ6RkRHmzZsHiUSC6OhobvPAgIAAhIeHAwCioqK4XUxsbGywa9cuhZWtX78eBw4cwOXLlxEXF9ftO4y+TKSmpuLbb7/FhQsXZDYV7Cqff/45UlJS2k0zcuTIZ65LEeXl5diyZQsuX76MU6dO4cGDB3KbZL7yKHoneW1tLdna2hIAOnjwIBd+5MgRAkDvvPOOXJ63336bPD09SSQSKX3XuVgsJjc3NwJAcXFxz/jmdNVy7do1qqur67Yynj59StOnTyehUNgd5hER0ZUrVwgADRo0iOrq6kgsFlNtbS3dv3+fnJ2dZX7L7kYqlZJEIqGVK1cSAHr06BEXl5+fr3RDylcJhd1ILS0t+Pr6AgC++OILLnzChAkwMjLCxYsXUVdX11KwuHXrFuLi4qCtra1U2AKBABYWFt1wi1A9EREREIlE3VaGhoYG+vXr1x2mcQwZMgQAoKOjA01NTQgEAmhpaWHAgAEIDg6GWCzu1vpaoqamBh6Px+3V3ZJPP/0UhYWFz63ulwWlYzY/Pz8AwLlz53Djxg0ATSd04sSJAIDff/+dS5uRkYERI0bA0NDwmYxpbGxUuqE60CTqF0FcXBxOnTqlMI6IOmRXW2U0l6NoV9bOoGxjkqdPn8LX1xfjx4/nwqRSKaqqqpSWJZVKn9keADhx4gTi4+OfuZyegFKxjRgxgtsVdOfOnVx4SUkJACA2NpYLi42NxfTp07njkydPwtPTE/b29njzzTfbvMgA4ObNm/D19cWcOXNgZWWFiIgI7oeWSqX49ttv4enpCXd3dwwbNgy7d+8GEaGiogJRUVEYNmwYfvjhB8ycORM2NjbQ09NDXFwckpOT4eDgAG1tbcyfP19GyB21MSYmBsuWLQMA+Pv7Y/PmzQCAvLw8eHt7IzAwEMOGDYO/vz8yMjIUCi8zM1OmDA8PDxQVFXHxO3bsgImJCXR1dbFy5UouvK6uDl999VWnzqUivv76a9y8eRN6enq4evUqvL294erqilWrVmHgwIHIzMzk0hYWFsLPzw+zZs3CwIED4eTkhF9//RUPHjzAuHHjYGpqilu3buH+/fsIDAyEqakpkpKSFNabn5+PWbNmQSKRYMGCBYiMjERkZCQ+/fRT2NnZwc7ODvPnz0dtbS2qqqoQGBgINzc3lJWVdbqNLwVt9TE3bdpEAMjIyIjq6+upsLCQXnvtNRo4cCDx+XwqLi6m+vp6MjMzo7KyMiIiysrKIltbW6qsrKTGxkZydnYmoVDI9dlDQkJkxmyFhYVkaGhIN27cICKib775hgDQ8ePHqaKigt5++20yNzenyspKIiKKiYkhALR9+3YqKyujtWvXEgB699136cyZM/To0SNydXUlTU1Nmjt3Lt2/f59rx/nz54mIKC4urk0bW7Nx40YCQBUVFUREVFFRQebm5vTFF18QEVFlZSXZ2dlxdnWkDCKiFStWEABatWoVPXnyhBYuXEgAqKioiIiIAgICaNy4cR22s7GxkQCQqakpRUZGUmRkJM2dO5f09PQoLS2NiIicnZ0JAJ09e5aIiPz8/MjDw4OIiEQiEVlZWVFKSgoRESUnJxMA6tu3LxERJSUlEQDKysoiIqJbt24RAPrhhx84G3bv3i0zZmvOc+3aNS5NQ0MDBQYGEgDavXs3F75w4UJKTExU2LaeQJuu/2ZvYWlpKQ4fPox9+/YhKCgIc+bMgUQiwd69e5GcnIxRo0bBwMAAQJPHq1evXli5ciWWLFmC6upqVFVVIT09XWEdmzZtwqhRo2Bvbw8AmDt3Lvbu3Yt3330X33//PTIzM+Hr6wuhUMjFW1lZYcOGDTAwMOC6tZ999hlGjx4NS0tLeHl5ob6+Hlu2bMGAAQMQGBgIoGmf6Nu3byMkJKRTNjbT3E37/vvvUVRUhNDQUACAUCjEihUrAAAbNmzoUBktWb9+PYyNjTF58mQAwN27d3H79m0kJSXByMio03YKhUJ4eXnBy8sLnp6eMmPDRYsWYcWKFbC3t0dJSQnKy8u58VRMTAx0dXXh4eEBoGmMfuDAAezevRsAoK4u67xufdzRdqurqyM+Ph42Njb46quvQERoaGjAyZMn4e3t3eEyXzbaPFuWlpbYsmULli9fjj179uDBgwc4cOAADA0N8fHHH2PPnj0YOHAgd9EBQFZWFtzd3bFq1SqZsvT09BTWceXKFdjZ2XHHmpqamDFjBgDg1q1bACAzFuTxeHBzc8OePXtQWVmp8OJtXVez06ahoQFZWVkAADc3tw7b2Jpmu4yMjLiwsWPHAgAKCgpQWVnZ4bJa0nxDaWnn+vXroaur2yk79fX14ezszB0TEVe2n58fzMzMsHjxYmhoaKChoYHrXl+5cgWmpqZcPh6PBx8fn063QxGtfyeBQIDIyEgsXLgQx44dg1Qqxbhx49CrV69uqe/vSLuT2pGRkRgwYACOHDkCXV1dDBo0CKampvDx8UFOTg7S0tLg6enJpVdXV8edO3dgamoq82k5RmlNZmam3FinpKSEu8iKi4tl4oRCIfh8PrS0tBSW19YOpgKBAAA6bWPLchXZ1SyAtuxqzzZFdiYnJ3faztb4+vrC0dERADBv3jy4uLhg3Lhx2L17t5z3MCsrS8bTDDRNUjc0NHSqzo4wc+ZMmJmZISoqCvHx8Zg1a1a31/F3ol2x8Xg8rhsWFBTEhc+ePRsA4OPjI3M3Gj58OFJSUpCcnMyFxcTE4Oeff1ZY/vDhw3H9+nV88803AJruwlFRUUhPT4erqysA4NChQzI/9oMHD+Dq6goNDQ2FZTbfqVsLGADeeustAOiUjXw+HwBQX18PAJxd+/fv59Lcv3+fi1NkV+syFNnXfExEnJ0REREdtrPZta/Mo1tRUcE5tgICAgA0bUTfnH748OEoLS3F//3f/3G2FBUVYe7cuRCLxVy3sbS0FAA4R0Zbq2AUtbsZTU1NRERE4Pjx47h58yaGDRumtJweQUcGdteuXSM+ny8zMG9oaCATExM6evSoTNp79+6RoaEhASAPDw8aO3YsjRkzhp4+fUpisZicnJwIAK1Zs4aIiB4/fsxthj5kyBAaOnQozZ49m4iaJkqjoqJIR0eHwsPDqa6ujs6ePUsmJiZ0/fp1IiLav38/AaDo6GjOhmXLlhEAOnXqFBERXbhwgQDQJ598QlKplI4dO6bURkUcPHiQANDs2bNp9uzZ1NDQQH5+fmRoaEhZWVn09OlT8vLyIhsbG86utspodgQEBAQQAMrLyyMioj179hAA+u6774iIaMmSJdwm8u3ZWVVVRdu2bSMApKenR7m5uXJpamtrSSgUEgBydnamgIAAGjRoEAkEApo1axZVVVXR4MGDCQDZ2NjQiBEjyMDAgI4dO0ZERLm5ucTn88ne3p4WLVrELVBwcHCgGzdukFQqpcjISAJAZ86cIaL/P9Hu4+NDYWFhFBYWRuXl5ZxNZWVlpKOjQ1FRUQrPW0+Cv2bNmjXtCdLMzAxCoZAblwBNTzxtbW34+flxdy+gaXzl6+vLPe3c3d2xdetWaGhoYMeOHTAzM4OLi0uz0DFkyBCEhoZCV1cXhoaGmDp1KlatWgU1NTWoqanh3XffRWBgIC5duoS9e/eisLAQu3btgp2dHfLy8pCamgoXFxfU19ejoqICT548QVFREVxcXPDo0SOYmpri4MGDcHFxAZ/Ph6amJlxcXJTaqAgbGxvweDyoqalh1apV0NPTg7+/P4yNjREXF4ejR49iwoQJ2LVrF8zMzNotIzQ0FGpqaqirq4OLiwuuX78OCwsLpKWlwcXFBTU1NbCzs0NAQAD+8Y9/oE+fPu3aGR0djfLycri4uGDkyJG4evUqXn/9dZnxnUAggIuLC4RCIfr06YPw8HDMnz8ffD4f06ZNg42NDcLCwmBoaAg9PT14eHggLi4OgwYNAtA0FnRycoJIJIK1tTU2btwIPT09bNiwAebm5oiKigKfz4eLiwsePHgAKysrDB48GIaGhiAiLF26lHNiNaOlpYWEhARERUW1uSCiJ8A2sGe8UB4+fIjQ0NAuzR++bHTcd8tgdBNlZWXIyMjAgAEDsG3bNm5pYE+HPdkYKmfp0qXYunUrgKb1nJmZmT3a5d8MExtD5RQXF+PAgQMQi8UIDg6Wma/syTCxMRgqgr0WgcFQEUxsDIaKYGJjMFQEExuDoSKY2BgMFcHExmCoCCY2BkNFMLExGCqCiY3BUBFMbAyGimBiYzBUBBMbg6EimNgYDBXBxMZgqAgmNgZDRTCxMRgqQuE7SCoqKlBRUaFqWxgvEdbW1m3GS6XSNncketlo3hKroy/ZVYRSsRkbG3fqXe6MV4e8vDylcY2NjSgtLeUuzp5EY2MjevfuDV1d3S6JTqma1NXVoamp+UzGMV4tGhsb8eTJExgbG3OvT+9plJWVobq6mts7oTP0rFsP44VBRCgpKYG2tnaPFRrQ9BLimpqaLm3MycTG6BakUinKy8uho6Pzok157vD5/C6NR5nYGN0CEUEsFj/TOK2iogIFBQUAmrbe+uuvv9qtMzs7W+XbP0ulUvZkY7zcnDhxgnt567Zt2xATE9NmeolEAj8/v+eyndXzgLkbGX9L1q1b90xu9r8jTGwMlVNbW4u0tDTk5+fDwMAAkyZNQu/evWXSZGdnQyAQYMiQIQCA8vJyHD16FCKRCG5ubnKbOAJAWloaCgsLMWnSJNy9exeamprIycmBWCzGpEmTkJubi7Nnz8LBwQGjR4/m8hUWFiI1NRUaGhrw9PTktqzublg3kqEyGhoa0NDQgICAAHzzzTcQiURISUlBUFAQJBKJTNrjx4/j9OnTAID8/Hz4+Pjg6tWrKC4uRmBgIK5duyaT/j//+Q82btwIBwcH6Orq4ujRo1i4cCHu3r2LgwcPIjg4GJs2bUJpaSnCw8OxZ88eAE1zhgEBASguLsatW7cwZcoU1NTUPJf2sycbQ2U0bwslEAjwyy+/gMfjobGxEbGxsaiurlaYRyqVIjg4GEOHDsWWLVsAAKNGjYJIJOLSJCYmIj4+Hvv27ZPZE9zJyQkrVqxAbm4uJk6ciHPnzsHAwACPHz9GUlISwsLCsG/fPowfPx4rV64EAMyZMwfHjx/vtr3EW8LExlAZ+fn5AIDBgwdzXkt1dXXMmzdPaZ7Kyko8efIEdnZ2XFjzppyNjY0AgPXr12Py5MkyQgMACwsLAE17sPfq1YvrHhoYGHBOleLiYly5cgV//vknl6+qquqZ2qkM1o1kqAxLS0tYWlri3r17XJhUKsWRI0eUXuB6enrQ19dHbm4uF3bt2jVkZ2dzx0ePHkVGRgYSEhI6bZOJiQmmTp2K1NRUpKamIjExEf7+/p0upyOwJxtDZbi6ugIAoqKiMHXqVHh4eCA7OxtZWVkYN26cwjw8Hg979+5FaGgoNm3aBH19fcTGxnJTBEDTE2zbtm0ICQlBbW0tZs2a1WGbpk6ditDQUG5/uF27diE+Pp7b2rg7UbindkVFBXR0dNhCZIZCSkpKYGxsLBMmlUrx5MkTbv9vRairq0NdXR3e3t4Amrpwffv2xWeffQYdHR2oqanBxMQEr7/+OgDgtddeg4WFBYyNjeHp6YkHDx5AJBLhgw8+wMiRIwE0rcZ/5513YGZmhkGDBqGoqAhmZmYwMDBA//79YW5uDjU1NWhoaGD48OGcLdbW1nB0dISJiQlGjx6N7OxsiMVifPzxx0EhNSAAAAD+SURBVO0KTSQSoXfv3p2ewFe4P1teXh769OnDFiIzFJKTkyN3QUqlUty5cwfW1tY9/ropKCiAubl5p8XGxmyMboHH48HY2BglJSUv2pTnytOnT9HY2NilZWlMbIxuw9DQEFKpFPn5+airq3vR5nQrEokEZWVlyM3NlfN6dhSlg7K2/kGQ8WrTco6rJTweD+bm5igtLUVubi7EYrHKFwk/L3g8HoRCISwtLaGlpdWlMtie2oxuh4ggkUh6jNCa4fF44PP5Xc7PxMZgqAg2ZmMwVAQTG4OhIpjYGAwVwcTGYKgIJjYGQ0UwsTEYKoKJjcFQEUxsDIaKYGJjMFTE/wM4bdfg7tCqrgAAAABJRU5ErkJggg==\"/></em></p>\n<p><em><strong>OR</strong><br/></em></p>\n<p><em>Award <strong>[2 max ]</strong>.<br/></em>Initially it displays:<br/>My First Page – in large font;<br/>Welcome to the Faculty – in normal font;<br/>A text box + a button labelled “clickme”;<br/>A Then if you type in the text box and then press the button, it displays what is typed between “Welcome to the” and “Faculty” – e.g. type “Science” and it will display; “Welcome to the Science Faculty”;<em><br/></em></p>\n<p><em><strong>Note</strong>: Students either show the output on the web-browser or describe what would be displayed on the web-browser.</em></p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The button’s onclick method is called, i.e. setVar();<br/>This first sets the variable subject to the value of the HTML element with id kippers, i.e. the text box;<br/>and then sets the inner text of the element with id subj, i.e. the span, to “Pugh”, so that the display is “Welcome to the Pugh Faculty”;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for identifying correct validation check.</em><br/><em>Award <strong>[1]</strong> for describing the validation check.</em></p>\n<p><strong>Presence check</strong>:<br/>It happens in JavaScript in the web page on the <strong>client</strong>;<br/>because this will save the server handling many transactions/to avoid sending a null value that will need to be handled by the server;</p>\n<p><strong>Lookup check</strong><br/>It happens on the <strong>server</strong>;<br/>because the database is stored on the server rather than on the client’s computer;</p>\n<p><em>Mark as <strong>[2]</strong> + <strong>[2]</strong></em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The website certificate is used to initiate sessions between the user’s browser and the server;<br/>The web server sends the browser a copy of its SSL certificate;<br/>If the browser trusts the certificate, it sends a message to the web server;<br/>The web server sends back a digitally signed acknowledgement to start an SSL encrypted session;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>The Common Gateway Interface (CGI) is used to provide interactivity to web applications/enable forms to be submitted;<br/>It uses a standard protocol that acts as an intermediary between the CGI program and the web server;<br/>The CGI allows the web server to pass a user’s request to an application program;<br/>And then the forwarded data is received to the user’s browser;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Cloud computing is based on sharing computing resources over the internet;<br/>Cloud computing can be offered as a service to individuals and companies (SaaS) which means they do not have to maintain the infrastructure;<br/>Cloud computing and applications are more scalable than client-server architectures;<br/>Client-server architecture does not need to be operating over the internet, but instead can be limited to a local network;<br/>Client-server architectures may require more maintenance of the infrastructure by the organization who are using it;<br/>cloud computing is typically more resilient than client server, because the services offered are more widely distributed, rather than being concentrated on one server or on a small number of alternative servers;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not all students were familiar with the processing of web scripts. The processing of such scripts is fundamental to the functioning of the web commercially and is an important part of the Web Science course.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not all students were familiar with the processing of web scripts. The processing of such scripts is fundamental to the functioning of the web commercially and is an important part of the Web Science course.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was testing the students' knowledge of client-side and server-side processing. Not many brought this aspect into their answers. As with part (a) more practical experience is recommended.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Poorly answered. Not many understood what these certificates were and even fewer could correctly explain the process involved.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Students were more comfortable with this concept and were aware of its role as an intermediary between application and server.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a comparison question which required relating different aspects to both paradigms. It was not answered particularly well.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.7",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-4-the-evolving-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The World Wide Web can be represented as a web graph.</p>\n</div><div class=\"specification\">\n<p>The World Wide Web has expanded significantly over the last 10 years. In June 2018, there were an estimated 1600 million websites and 4100 million users. This is approximately twice the figures of January 2014.</p>\n</div><div class=\"specification\">\n<p>The World Wide Web can be divided into the text web and the semantic web.</p>\n</div><div class=\"specification\">\n<p>The development of technologies that underpin the World Wide Web has led to the growth of ambient intelligence and collective intelligence.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a web graph can be used to represent the connectivity of the World Wide Web.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why a user can still navigate from one web page to another web page in the same amount of time as previously, even though the number of web pages is significantly larger.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> reasons why there needs to be a balance between expressivity and usability on the semantic web.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> features of ambient intelligence.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the role of collective intelligence in the advancement of human knowledge.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>In graph theory, a graph is a set of nodes (also called vertices) that can be connected through edges;<br/>In a web graph each web page is represented by a vertex;<br/>In a web graph each hyperlink is represented by a directed edge;<br/>Therefore, the web graph provides a representation / model of the connectivity of the World Wide Web;<br/>The direction of the edge is from the page with the hyperlink to the page that is the target of the hyperlink;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Although the growth in the number of web-pages on the World Wide Web has increased exponentially, the diameter of the World Wide Web increases linearly;<br/>The increase in routing hubs may lead to a more efficient routing algorithm being used;<br/>And in combination with the increasing speed of web-pages loading;<br/>Means that despite the significant increase in the number of web-pages, despite there being an increase in the number of hops required to navigate from one web-page, there may be no discernable increase in time for this process to occur;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>The semantic web is based on data and enables computer systems to understand the meaning of information and its relation to other pieces of information;<br/>this increased usability is dependent on the ability of the different computers to process the intended meaning of content better;<br/>however, giving information more expressive power may come at an expense of usability;<br/>for example, a language such as RDF can be highly expressive power, but requires the user to specify relations of information to other information;<br/>in some cases, it may be beneficial to create shared databases (i.e. increase the usability);<br/>but, there may be other cases where the increased expressivity leads to a decrease in usability;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>embedded, devices are integrated into their environment;<br/>context aware, these technologies can adapt to the needs of the user;<br/>machine learning, these technologies can learn from previous behaviors;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>Collective intelligence is the intellectual by product of a group of people working together;<br/>This ability to harness the benefits of collective intelligence have been made possible by the development of the internet;<br/>Collective intelligence can be used for a variety of purposes, for example linked to social media (Facebook and Twitter), gaming sites (Online Multi-player games), Scientific research (Large hadron Collider and Wikipedia;<br/>Collective intelligence benefits from the open sharing of resources and may allow for a more rapid evolution of ideas than ‘traditional’ methods;<br/>Collective intelligence may not have a central authority, so the user created information may be considered to be factual when it is not;<br/>Collective intelligence may lead to a disruption in the way that knowledge is stored or classified, i.e. the development of folksonomies in place of ontologies;<br/>The lack of a formal organizational structure may lead to the acquisition of the collective intelligence being potentially piecemeal or haphazard;<br/>There may be no owner of this additional knowledge, so it may not be stored or maintained in the most efficient manner;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many answers identified the fundamental elements nodes, vertices, edges, and some also made references to the identification of the SCC.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Two reasons, the advance in technology leading to increase in the speed of operations and the linear increase of the web's diameter showed that some schools had investigated this aspect of the developing web.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>There were few full marks with most students failing to make reference to the ability of devices to adapt.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was reasonable well-answered, with references both to positive and negative aspects.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "20N.2.HL.TZ0.12",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-5-analysing-the-web",
      "c-6-the-intelligent-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline why single processor computers may not be able to render 3D graphics effectively.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Single processor is mainly occupied with the OS jobs / might not be able to handle multiple jobs/;<br/>Rendering 3D graphics requires a great deal of processing (which a single processor may not be able to give);</p>\n<p>If attempting rendering on a single processor, a (very) high clock speed is required;<br/>which may not be available;</p>\n<p>3D graphics processing is (inherently) parallel;<br/>A single processor is not able to handle parallel processing;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Not an easy question for many candidates.</p>\n</div>",
    "question_id": "19M.1.HL.TZ0.4",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The global variable <code>tables</code> is declared as follows:</p>\n<p style=\"text-align: center;\"><code>Payment[] tables = <strong>new</strong> Payment[50]</code>;</p>\n<p style=\"text-align: left;\">The indices in this array represent the table number, so <code>tables[1]</code> is a <code>Payment</code> object for the customers occupying table number 1.</p>\n<p style=\"text-align: left;\">The driver (main) class contains the following code. <strong>Note</strong>: You can assume that all appropriate accessor and mutator methods have been included in their respective classes.</p>\n<pre style=\"text-align: left;\">tables[1] = <strong>new</strong> Payment();<br/>tables[2] = <strong>new</strong> Payment();<br/>FoodItem a  = <strong>new</strong> FoodItem(\"f102\", 2);<br/>FoodItem b  = <strong>new</strong> FoodItem(\"f100\", 1);<br/>DrinkItem c = <strong>new</strong> DrinkItem(\"d102\", 3);<br/>tables[1].addFoodItem(a);<br/>tables[1].addFoodItem(b);<br/>tables[2].addDrinkItem(c);<br/>tables[2].addDrinkItem(new DrinkItem(\"d103\",1));<br/>System.out.println(tables[1].getFiCount());<br/>System.out.println(Payment.getFoodTax());<br/>System.out.println(tables[2].getDi(1).getItemCode());</pre>\n</div><div class=\"specification\">\n<p>The price of each item is stored in an object of the <code>Item</code> class.<br/>The class is outlined below:</p>\n<pre><strong>public class</strong> Item<br/>{<br/><strong>  private</strong> String code;    // item code<br/><strong>  private</strong> String name;    // item name<br/><strong>  private double</strong> price;   // unit price before tax<br/>  // all accessor, mutator and constructor methods are included<br/>}</pre>\n<p>All of the objects in this class are held in the global array <code>pl</code> according to the following declaration: </p>\n<p><code>Item[] pl = new Item[200]</code>; </p>\n<p><strong>Note</strong>: The number of objects held in this array will change from week to week.</p>\n<p>The method <code>findPrice(Item[] pl, String c)</code> in the <code>Payment</code> class looks up and returns the price of the item with code <code>c</code>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the output after this code is executed.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct statements, in code, that will print out the following: The number of drink items ordered by table 40.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct statements, in code, that will print out the following: The item code of the third food item ordered by table 2.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>findPrice()</code>. You may assume that the item exists in the array.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>When a customer wishes to pay the bill, the <math style=\"font-family:'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>calculateBill</mi><mo> </mo><mo>(</mo><mo> </mo><mo>)</mo></math> method is called. If the bill was for table 10 then the following call would be made:</p>\n<p style=\"text-align:center;\"><code><strong>double</strong> finalBill = tables[10].calculateBill(Item[] pl)</code>;</p>\n<p>Construct the <code>calculateBill()</code> method. You should make use of any previously defined methods.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each correct value</em>.<br/>2;<br/>0.2;<br/>d103;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><code>System.out.println(tables[40].getDiCount())</code>;</p>\n<p><em>Allow variations of the <code>get</code> method name</em></p>\n<p><em><strong>Note</strong>: Ignore minor syntax errors.</em></p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><code>System.out.println(tables[2].getFi(2).getItemCode());</code></p>\n<p><em>Allow variations of the <code>get</code> method name</em></p>\n<p><em><strong>Note</strong>: Ignore minor syntax errors.</em></p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[6 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for correct initialization.</em><br/><em>Award <strong>[2]</strong> for loop that checks entries, but has early exit.</em><br/><em>Award <strong>[1]</strong> for a loop that checks all 200 entries with no early exit.</em><br/><em>Award <strong>[1]</strong> for correct assignment.</em><br/><em>Award <strong>[1]</strong> for correct comparison.</em><br/><em>Award <strong>[1]</strong> for correct return value.</em><br/><em>Award <strong>[1]</strong> for both the assignment and comparison <strong>IF</strong> <code>get</code> methods are not used.</em></p>\n<pre>public static double findPrice(Item[] pl, String c)<br/>{<br/>  int x = 0;<br/>  double price = 0.0;<br/>  boolean found = false;<br/>  while(!found)<br/>  {<br/>    if ((pl[x].getCode()) == c)<br/>    {<br/>      price = pl[x].getPrice();<br/>      found = true;<br/>    }<br/>    x++;<br/>  }<br/>  return price;<br/>}</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[7 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for correct method header.</em><br/><em>Award <strong>[1]</strong> for correct initialization.</em><br/><em>Award <strong>[1]</strong> for correct loop.</em><br/><em>Award <strong>[1]</strong> for finding item code.</em><br/><em>Award <strong>[1]</strong> for use of <code>findPrice</code> method.</em><br/><em>Award <strong>[2]</strong> for the calculation if completely correct, award only <strong>[1]</strong> if taxes are wrong and/or no use of accessor.</em><br/><em>Award <strong>[1]</strong> for consideration of both food and drink objects (whether correct or not);</em></p>\n<pre>public double calculateBill(Item[] pl)<br/>{<br/>  double total = 0.0;<br/><br/>  for(int x = 0; x &lt; fiCount; x++)<br/>  {<br/>    String c = fi[x].getItemCode();<br/>    double price = findPrice(pl,c);<br/>    total = total + fi[x].getQuantity()*price*(1 + foodTax);<br/>  }<br/><br/>  for(int y = 0; y &lt; diCount; y++)<br/>  {<br/>    String c = di[y].getItemCode();<br/>    double price = findPrice(pl,c);<br/>    total = total + di[y].getQuantity()*price*(1 + drinkTax);<br/>  }<br/>  return total;<br/>}</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.11",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a truth table for the following logical expression.</p>\n<p style=\"text-align:center;\">(A XOR B) AND NOT C</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for a truth table with all 8 inputs and up to 3 correct outputs.</em><br/><em>Award <strong>[2]</strong> for 4 or 5 correct rows in the truth table.</em><br/><em>Award <strong>[3]</strong> for 6 or 7 correct rows in the truth table.</em><br/><em>Award <strong>[4]</strong> for all 8 correct rows in the truth table.</em></p>\n<p style=\"text-align:center;\"><em><img src=\"data:image/png;base64,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\"/></em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates demonstrated that they could create a truth table for a given logical expression, and as a result, achieved full, or near full, marks. A small number of candidates lost marks for errors such as an incorrect number of rows in the truth table or applying AND C to the final result, rather than AND NOT C, as required in the question. Inputs were mostly laid out in an organised manner, making it easy to check that all combinations of 0 or 1 had been included. A small number of tables were less organised, and this may have contributed to some errors.</p>\n</div>",
    "question_id": "20N.1.SL.TZ0.6",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of using virtual memory.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> disadvantage of using virtual memory.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award [1] for an advantage and [1] mark for an outline</em>.</p>\n<p>Allows more applications to run than there is available physical memory;<br/>By the use of page/swap files/part of hard disk as primary memory;</p>\n<p>Larger application can run with less real RAM;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award [1] for a disadvantage and [1] mark for an outline</em>.</p>\n<p>Applications run more slowly;<br/>Uses hard drive memory as primary memory / takes more time to switch between applications;</p>\n<p>When a computer's virtual memory resources are overused /Reduced amount of hard drive space available for your use;<br/>programs lock-up/do not run/disk thrashing;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Poorly answered by majority of the candidates: virtual memory was confused with cloud storage.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Poorly answered by majority of the candidates: virtual memory was confused with cloud storage.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "19M.1.HL.TZ0.5",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A school has a local area network (LAN) connecting its computers and peripheral devices. The LAN also provides access to the internet.</p>\n</div><div class=\"specification\">\n<p>Users have been troubled by slow speeds when accessing the internet.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the role of a router in this network.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> reasons why there might be a reduction in data transmission speed at certain times.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> measures that the school could take to safeguard its data from unlawful access via the internet.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The inventory of office supplies used in the school is stored on the computer as a single file.</p>\n<p>Each of the office supplies in the inventory (such as paper, ink, toner, printers, pens, staplers, pencils and scissors) has a unique ID number, name, maximum quantity, minimum quantity and remaining quantity.</p>\n<p>Outline the steps in an algorithm that would output a list of supplies with the quantity to be ordered.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>controls the flow of data in the network;<br/>inspects address of data packets;<br/>directs to the appropriate network path/ selects a path between networks (by inspecting address of data packets);<br/>securely transmits data packets (across that path toward the intended destination);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>malicious software/spyware/malware / viruses;<br/>attacking the system;</p>\n<p>the type of connection;<br/>valid outline for the slow down (depending on the type of connection);</p>\n<p><em><strong>Note</strong>: There are many types of internet connection: wireless connections, wireless hotspots, mobile internet, broadband connections, fixed wireless and satellite connection, cable, DSL</em>;</p>\n<p>hardware malfunction;<br/>internet speed can considerably slow down if modem-router configuration is not correct / updated;</p>\n<p>external factors;<br/>during the peak hours of daytime the most visited websites encounter more network traffic than what has been anticipated;</p>\n<p><em><strong>NOTE</strong>: Accept examples. For example, too many students try to connect to an e-learning site at the same time</em>.</p>\n<p><em>Mark as 2 and 2</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Preventing unauthorized access/No access without user names/passwords;<br/>which should be regularly changed/made difficult to guess;</p>\n<p>Data should be encrypted;<br/>Allowing only those with decrypting code access;</p>\n<p>Teachers/students should be trained for safe practices;<br/>to create a risk-conscious /security-aware culture within the school;</p>\n<p>Installation of virus checkers/spyware software;<br/>to prevent damage to data files or the system / to prevent data being extracted from the files/system;</p>\n<p><em>Mark as 2 and 2</em></p>\n<p><em><strong>Note</strong>: Accept appropriate measures such as physical security measures / access control / data security / network and communication security / user awareness and education, etc</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for looping through the inventory file;</em><br/><em>Award <strong>[1]</strong> for reading the supply data record (ID number, name, maximum quantity, minimum quantity and remaining quantity);</em><br/><em>Award <strong>[1]</strong> for checking if the quantity remaining is less than minimum quantity;</em><br/><em>Award <strong>[1]</strong> If so, calculate the quantity to be ordered (number to buy) / as the maximum quantity minus the quantity remaining;</em><br/><em>Award <strong>[1]</strong> for outputting the name/ID number of the office supply and the number to buy;</em></p>\n<p>Example 1:</p>\n<pre>while not end of inventory file<br/>    read supply data record from file<br/>    if remaining quantity &lt; minimum quantity<br/>      then<br/>         numbertobuy = maximum quantity – remaining quantity<br/>         output (\"Reorder \", ID, name, numbertobuy)<br/>    end if<br/>end loop</pre>\n<p>Example 2:</p>\n<pre>for each item in the inventory loop<br/>  with item do<br/>    if remaining quantity &lt; minimum quantity then<br/>      output \"Reorder \", ID, name, (maximum quantity – remaining quantity)<br/>    end if<br/>  end do<br/>end loop</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally recognised that a router directs data between networks and gained a mark for this. However, most answers didn't really go beyond that point and concentrated more on access to the internet. Some candidates did recognise the role of addresses in data packets to ensure they arrived at the correct destination.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates who achieved marks in this question, which asked for two reasons why there may be a reduction in data transmission speed at certain times, gave answers that may fit into category 'external factors'. An example of this was high demands on bandwidth due to many students accessing the network at the same time. Sometimes, candidates gave more than one answer related to this same category. Other categories, such as malicious software, type of connection and hardware malfunction, were largely overlooked by candidates.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates recognised at least one measure that a school could take to safeguard its data from unlawful access via the internet, with correct answers based on authentication of users or encryption. A small number of candidates recognised the benefits of anti-virus software. However, candidates could also have discussed the benefits or training staff or users in safe practices.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were required to write the main steps in an algorithm to output a list of office supplies with the quantity to be ordered. Candidates who gave a list of logical steps written in the style of an algorithm, rather than as a purely descriptive answer, achieved higher marks. Marks were generally lost when candidates failed to recognise the need for a loop to check the whole file, or if they were not specific enough about either what was being ordered, or the quantity of the order. Sometimes, candidates would output the office supply item and its order quantity but failed to show how the order quantity was determined.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.1.SL.TZ0.7",
    "topics": [
      "topic-1-system-fundamentals",
      "topic-4-computational-thinking-problem-solving-and-programming",
      "topic-3-networks"
    ],
    "subtopics": [
      "1-2-system-design-basics",
      "4-2-connecting-computational-thinking-and-program-design",
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A single parking space that holds one car can be used to hold two motorbikes. The programmer has decided to use a two-dimensional array to create a map of the parking area.</p>\n<p><code>Vehicle parkingSpaces[][] = new Vehicle[2][200]</code>;</p>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxMAAABtCAYAAADamjx8AAAaKElEQVR4nO3de1xUdd4H8E+z83J9fJDHnqxlsXRGBzFRVyYsUVPKvIUrBEVtDxQVWqYidNNWFEvdzXIdQMpW3YDVtc00htTsIoJu3mHwlZpcxiAUnFxNgllLgjnPHzMDyL3jgXOOft6vl69XDTNnPuc253zP+f1+5yZBEAQQERERERH9Qhq5AxARERERkTqxmCAiIiIiIlFYTBARERERkSgsJoiIiIiISBQWE0REREREJAqLCSIiIiIiEoXFBBERERERicJigoiIiIiIRGExQUREREREorCYICIiIqImamGzfIA15hI45I5CinedFRN2WEwPwqDTw6B7EEkWe4t3OGwHsSYm0PmeaSuQa6tt8Z46iwkBOj0MOj0CTBbUdXtGC3akJyDY3wRLG18ub8ZqlGSvwayhzu8fEbMGe0qqlZXRYYNl+wdIcq/roTOx5qitxY9i12bs7PQdsFvWIFhnxCzzWZHT6KqMZ5EVY3QtZ/e/ljmVsRyrUZKbjsXT/FzrPB5ZlbW/cBpdkbEONnNss2Xo+hdjhu3qiSDJ3/W3dvZ/6TM6OWxHsSkhxJUvBIvNRWjxCyVzRtiLkNWdGTucVi1slk+QkRDS7nbVpcceyTJ24bFHkowO2Eu+uOp3PSm7pMX6lzdjLWyWT5FpmokROj0MukDMSjkIW8uDT5fuR9es+gDejnwH5/vcLOuJYofr0mGDZft7WDwtpNXzKdebYC8xNx4bpiUiq5VzFjhssGxMQLBOD4POD8EJZpTYm6+4js/hZJ0HVKPEnCj5PHRIuK7UCPmrYwRTfk0bfz4ipCz5WKiob/x/03MbheL61t/+c/5qYcLqfOHnbst4RajIXCTMXr1FyPl6t2C6d7WQ38GXd3/GeuGHnCXCXc+sFY6euyIIwhXh3JG1wsyRcYK54opCMv4oFKdFCw8u+oeQf+6KIAj1Qk1xppAwNVpIL/6xGzN2cro1RwTT1BnCzGfChJmZZ65tWpJnPCOYn3lJMJ/r3LfKtxx/EArT5gmhi9KEnOIfFJbxZ+Fc5kst1m19cZrw8KIcodW0P+d3av+XLqMgCPXfCua5Lwnphe5EPwiFaS8JsZnfCq3+RMqRUbgk5K+OFmYmHxDO1QuC8/cnTUj5uAsztjeN+m8F89w4wbRtr/B1zltt5+7qY881Z+yGY48Uy/GHHCFhZIyQcuScUC8IQv25A0LKM9OE2W1so3JkrC9OE0KnLhI25jszCjWFgnlRmBCaVtit+9G1cR7nhw/4vzaPmd2lvXVYX5EpxD63WvgoJ1/I6eDczzTjuYbtRqg/JxxNXStsv+qcxbUPpJ0QnFOpF2oKNwqz52Y27rdXT7T9801Z5qFeqMlPEUIbzs+c+8jbybuueR46cp3dmWiPA9V5e3HxvkB4u+fawx+PTSnE3/ddkDVZox7wDl2Od+IfQZDPzXKHaUMlcjZVYt6CaAR49QDQA16jovHyfDt2HjkvdziXnvCJTsPO5Y/D6NUDgAYePtMx59m+2H9SKevarQqW9avxxeSFeHn6QLnDqJQDdstG/KU0BGtfj0aQj6fcgZrRwiv0LawLvb3Jaz/h9H4LhjwwDIpJe96CnT+OxRRfdyJP+E4dix93WKCUPRt132Dflv54PPoeeGkA5+/PA+if9yVOy9EWQ9MfIWtMiAsbDx/PX7XxJpmPPZ3KKPOxp1MZ62Dbk4UT81/CnFFe0ADQeAVizoIInOuObbRTGQGNTzQydy1HpNGZER6+CJkbhVu/PKWQ/cgBe0kuMhru7gVilumLJlevLyA34T4Yo/+Oy9iPZZOmYHGuazu1lyA33X3lvu1WCd1F4x2K5LXxeChocLu/o3XFX+LD4Y/iSdd2A40XAsL7Ie/z0iatFc7j6I4rmDLVFx7OqcPDNwhTftyPo+e77raRtPNwGcV7D2NY5MOu8zNA4zUKYf1P4YvTP3XZPADXXTOn9lyGtaAIfTx7NnlNi779b8G/Csolb/Jw/bodIRvW40mfnle/3GcCHh99mzyROsFh24t304Dgu5WU0XkSvPjzMVg28y70ljuOan2PvI+KMDZyrOsEUwUc3+LA9lsx0fi/cidp5NEPQ/+9H4cbmoY5UF14HJfG3Qkl7TWtqijA1114wL82PPZIQwuvUBMyo32bnbjcgkmRdyt3G3VUInftVminGxWQ0QG75W08OskEa8CbOFZWCuuJDRh7cgnCX/4YlQ4A6Iug19dh8e9uxvCln6K4bC+WBfUF7EeR9EgMNlcH42/flML6zUG8N+IrxMVmwNKiGY0a1KPyqgLPA3f4XcBnhyobT86rrci75Ieht2llSdix5vPQlsouv5CqlkMvKVYtbLkb8Wmf+zHeVQkribut4uCncmBIfBG/91ZQRns+NizaigHPhmCkh5J3xWy8ONrHdRXLD8EJm2Fppb23bOrKcWyfHn7/VdDQJl3uK2YdcZw+CHPdHeinpPXucRdiVo1FwTuv49VpRkyZNh1P7h6OPz3so5wDhXYgxkeUY3P6Ydgczjb+WaZkpFdUoaqGp+U3GoctF2/v6o2w8d7K2Ubd3H0iBs5GtmE+lszoL3/G6n1YGfkuMD8BC0JdV+A9/BC14FkM2rW18eq1/RysJb0xTHeLK3MVLOvfwHu6eCydG+i8aKPxgnHaWAw6tRWZed/LNUedoh08Do8c/wAZR21woBY2ixlJSR+g4lIVahoqhz4wzlyIKQXrkLgwBIZJDyI4KgcBb4TBR/YV19l56IXBE+7BiU1bkWerdfbFMK9FctpJXKr6T5d2pFfAIiL1qoUtNwMfIhhzghT4Yw5Aa4xHXlkRvlwxFEXvZGCfYk6Ca1G5ezM26eKRoISDTJtuR8gGC6xlpa5/x/FBlAMbVnziuoqlAJercbHmCNYt3QI8m4nislJ8lTQZNTt2KPSK2U84vX8v8FAgBilqxdtRcagChqhX8OddFny26594w7cChysk6JwnGecBP/j8Gxg3UI/B969F+bgozLvrR1xiMXFDcdhy8fYW4JG5Qcq8I6k1Iq6gFMWHEuBbtBHv7quUeVQkB6otOci67I+I6cNdTXmaOgOra193fFeGE5fvgKGf613Vx5C5/gJCHh3X2FQPriZd7jsXSuZxF2JWTcH5JRMxWOeLye/YMO6pJ2GsvNTkRNwBe8VXKDX8AQveyIL1ix344M1hKD10puUAD3Lo1Dxo4GF8Em9OP4fE0b4wDHwI75YbER07CpWXWEx0IQcuV1fhZ7ljqJLyC4lGPeBlfAwLnu+F5NQDUMb16vM4umMvqnbFY/xAPQw6H4yL24o9cTNaHdFJObqnHekvdqUM2unxDW2p4TEI9+hPY1/xZbmTteICvv5Si9CxA5S139j2ILnIDzOa9pmY1A8HNloUss+4ePgiZHmWs7j9ej3m3fVrnDl+K/TePTv+rGLw2HMtFF9INKHxGoXIhVHoY/ob9lXLWU58D8vuXFc/iDuvGlVu8KTXcLzhfT/h9P7dON5rEAb8xnknv0VxoToaePiEYtmuk7CWleKrDc/BqLHhhJ8e3g0tmCqRnVSMkSF3NukzEQj9ATPyZF1vbp2ZBwDwhE/oa9hZVgpr2UGsi70LmjOV8NPfhq5srKXUhmBdoBcM/jpsKK8CjO4dohbflV3C/f79b6QFIYFqFG3MQPaQRxpP3hRPg169eytoPTuv+Ic0/H8dbOZXsQTzm3XUVSDHf1B1yRMDeilkzXsOw8Qwb2T36dVsW/TAzb2Vs8YbVFuRd2ksnhikrJNfR00Vqm7pj15NX+zbH4N/KMdlQDkdxa/igP3Y5zAPD0GGlwLXNQAee6TkgL1oGzbsvh2PuZvbqEEvT9ws94p2XMS3xy+gV2Qavlwe1M7+fAEV1jOAzwOuZpgO2CtKcbr7knaDKhzbsR/DIv8ML/dLruPaHVcd1/rgjsEOnLnsADyVtrG1Mg+tsR/Hzsz+eHyjd5emUdrS6UIaeAaE4p68DxvG93ZUforUw3cjMkBBnSAVrxa23K045BeF5xsKiTrYzC8r6Ir6WWTNjkWS2eIa29s5PnNi7GcYGzZSoSdFSvQTStKfQnBCOnLd/Q/sJdiTvApr7rwXRsX8uPbF+OejcNH0V9e+XQubZQve/uxWjFHYCbuzqcG/UPh7pTVxAjSDAjGleCd2NvQ1qUbJji9QNVEJHUdbUwvb0XV44bVqzJ87RsH7NY89UnHY9iHt4CDEzGlSSNjMmNX8eS2yqYPNvBCzTObGfmX2ImQt+SO2jJuKADl/MzUDMOYhf1w+Xobvml5od5Qja/YEPJRe5GwG47iIb4/XYHhDM0wNPPrpMaj59BxFyAgxNn5OLRw25KW8imVVj2HO+CbNszQDMGZqBTJ3uJ9b44C9ZDc+rhqJUUrrgN3WPLR420GsiUtGVfwzGN/F257CDmddzMMPUXNHIPcpfxh0eoxcYkNUYgR8FdMJssmDRAzhSC1PQYSh6x6yJYrtEyyJXoZlYf5NbpP6YFxcttzJmrgdIW/Nw8iqj/DMQD0MukEYGZsH3xUmxBr7yB2uJZsZsxTZzKknfKKTkfQAkB3rekjUsCfwT0Tg/YXjFXXypvF+EEsS9ch+yh8GnS/GLSpCwAszFNFx7mrfw7L735iutCZOAKDxRVTiAyhfOcW1X0fh7zX34zkl9elxVCI30TWk5dA5+OeZoXj5H68iSK7BH5o8eGxIWArOJodjSGsPgpLz2NOpjDIfezqV8Sy2J7yA5KXhGNn0wY+j47Gnq/N1OqMWXqF/xMv+Vch0rWvDsFeQ57sQf5s/qpV+Ct2pJwZNfhiTSzLxD3f/DXsRspbMw+KfZ+IN90AL9nOwllxxfuSyHXaH80JD6O++xpZN+50X6OxFyFryCv5y6wuNn5NB48PghiMieTdSw4a3+hBAhy0bS10Pexsx632cGfECNr42sdmdrZ7weSIOIeWrMMY1zUczLmPG7ClX9RNR9jzUwpa7wjV0byCee/8shi94B0u7oSm6wsqtrqfxmoilu05iqdxBWuUBY/wnsMbLnaMdXqFYVxYqd4qOefggKHo5gqKXy52kY4pepp7wCYrGsqBoLJM7Srt6wMv4OJbtelzhOfsiaHmK3CHapPEKxLwNBzFP7iBt0Xgj6LUsWF+TO4iLq5NtXCfeKtuxp1MZZT72dCpj86ah3azT61q5v5ka7xl4M+u/sWFlOAZH2wCMwB+WxmPbwvHwcRe2nkZEvjIejyydisGZidiVGQ0fjS+i/vpX/Cp1OcYNjAHghfvnv45tCyc2fk4GzgFWOt5oO73vabwQELseX8VKEq9TpJ2HHvAKWoSdZYukitdpirngJJ0KV2Un/hHh7kpxSFhKF/XiZ0ZpqCEjYHddwRJ7hY8ZnW6EjA1XPw3hSK2ROp0TM4pQ47pS3+xqodSuaRtlxgZqyNgd+1FLGnj4TELchoOu0fmysCw6qFlB4Anf6LX4qqwU1qzohru7Gq9RiHQPfFB2EOviJ3VbIXHNvwfSpnHdxRuOiOSKzn/qOpiH9twkCIIgyZSIiIiIiOiGch3emSAiIiIiou7AYoKIiIiIiERhMUFERERERKKwmCAiIiIiIlFYTBARERERkSgsJoiIiIiISBQWE0REREREJAqLCSIiIiIiEkUrd4DOMOj0ckcgIiIi6nbWslIALc+F3K+39ze1vk7ya7p9dUQ1T8A26PS/aMbkwIzSYEZpMKM0mFEazCgNZpQGMyrve+X+bimoPT8gbh7YzImIiIiIiERhMUFERESkMHJd4Zbzyrrar+rfqFhMEBERERGRKCwmiIiIiBRGrk7JcnaGZkdsdWIxQUREREREorCYICIiIiIiUVhMEBERERGRKCwmiIiIiBSGozmRWrCYICIiIiIiUVhMEBERESkMR3MitWAxQUREREREorRZTNRZTAjQ6WHQ6RFgsqCu+RscNuSlzMQInR4GXQiW5lbC0XIiSPJ3TsPgb4KlxUSIiIiIiEit2r0z4TF/GwrLSpEXb4T2qr9UwZK6CRUPv42vykphLcvAjIJ3sLHkp6snoDUirqAUVus2zO0teXYiIiKi6xI7YJNaiGvmVH0MH18MwH3ePVwv9IHxDwE4nHEI1dJlIyIiIiIiBRNVTNRZC5DbxxO9mr7Ytz8G7yuAVdFNmapRkr0Gs4a6ml7pQrA4fT0Wh6+RvQmWu1nZiJgVWBUTCMPQ2cg4dQq5iSEw6PwQnJgNW4t2ZHLhcpSGcpdjIwfsJV8gKSbQldEPwQnrkZEwG0kWu7zR3M0oh87EqrdmYoQuELPSLSjJXYFgnR6GaSuQa6uVN2MDBS/HBgrOqKp1rQYKXtcN1JCxa7EDtlTUcKxVtxuoA3YtKs3L8ErOHXj5yGlYy0phLfsQc/zq8V2l/FuT1hiPQx/FosfuI7BHbkNh1j0wT3sam/V/wrET6Zh0ZAcOn5c/J5ejVJS9HN0clR/jlZe+hH7BZ66MJ7F97jBU26rkjuZsRnl0G+b++gBy7I/hc2s6xmZGIXzTAKw+kY8tk09h86HzcqcEoPDl6KLojCpa12qg6HXtooaMpAbqONaqnbbjt1wn6k5gy8pemP/ZDPh4uGuoHvAa9RzWHZQ12VU85iciIcgb2roRmNA/AuMj/eChPQt9P4WsKi5HaahiOdpx7IOtuCU+CSE+ng2varwCMW9DoIy5mukdg2WLJ8JLa8fwoDF4ekIofD16wqb/rdzJXNSwHNWQESpY12qghnWthoykCqo41qqfdHcmLlfj4s+STU16F8rxtZ8/hnjeQDdjugKXozRUsRyrcObkbxEwpI/cQVRODctRDRlJGmpY12rISKqgimOt+olaulqDP+4tLseFJq85vitD4UR/GBRy4beFXn3wm/wCFFYrpsG8OnE5SkMVy7En/sfrG+QVslnBtVHDclRDRpKGGta1GjJ2PY7mJAFVHGvVT1yp5mlE5D3Hsd79bAlHObannMT0KCM8O/qsXDzHYHbiZSS/+TFK7O6NqholuR8iKWY2MpoPa0ut43KUhiqWY1+Mfz4KF01rkFXiHqfNAXvJPmSaZuOR9KKWz5ahVqhhOaohI0lDDetaDRlJFVRxrFU/kfd9POH7xDO4N2c2Buv0MAxbhvInXkSUr2JLCQA94B26GG/edwZv3T3I1aN/Ct4qAEYuWIknfXrKmq7OYsLosBScTQ7HkBgzbABQk4KIUe6H/WXjxdEzFDCKBZejNJS9HN003jPw5qpxKF05xZVxEMaszAf8X0BatK+8IzjUWZA0Khyp5SmIMMQiy1YHoAKpYRGu9XsFe+Lubf2hm91M0cvRRdEZVbSu1UDR69pFDRm7GkdzkoI6jrVqd5MgCEJrf6izmPDA3gnY3eKBdSLUWZB0/16M3xMPo8iJGXR6xT/MhBmlwYzSYEZpMKM0mFEazCgNZlTe98r93VJQe35A3Dy0W9zbk8MxRKcXf7XHPTa4IRypNWImQEREREREStXmfQKtMR55ZfHXOHUj4gpKEXdtUyEiIiIiIgW6EZodEhEREakKR3MitWAxQUREREREorCYICIiIlIYjuZEasFigoiIiIiIRGExQUREREREorCYICIiIiIiUVhMEBERESkMR3MitWAxQUREREREorCYICIiIlIYjuZEanGTIAiC3CE6wo2LiIiIbkTupj/Nz4WaNglq629qfZ3k90uanKmimACcG5rS29IxozSYURrMKA1mlAYzSoMZpcGMyvteub9bCmrPD4ibBzZzIiIiIiIiUVhMEBERESkMR3MitWAxQURERKQw7IBNasFigoiIiIiIRGExQUREREREorCYICIiIiIiUVhMEBERESkMO2CTWrCYICIiIiIiUVhMEBERESkMR3MitWAxQUREREREorCYICIiIiIiUdosJuosJgTo9DDo9AgwWVDX/A0OGyzb38PiaSFIstjbmgiS/J3TMPibYGkxESIiIiIiUqt270x4zN+GwrJS5MUboW3yuqPSjPlz3se3vUdi4uTb2p6A1oi4glJYrdswt7dEiYmIiIiucxzNidRC2/FbWtJ4hyJ5LQDYYSmQNhAREREREakD+0wohLtZ2YiYFVgVEwjD0NnIOHUKuYkhMOj8EJyYDZtD7pRERDJwN5kdOhOr3pqJEbpAzEq3oCR3BYJ1ehimrUCurZYZ6brC0ZxILeeGLCYUQmuMx6GPYtFj9xHYI7ehMOsemKc9jc36P+HYiXRMOrIDh8+z0wkR3YC0RsQd3Ya5vz6AHPtj+NyajrGZUQjfNACrT+Rjy+RT2HzoPDMS0XVFLeeGopo5UdfxmJ+IhCBvaOtGYEL/CIyP9IOH9iz0/biqiOgG1zsGyxZPhJfWjuFBY/D0hFD4evSETf9buZM1UkNGIlIVpZ8b8s4EERERERGJwmKCiIiISGE4mhOpBYsJIiIiIiISRVQx0fhAu+GISN6N1LDhfDDdNaqzmDA6LAVnk8MxJMYMGwDUpCBilHuZZuPF0TPafkAgEdH1qs6CpFHhSC1PQYQhFlm2OgAVSA2LcP0mXsGeuHtbf8AqM5JKcTQnUsu5oaieG1pjPPLK4qXOckNrbZnGFZQizvXfxg0WhHR/LCIi+bkegBrX9LX4T2B1/2QaU2ANTZEhWBNqyEhEqqKWc8N270zYk8MxRKcXfyXFPe62IRypNeICEhERERGRMrV5Z0KSuw+tXakhIiIiIqLrAjtgExERESkMR3MitWAxQUREREREorCYICIiIlIYjuZEasFigoiIiIiIRGExQUREREREorCYICIiIlIYdsAmtWAxQUREREREorCYICIiIlIYdsAmtWAxQUREREREotwkCIIgd4iOsFIlIiKiG5G7H0Hzc6Gm/Qva+ptaXyf5/ZL+K6ooJoiIiIiISHnYzImIiIiIiERhMUFERERERKKwmCAiIiIiIlFYTBARERERkSgsJoiIiIiISJT/B1BC606HhUFrAAAAAElFTkSuQmCC\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n<p><strong>Note</strong>: <code>m</code> and<code> c</code> have been used to show where motorbikes and cars are parked.</p>\n</div><div class=\"specification\">\n<p>Police have requested a way of searching the parking area for vehicles with a specified registration plate, and a binary tree has been suggested.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> advantage of using a two-dimensional array for this purpose.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> disadvantage of using a two-dimensional array for this purpose.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why searching for the registration plate in a binary tree would be faster than looking for it in the two-dimensional array.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the steps that would be involved in taking the data from this two-dimensional array and creating a binary search tree.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Faster to access (i.e. O1 instead of On);<br/>Arrays take less memory than lists (as they do not store pointers);<br/>Mirrors real life situation;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Static, therefore memory wasted when not being fully utilised;<br/>Inability to grow, should further spaces be added, i.e. If Parking area grows;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The array would need to be searched using a linear search which is inefficient O(n) whereas a binary search is much more efficient O(log n);<br/>A linear search for a car in this parking area could require a maximum of 200 comparisons (i.e. it would look at the first row of every column) whereas a binary search would only require 8 (i.e. log 200 base 2);<br/>A linear search for a motorbike in this parking area would require a maximum of 400 comparisons (i.e. it would need to look at both rows of every column) whereas a binary search would only require 7 (log 100 base 2);<br/>In a binary search, each comparison would eliminate half of the remaining possible matches, whereas in a linear search it only eliminates 1;<br/>A binary search has the registration plate as its key and therefore it doesn’t require accessing the field of the vehicle object for each comparison, whereas in a linear search, each comparison would be slower because it would require getting the vehicle object’s registration field using an accessor method;<br/>A binary search is always faster than a linear search on a large data set. As the police may be searching for many cars and in many parking areas, their searches will be on large set of data;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/>Create a binary tree to store vehicle objects;<br/>Loop around all of the elements in the 2 dimensional array (e.g. using inner and outer loop);<br/>Skip empty locations;<br/>Take the first vehicle found and add as the head of the binary tree;<br/>Take all subsequent vehicles found and do the following;<br/>Get the node’s vehicle registration;<br/>Find the appropriate insertion point in the binary tree by navigating from the root and comparing the registration to the key of the current node;<br/>When a leaf node is reached compare the registration with the vehicle to be inserted and insert it as the left or right child node, using its registration as the key;<br/>Continue until the end of the vehicle array has been reached;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Both parts were well-answered.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Both parts were well-answered.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students identified the tree property that effectively eliminates half of the tree when searching but answers were not always detailed enough to gain full marks.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of students were unclear as to how a binary search tree is created. Full marks were only reached by those students who clearly identified the different stages of the process.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "20N.2.HL.TZ0.16",
    "topics": [
      "topic-5-abstract-data-structures",
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "5-1-abstract-data-structures",
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Website developers need to consider a range of usability factors when designing a website.</p>\n</div><div class=\"specification\">\n<p>A company promotes its products online. To make a purchase, customers are required to register with the company and provide data like their name, date of birth, age, gender and email address. Once registered, more than one customer is able to access the server to retrieve and modify their data at the same time.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> usability factors that need to be considered in the design of a website.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason why visual displays on a computer screen can create difficulties for some people.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State where the customer data is held during the process of modifying their data.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the operating system ensures that each customer’s data is secure when multiple users are accessing the data at the same time.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The company is considering sharing its customers’ data with marketing organizations.</p>\n<p>Explain why there could be ethical issues for the company when sharing its customers’ data.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>complexity / simplicity / amount of effort to get a result / number of errors with the time taken to move past them;<br/>readability / comprehensibility / reading or writing speed;<br/>learnability / time to accomplish tasks on the first use;<br/>effectiveness ( user performance);<br/>efficiency (time needed to complete a task);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award marks for the reason/accessibility issue arising from visual / hearing impairments / cognitive disabilities, and not only for users with disabilities but also for those using mobile devices (alternative browsing devices such as TVs, watches, etc.), or those with slow network connections</em>.</p>\n<p>Visual display design is not logical;<br/>workflows are not simple, and do not require as few interactions as possible to complete;</p>\n<p>Visual display design is not consistent;<br/>navigation, header, footer, and main content are not always in the same places;</p>\n<p>Visual display design is not usable as possible;<br/>tools are not easy to use, processes are not broken down into logical steps;</p>\n<p>People with poor eye-sight/color blind can have difficulties to distinguish;<br/>because of the way graphics, words and directions are used / combinations of some colours (e.g., red and green);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>RAM/primary memory;<br/>It's held by the process/thread handling the customer access, which may be in RAM or in other storage if the process is swapped out while handling the access;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>OS (memory management function) allocates / deallocates memory to each process/customer task, and guarantee each customer task the resources it needs to run correctly;<br/>moves processes back and forth between main memory and secondary memory during execution to prevent overwriting / accidental interchange;<br/>OS (hardware memory protection- part of an OS) isolates/protects customer’s data/applications;<br/>and control access rights to the specified memory area (for example, prevents write access to the memory which is not allocated to the process/ customer task);<br/>and protects data / applications when in memory / RAM from malicious code (prevents attempts to execute the contents of the partition/ allocated memory);</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>privacy of customer/ person data;<br/>company needs to gain permission from customer;<br/>customer would need to be able to view all data details;<br/>customer must be informed about all uses that will be made of data;<br/>customer must be informed to whom data will be disclosed.<br/>legal issues to do with unauthorized disclosure of customer data;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Answers that simply identified usability factors related to website design, such as: complexity, readability, learnability, effectiveness and efficiency, were rare. The vast majority of candidates opted for a more descriptive approach, which generally did not reflect the required answers.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates in general offered responses to this question related to visually impaired users, and some gained marks here, particularly if their response related to poor choice of colours making it hard to read, for example. However, candidates could have also given answers related to screen layout not being logical, consistent screen layout design, or the fact that consideration needs to be given to the size of the screen being used, such as on mobile devices.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates recognised that customer data is held in RAM while it is being modified. However, other candidates incorrectly stated that it would be held in the computer or the server, which is not specific enough to achieve the mark.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The majority of candidates did not recognise that this question related to how an operating system functions and keeps data secure in multiuser environments. Candidates offered answers related to security, but often in relation to authentication when logging in. Some marks were achieved for statements related to the operating system's role in preventing one user's data overwriting that of another, or for the operating system controlling access rights to different parts of the memory. However, high marks on this question were rare.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates had the opportunity to discuss ethical issues related to a company considering sharing customers' data without permission. Many candidates described one or more of the points: privacy of its customer data, the need to obtain permission from customers in order to share their data, and the legal issues that may arise due to the unauthorised disclosure of customer data. However, candidates could also have discussed the need for customers to be aware of how their data may be used, the actual data that may be being shared and to whom it would be disclosed. Most candidates achieved some marks here, but mostly in the centre of the mark range.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.1.SL.TZ0.8",
    "topics": [
      "topic-1-system-fundamentals",
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "1-2-system-design-basics",
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>State the output from the binary tree using postorder traversal.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2]</strong> for completely correct answer and <strong>[1]</strong> for any 3 numbers in correct order</em>.</p>\n<p>Postorder traversal: 76 75 79 70 68 72 83</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates answered this question reasonably well.</p>\n</div>",
    "question_id": "19M.1.HL.TZ0.11",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline why a binary tree would be a good choice of data structure for maintaining an address book.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Time efficient searching / traversing for a contact in an address book;<br/>Each iteration / comparison allows the size of the search to be reduced (by skipping about half of the remaining contacts);</p>\n<p>Fast/easy addition / removal of contacts in an address book;<br/>Quick search for the leaf node / empty node where a new contact can be placed / for the node containing the contact to be deleted;</p>\n<p>Contacts can be listed / output in alphabetical order/ fast sorting;<br/>using inorder traversal;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A few candidates provided too vague, out-of-context answers.</p>\n</div>",
    "question_id": "19M.1.HL.TZ0.12",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The internet and World Wide Web are often considered to be the same, or the terms are used in the wrong context.</p>\n</div><div class=\"specification\">\n<p>Many organizations produce computer-based solutions that implement open standards.</p>\n</div><div class=\"specification\">\n<p>A search engine is software that allows a user to search for information. The most commonly used search algorithms are the PageRank and HITS algorithms.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between the internet and the World Wide Web.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> advantages of using open standards.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why a search engine using the HITS algorithm might produce different page ranking from one using the PageRank algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Web crawlers browse the World Wide Web.</p>\n<p>Explain how data stored in a meta-tag is used by a web crawler.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The internet is a global network of interconnected computers / a network of networks;</p>\n<p>The World Wide Web is software / a service that runs on the hardware of the internet and provides access to content / a collection of pages that can be accessed through hyperlinks / a way of accessing and sharing the information that is held on the internet in webpages;</p>\n<p>The World Wide Web uses the http protocol. This is only one of the many protocols used by the internet;</p>\n<p>E-mail, File Transfer Protocol (FTP), and instant messaging services are part of the internet but not of the web;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Open standards provide a publicly available specification for a specified task;<br/>This is an agreed set of parameters that enable interoperability and/or compatibility to occur;</p>\n<p>Using Open standards means that you are not subject to a governing body with its own agenda/self-interest;<br/>Thus, you can be confident that you won’t be subject to fees/bias;</p>\n<p>Open standards promote interoperability;<br/>This enables the various devices to communicate with each other;</p>\n<p>Open standards advocates also argue that openness encourages better and more secure systems;<br/>this is because more people are able to analyse the standards and resulting software and no-one has a proprietary interest in suppressing knowledge of problems to keep sales up.</p>\n<p><em>Mark as [2] + [2]</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The HITS Algorithm ranks the page based on a combination of its importance as a hub and an authority;<br/>The PageRank Algorithm ranks the page by counting the number and quality of links to a page to determine the relative importance of the website;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Meta tags are included in the header of a web-page which are available to a web-crawler and give information about the page that it could make use of;<br/>When the web-page is crawled, a copy of the HTML is replicated in the search engine database;<br/>When a user enters text into a search the search engine retrieves the data indexed from the web-page;<br/>And the search engine ranks and displays the content (in order of relevance);</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Standard question and well answered.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students referred to the idea of interoperability.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many answers talked generally about popularity and some did mention the idea of hubs and authorities and others referred to the quality of connections, but few put the two ideas together correctly.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many answers identified the content of the tags and some were able to relate this content to subsequent decisions taken by the web crawler.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.8",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-3-distributed-approaches-to-the-web",
      "c-2-searching-the-web"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a diagram showing the relationships between the <code>Payment</code>, <code>FoodItem</code>, <code>DrinkItem</code>, and <code>Item</code> classes. You do not need to include the names of attributes or methods.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>By making reference to any of the classes from 12(a), describe <strong>two</strong> benefits that a programming team would gain from the use of encapsulation.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The company that owns this restaurant also owns hotels, shops, and a car hire business. The programs that manage the finances of these different businesses include classes similar to the ones shown in this paper.</p>\n<p>Explain how inheritance could be used as a tool to both improve the clarity of the design and to reduce the amount of code that needs to be written.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows, up to <strong>[3 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for 4 classes connected with arrows/lines.</em><br/><em>Award <strong>[1]</strong> for “has a” label or correct arrow from <code>Payment</code> to <code>FoodItem</code><span style=\"font-family:monospace;\"> </span><strong>and</strong> “has a” label or correct arrow from <code>Payment</code> to <code>DrinkItem</code> (with no additional arrows).</em><br/><em>Award <strong>[1]</strong> for “uses” label or correct arrow going from Payment to Item (with no additional arrows).</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying a feature, <strong>[1]</strong> for an example in the given context, and <strong>[1] </strong></em><em>for an elaboration, for two features up to <strong>[6 max]</strong></em>.<br/>Encapsulation places all attributes and methods that relate to a particular object/entity together;<br/>For example, <code>Payment</code> class includes attributes such as the food and drink arrays and methods such as <code>calculateBill()</code>;<br/>This provides a clearer view/understanding of each section of the problem;<br/>Which can lead to more efficient programming (faster, less errors etc.);</p>\n<p>Encapsulation protects the values of the data stored within the object;<br/>From (accidental) changes made by other classes;<br/>For example, quantity in the <code>FoodItem</code> class cannot be altered through another variable called quantity in another class;<br/>This allows programmers to select any variable names they wish/no restriction on choice of variable names;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>A generic <code>Payment</code> class can be created;<br/>This would contain data/variables/methods required by all units of the company;<br/>Each of the different operations could then inherit this class;<br/>Adding new variables/methods that relate only to them;<br/>Overriding the superclass methods as necessary;</p>\n<p><em><strong>Note</strong>: Allow a similar answer that deals with the different items (<code>FoodItem</code>, <code>DrinkItem</code> etc.)</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "17N.2.SL.TZ0.12",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-1-objects-as-a-programming-concept",
      "d-2-features-of-oop"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A new parking service has been introduced: staff will park the car for the driver and then return it later when the driver wants to leave. This service will use a narrow parking area surrounded on three sides by walls, where any cars behind the required car must be reversed out. For example, car <code>X00000011</code> was the first vehicle to be parked, so to remove it from the parking area, the other five cars, starting with <code>X00000123</code>, will need to be removed. The cars are then returned starting with car <code>X00000010</code>.</p>\n<p style=\"text-align: center;\"><strong>Figure 5: Cars parked in the parking area</strong></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">The class <code>Stack</code>, which contains the <code>Vehicle</code> objects, has the following methods:</p>\n<p style=\"text-align: left;\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlYAAACXCAYAAADXjhUnAAAgAElEQVR4nOzdfVxUZd748c8968v1Zokbyy3SVGhAEkRxwhbFn5KaaZY8aLYVJRbakylkapsguri1agqaurVZglLemsJQmq2ijt5qljg+oCkOBFoqliWNs2Suzvn9MTMwM8zAgOND9X2/XvN6ycx5uK7rXOec77kejv+lKIqCEEIIIYS4YqrrnQAhhBBCiF8LCayEEEIIIbxEAishhBBCCC+RwEoIIYQQwktaufshODDoWqZDCCGEEOIXpbyqssF3bgMrdysIcb0FBwZJ3RRCCHFduWuAkq5AIYQQQggvkcBKCCGEEMJLJLASQgghhPASCayEEEIIIbxEAishhBBCCC+RwEoIIYQQwksksBJCCCGE8BIJrIQQQgghvEQCKyGEEEIIL5HASgghhBDCS7wXWFVrGRcYRHBgEMGBGsZpv3H83agjI8z2exBR2XouNWf7JgO6gmwmLrRbr26fEyiqbtbWmsmEPvuBlqXb2TVLswdclemv0CV9NlGB9XXP8dObcdla9NUXm7/h30j5CSGE8NxVarE6x+6Scox231wq38eG2pZuz4T+nYmkvLSQnZe9kDyBlKlNNVsWpDFqzNvoTeZmrCflJ4QQoqGr1hVYW7AVvdF2o7pA5cG91Fytnf2SBMTzz6pKyqsWEhfQ6P+BLbyt7QRWl1dSXmX7VLD/X1kM8AGOrKGw5IfrnUIhhBC/cFchsIrh0SQN1OrYrLfeqMzH2VW4D3yG8OifgxuuYjKgy01nmK17Zmg6eToDJgC+oSilH6MWHAGgZsEI7gp8gBy9yXEb5mr0K2zbiCNjxR6qHRogzJgM2ynMHkt3l/uxX24TOSm9LcuEjSVn8zGH1je3nPMRGM6w9Fx0Bru1XXYFOu1zaDr5egMlDt2Pl6jWTrD8nrIKvV7rkMY391RjdkrLFvu8OnR5NVKmbroq67rTUrRU1x0XDcGBD/CGtoAZQ8MJDgwnIbcMM2ZMBh156XHuy+GGoMI3NJZhfdoC5zlTc6H+p5bWSU/Lz7Zcz79RtOlv1v0kkWcwNeM4GzHocskYGl7ftemyTgshhLhWrkKTyc1oBkVwKP81Nuw7QWZsO1QVn6E9UItP0v3ce8txVtovbj5B0eQnmbShuv67I++TlbyZnTOWMj/5fzzYZznrp49jS/ER698HWZmRzKHLBaxNDkWFGVPZB7yUkMEW++7II++TlVzI6om5rErrhS9gPvURU+LS2GhbrraYRU8Xe5CGGvTvvELKAr3dd7WU5c8kZe95Vn/4Ahpf13Fsg30eeZ8ZSRUM+NM517sqfoVR9kmqLWbBwz/ht2kpo0PauC5Ta5fX7mPw6eIoD/LjqSO8lTrJ+u9OdAu8BZzzYyuHgs/JKJzN6FA/L+7/Slykes861u86B8QQE97O8rVX6qSHzi1l0ljrv33UdL6tNRy2/t3ocW6NSZ9HavJ8yuy3d+R9spKPcK7gXVI1/t5LpxBCCI9cla5AVXB3+reFGt1BKs1mTCcrqaAt0VGhOF7qzRi3v0vGhmp8BmWx/lCFXfdMNVvmrKbE2J64pdtZPbErAP4T13K06hNSNb522znOyYBxbDhUQflXO1k6ujtQS+mOI3wLYDawdsrrbKnFbj9l7MhNIZRayhb8naX6GuAs25dkWwKCriks3V1mWe7DqZbuosZc+orty/VAJx7N3WPpajq0mvFdfeDIW2StMeB6BM8FKjauabjPJd04ubXa5RrQnUdzPmV/VSXHdmUz2AfgS3YePmsp1Yot5G6ohq4vsdqa1+058fgAtbsrqTbf4UGZNoNtP19tYOo9ZmsZBjBgxjr2V1VSXnWA9TOG4FP7KfNW6D1r/bsazi1kVLD9wPVQ+j48my21PoSOHsP96jZ4r056yofQiastx/KLydzjZ39KNnacazm2bQNlgE/SMvRVlZRX7WP1RA2gZ9FMLYbmDBkTQgjhFVdnjFWrzkQO6wQHitlVcQp9sY5awogJ/6PTgrWU79tDLVBbnMGwbmqCA9VE3m9tWbLvTmxUT0aNHkKIrwpUAWjujcQ+DjJbW8wghklTRxLqqwJaExD7DJOTOgF68rd9xSXz9xwvPQu0ZcAzo4kNaG1ZrtdIHkvs1HgSVO0Iig4ATrAy+SHGZa+iqPgkwX/bzLGqwxQmh7oubFs3KT5EPDKSfrZ91qXNhR4JPDk8FF9A1T6K+/u0dUxKSDKFVZUcWxbDqeKPKMr9K2NTtdQC/FyDsdabd1wfIh4ZTKSvClQ++Lb+hv3rTwDVbJnxIJGBQQQH9mDYjE8tx9lh7N31FsCAiX9nXq6WVTMHEqAC79VJT/Vk1IMRlmPp6+tQbxs/zq0JCAq1BMv5Y+ibkk2hVsfXwdPY8VUl5UXJhMjLVIQQ4pq7Spfem9EMisWHfWi1/8u/Ck5Aj0H0UbdxWq6Grw+7a5WBBuNeGtmf/022Xk0VPn7+tLb71Xz+HF8DtL2biCD7NLTB75Y/WFJy+ARnzf/m3Ila4CZu83e9nFuqTjyUuZAZSd2xdLu9wqTUNFITe9NlaCZF7sYX1e2zHd0Cb7E7II3s84/+3FS3oD8dwwMcfzcdJi+lN12iR5CamsakGe/Xdxf93h8/n+Ye9kucPVHpZvKBU7rPnuBLNz2YANSe40evBnbNYBu8/tVn/O9Lg/Chmi0LPmAff7ALaLxVJ+01Un627j9XGj3OrWk/fCq5WY8TCtQWL2RyahqTXhxB3zvjyNCWyTgrIYS4Dq7StDQVfnf1JJrlbFm8mFJ8iEjojVoF+x2Wa8P/BNwEnMN/4lp2p2ncJOjKbhGqm9rSEag5t5fSygtoQmxB0wWM3/8bAP/wTrRT/YG2nXzg3FkOVX2PmXbWgKF+uUb3E9CLpFlFJM0yYtBt48uaWirX5bCoeDmTXg4irDCZkAYrXdk+G7qAYc3rZBVXQ9fHyXimLxHRA+h+ajHRiQuvwszMP3CLn10Q6uPPbT5AbVfGF6xueffi1aQKIGp8BllHDjFpw0Hyn59Lz+LZxLVvzbWqk3VaFOhaqQLQPDGL9U/8FZNhB5sP/4C5cj2ZC4pZmZpFcLh1zJ0QQohr5up1FtzalZgetnYA59YYG1vLFtS88w7vlxmBi1TrrLOkwjLReaHbSKXuTXwPH2An82avocxktu7nbebmnwA0JPW/k1aqzvRJ6AnUUrpqDdurL1qW27OGDwpONLoP8yktz4dZZiTO0JlQxz5EXHwCox6MptHhWe72WZe25jJxsvxrAHwCo7hv+BA0t55lR5HOs6DKFhixh3/tPoUZMFdv4623N3u2e79uDEzsBBzhvSVaS1mbT6HLtMwQ7J6uu35jrOypOhE3d6FlDFytloysTzhlhiuuk1dafp4yn6Doud6WGZeZWzGp+xEXH0/cI8Po29R4QCGEEFfN1Qus6gIGwCeWgZqbXe7eL2oUkwYFQO2nZN3fwzKgOHkpZQQwYMooovxUQCtuamtZ3+3rFhpNSwgjMp+1dpnYxs3Y9uND6MRXSNH4A21QDx5pGSR8ZCkp0aF2A5yb2EX7WFLGaoCD5CfH0MU6OLpfqpZaAhg8ZgBql6XtZp/PH6LDvQGuVmiCH12iNJaxNxvS6HdnEMF3xpCy6z+E+mA3xspNmfreSVT/AKCajan96RIYRJfoCXxG+8YDxDo3E/XEWAb42JX1nTGk5B0EnyFMekLDjTInEN8ejHpmsLWsZjProxOYr7ROXnH5eUh1BwPHPmaZfJGXQt87LQPyu/SxzMb0GTqS+xp0vQshhLjaruLw1jaoYwYRAfgk3ovGz82ufMMZnbOcpTMsY0UA8BnE+HeXMz85HF/btoa8wMRBtkDjv6FZ/4mICl/NC6zalMfciYPqb3BdHycjV1v3qgUAVfvhzCn6J+Nt+/IZxPh317LUOgPMPX80acvYkJvJo13tbqFdHycjdzlz4ju5LWznffoMeol/Fs3j2e5t3azRGMexN+BDaNLfWL1iEeP6tLUbfO2mTB3GigF059EZi8mZej9uRgI55wbf0MeYX7SMjLpt2PJ0I71qASxllUbWUGsg9Go2H5+6eGV18orLz1O2Ou1Yzpb9LWPt3OG0l8HrQghxzf2XoiiKqx+CA4Mor6q81un5DTKhzx5ledmkTzzzbGN9THvIeTiZRUd+z4Ccj/hn/B3XO6E3DKmbQgghrjd39yL5P1WuOx+69B9K6IIjlNVqmdRHyySHnwcz7J5br1fihBBCCNEM0llw3anw1TzDuwXZ9d2PgKULL5OlRTOts9WEEEIIcaOTFqsbQmsCNPGkLo0n9XonRQghhBAtJi1WQgghhBBeIoGVEEIIIYSXSGAlhBBCCOElElgJIYQQQniJBFZCCCGEEF4igZUQQgghhJdIYCWEEEII4SWNvscqODDoWqVDiGaRuimEEOJG1GhgJf8fm7gRyf8VKIQQ4npz94AvXYFCCCGEEF4igZUQQgghhJdIYCWEEEII4SUSWAkhhBBCeIkEVkIIIYQQXiKBlRBCCCGEl0hgJYQQQgjhJRJYCSGEEEJ4iQRWQgghhBBeIoGVEEIIIYSXSGAlfuXMGHWZdA8MItjlpzfjsjdhMJmbvV2TYRM5maswNHdV4cYFDLlJBIdlojNaC9VURlF6nPVYJZFnuNBwNZOBLdmzybf9Zi4jLy6c7uk6jNcu8XaMGDYvZsaKMrxaNVqULxdl+ot0lcrUEzdc/XL2aznGvx5XJbC6pM8mqmc2+ktXY+tCtEQnHs3dQ3lVpd2ngv2bpnLbxlRGTP6IU826Jv1ASd4sFu3/6Wol+DeoDSHJ+ZR/OZNYPxVwAcOaLCYV3Mm8XWWUV+UzOqSN0zpmjCX5pC4o5fL1SLIrRj3LX1zCgRsiQc5l+gt13cr0BqxfDfxKjvGviByFa6YGffZYMnRnHb826siIexN9s1tMxJVT4RsynMlTR8KGNWyqcNEaIm4AfvyPb6P/X7wQ4pq6SPWetxgXZtf6P7T597FL+myibOv/ihpjrkpg1UqTRsm+NDQtvBY6FHaDzwSKqq9e6V/SZxObrcfbezCf0rFU152EqJsdf/CL5skEPVlrDNe+iVvY+Zryk6b6P83V6FekM6zuopFOns6AZYmz6NJHkJJ/Ag7MZOid/cnQfWvtcuzvFDyfRZfev76Z3qgjIyycYemLyUnpTXBgEN3TN/ONLpPugUks020nv67ry7mb0ozJoCOv7nfndLl3teq1d9l1adQcIS/ubobO2Am1y0nprnbR9WLGqJtJ3+Tl1LKTrPu6Oi5TvY+N2WPruoG7p7zJFoPTFqr32JV3OMPSc9EZmujgMRnQ5drVjcA4MnJ1luNk1JERPYaVtbWUzhhCl7rumYtU67V1x9ztsXOqd67SbJ9/U1k+48J6M27hZ1S7vIA4dxO1tA5dpFr/ARlDwxvmGair53FvULTpzfob7tB08vWnMBo21ec9bCxv7qm2Xu9s672F7vO3nNazLtOgTF9m2bsv0t1V15dRR0aY8znYGCMGXa6bfF3t+mUbphBHxntvOAQpjnXd6Rpiv25YJjpjbYuOcYvqfl2x7WLxuAPEFB6o7wHY8CIa3+aFFK00aZRUVVJevpbxNzVr1RvaDdli1UqTRsnubAYMymaHQ9fN/zFv0O+vd/JaoIb9q1ZxOmEwkQ0qXhvUg+O4fcEHbJf+8evgImeqKqilI8EdfC1fmU9Q9EICydvuYObuMkuXYc5d7Hz+SaZoT2CmHbGz1rI0qRP0yGTDV9vIim3XjH3WUlZwiLaT/0X5V5+z9qm78QNgJ3+bvZmbRq+gvKqSY7tn0X7jVFLf2Wu5IJr2snRCJjuDX2N/VSXlVWXsmPrfrE6eyVpXY49+yVShjC7ay4YZMeDzJEsPVnBwVqy1nOoWwi82kx25T+JDDBmbjjgsU1us40DQy+yqqqT8q50s7FDMuAl5dU/V5lNaxg+YwPZbX2HHV5WUV33G/ODPmRCXSdGpi24SVoP+nVeYsOMu5h+qsB6nCfxu1UtMWWPA7BdL1u5lPOrjQ8SMTzn25Uxi/cCkf5unk9bzu2cKOeZw7F5hqb7GsmlrvRv1we8Yt+kA5VUH+LDvl6S6TI8ZU9kHvJSwGKYsZf6E3gR4cjVvUR26yCntVAYnbeXWv262pP/QawTv+EvDLvQDy/jnto5M/qKC8q92svSeUmYkxtB3dgV9Z22jvOoA66e04p3Rs/nYPk8HZjPhHRi3pYzyqgNseOZ3rEx8gYX6GmhQpm8w5uERxLGR1Vu+sXsgNWPUb6WIWAZqnB5gXTJSljuVEc/vrMvXsd2vcOuOvzD04cXoTVyj+nWQol0BTPqigmO715Ez8R5qC7ait90PjIfYXHACais4fsa23g/oi3WQeC8a5+4/D45xy+q+ndoaztx9P/eH+jW97G+QVwMrT5v1GkbKs8kYmtaslijbvrqn/I03UnoTHPYceUeOoMuMs2wzczPVZrs0paxCb990GTaWHK2+/invkp6cnkHclbiQbxaM4C67VrKouif9S1RrJ7j4/huKUjSW71O0VDsn1rifwnd+R3xMZ5cFrmofxf1372Gz/geP8y+84SLVe3KZO2cnPkNHcp+6DWDGuP1dMjaomTQ1maiA1oAK39DHeGPJYHa8+q5XAmCfxEdICPUD1a2E3Gm7OHXi0akvEhdi+VsV0IPYu2+iTHeY02Ywnz7MtiMdiYlRYwkBWxMQO431LsceWXlUr8G5Dp+yP0eH/g1d9cW6bdm3Gtef804tyeZqShaOtZs04NzCcfX5JKUxOT7UUlaq9vRLSiDiyOeUnr4InGX7kmw2hjzD5PG2oMSP0OQsFibqyViyy/XAZPMZSnWVqPv2IsT6kKQKGMiMDXoKk0PdXFB/oKRgDV8nPs7oXgHWZVoT0G8ko3pUsu3gGcyAuWILuRta29UBP0ITHyGOjeRurLQLIC47BlXJ4db60LQW1SHjLv7x6kbUU17mBVv6fcMZPf914rZl84/tdq1DPiOZPGW4pWxUAWjujcSHGLtzyY+QmD+hrtVTcsyuhH3iyfrrU/XLxL/I5KSzvFew3/Vx8IskYWx7Ni7bQkVdwTQSbLjMl578Ofvp+1pGXb5UAb15cf7rPHr8rSZ7EbxXvzoRlzSEUF8VqoBwHnhwKBF2QZT5TBWHiGbAvV+j3Xnc2op3iM0FEDeoG86hTdPHuIV1H+qvAdFpbClOo2/d+f0AOfr69jBztZ51uU4t/h/r3bSqNs2jmOEGuObYeDWw8qxZ7yzbFy2hLGqONZouZdXo0MY3XK1lXIqWau4gbulc4gJa0UqTxu6CCbQu/gJT0lqOFv0J7dCn+CDoNfYfyuW+L9bx+beX6pbzP/4Bb63z49ktZZbWry/nMYz1TF+wx9Ia0EpD6r5KjhZM4I6Jazlq11JWkqbB0qvZioD4OWyYEYNP0jK21H1/B3E5bzO+x1MsnT+cAKfkm89Ucej3dxMR5ObChT8dw2HDvhM3eFfNL9kJVib3cupWDqXv6L2EvbaWjYvjaa8C28W51kdN59ta262vwrdDEOpa3TUMgH3pENwRDJWcNJlR3R5O/677mJf3CSW6TzxrtveoXgPcQdxSPeW7sxlw/J+MHTCLstB0dnxVwf6FsXD+gnVbjq3GlnPeuSW5Bv2Cv/BPnmLjV7b9reDJjqVk/6W5kwS8zdrla20F8IkI5DaHq6ClzB1aDOypbiMiNojSOSv4eI+OIg+6YaEdsbO2cXCWhjO6jynSainSvkfGsESyDvxsXeYCFTuLKbVvOQVLa82Xhx2DtupNvJn6OltCnmHyk54HVUAL6pC1Fai2Hd0Cb3G8YfjeTnDIWYqKD1357LiQHoQF2J9vTRwH/Il88CEiDhSzyzY2spFgoyFbvtT8KfxWF/mCivLTHhxbZ1dYvwCVujfxPb60BlGWelGR+ATj+ne0pqnxlrkmj/EVpM12PSlv0KP0Cama+pqourU9HSPH87Ht9w8fhaKZLN7uaRetPU9ihhvrmnMdugIv8GO1fZOzCt+QeLI2ZBMX4DQoyxYRR6exxc3WfCdmkh7bnlZB3enfaRTPJoXj63s7QR2ctuWTwOTMx9DUnbx+hAwfRcyOTylpVgtEG0ISU4grWEphXdP5RU4Vr2Znwij6NXhSMmM6WUnF3UG0dzvmrA3tg4KpOXyCllQ74Qn7WYEV7N+UzaNd2xKaeC/9ors17EaxjuuxD8S63DeT0uuSdivfXqR+qGVhVA1FsyeTcl+Pq/NU1iqGyVs+JOuJXgSoVPiGxBAb0owmf+N+Cnf0t3saBvAjZOBY0u4uvaEmCdTmj0HjEGx3tYzrcssfTdoyNiyJoqYom0nJg4lscnyKbSxUD4YmL6Wkxgz8kYE575HRo/k38NrijznY4yEGGN4m+6MTzRub2eI65OLB5M4hZB2odVwsJIgOzRxn0yiH7i9HKvUAkodWMC9vN0ZbsNF5ZMNxrC7ZhgA0suvSKs5c4WnV/PoFqDrTJyGM0sLPqDCbOFn+E3GD7kETMwh1wVb0xgucqapw3zLn4TFuUdo8pQqgR2RAfYDhG8r9D3bhTE1Lzn0PYoYb7JpzHabatGfg1EcomZBIZKq1Wnd9nIypoxkRG+L49DUomx1L4wmo1jIu/Qp3+0d/bnKug6o/4N/WyI+1ZmjONFW/aJ6cspQRebtJmBWLn+kAq9cF8peckBtz0JpwYjkxZ74L4welkVxt5sOcxwi1vyH0yGRDYTIhbg/odQqBfUOIjQ8hNv4psszV6D9ayVuvjmFE+TJ2NBiD1EIdwrnLofWgmWprOLN3JkPvnOnix7YMyBnD6JA7Wr59r/EhYkYBa9124bnjR0hsPCGx8YyedZFq/Sf875LZpMRVsnR3JrHOi5sNrJ3yOrv7Z7O9rmUUy0BrQy1ENDPVSYtZOSuaM3elMPTVbD6+ZzZx7ZtxvFpUh2LI2LTUfXfh1TofGrQc21Hdwb2PDIbnt6KfEgbFu1A/ssjFOFZXWnNboBofKtzvukGLTnM1Vb/Mblr62qCOGUTEnEpOnjrA5oL/Jni0L6rbAulGMcdPH+N44Rni0hppmWvsGE/xJG1Xxlyt5+OV/yBjQbFd8NqWATkTW7A1D2KGG+yacx3iAFu0edjaelDGjr9FcS7/ZWa7m8kREM8/l8ZbutiqN5Hn8YwPO9/VcN756cP8b2rO+fE/Ps0tBvtWKyOniouofPAhNye0Ch8/f1rvreSU236+C5yqLMc/vBPNGQItroyq/QNMX5JCx+LXeck2QJyb0QyKxcdwgC+r7Z+UzZj0bzLM3UsqbV2F1yTltl0GoIl/gscSO3nl6brFzP+m5ju7yn1rV2IeymLDV/YTT2wfPf+MvwGCKr9uDExsR8XnR5zGfdSgzx5BcFyuhy9+bU2AZjhPJQ3Gx13riuk05QZQ/6mrQ8uo2VTD93V/WW+mDWanWl5G2TA9bQgZOZHxnTc2PiamKU3WIRV+mnuJ86ng88PfOraOmfaQM1RDQq4XXtpp7e622zgny7/Gp9HxUir8oh7kqc46Vq8p4F8Ft7kdx+pyXbf5sh6v4Nub1c3q4ArrlyWI2sjyGUspChlEH3Ub6zbPsHruAlYb+ng4QJ+Gx9jXW3XfDXMZK154h8ruUywD+6sqKa8ysCNnYAs36EHMcINdc655YGU25JIwNJOiumbz1tza/jZ+59Hal6je/TFvtmQsUm0hc2d+gL7uZmnE8JGW8ocSiXI6cf9z+EsqTPZTpO9jnPYbx+35RfPkFJg3+zXeXHc7owfd4bYwWwX3ZOjPeymtdNccWcPXh2Foz07XownxN6w1AbEvkDXxLsoW/N06O0uFX7+nyeq/m4zp71FSfRHL9OWPmD1tGUycyIiQNtSNfwLgAibTJevYiLMU5X9KmckMGDFo32Ru/gmvpNZsyCUhMI4MbZk1CDRjMuxg814YPGYA6ibOZo/qdZPa8D8BZ9l39FvMtrfPj0smy/7Njaog7h/xHcsXaO3ON6xvUR/TjKnwnrAPaM3Ummo9vMm3o9/zafTdNpsZi2yvKjBi0GaTsQDGZ8a7bq00l5EXp2FYura+W8VkYGvxfrBNgPC9neAQ65izWhMm642sdNUatlvLw1z9GYunz2ajXV+USj2YiaP9WTn7bctkAS5SvX0Nqw/c5To9vneT8rdn6Zj/Ju/ZZhY2oUV1yK8Pz70WzY5Xs1hse02CqYyiv89iEWPIGOmFlvraNcyd85G1TI2U5WYwoUBD1vN9LK0yzmVqO8i+EQx7pCMb/zqbD20BiKf8NCRNiXTK12HyXvoLKzs/a83XNa5fdWnrxsDE1mwv3g11LWe+dAi+jbJiHV83EnA2fYyvMG1NMf+bc6faENS1szUwNWLQFfK/6/a3bHOexAzX9JrTNO8FVnUzhoIIDh7BohMLGRXseoZgqw7+/GfdpLrR+10GvMflR99gqnXK+iV9NlENZh0EERwYQt/Uj+uWibbNdLLNxDu/kFG9bPvazKTo4fUzFTqPZnKSD4VjelpnBU5i/U2JTHWaUdMq8lEWdN/Gw93UlsHN00pom7SE+Q0iXssT41MnP+NHt61VVn6RJIy9XD+jw4n5VAn/2tvL8ycQ4UX+aMa+wviuR1k0bbHlhqbqRNzc5Szs+w2Z0aEEB6qJjFvPLc8s5d2Jvaz1pQ3qIWNIujiXoXfezRNrKjCrQhjxRg5PsZhh3dQEBz7B8vMDmTw9xispVYU8xj8KxnDLumQiA4Ps0pXN9OGdGj2Zm6zX1VrG2cYzOpx3zu+Nu5mo0XFcnn0fXQLVRE7YRrvnc5g7aAeTol+yLmsJWF/oD9vT+9e/myf1E0h8nZnNejWFB+WiHszE0efJui+c7o+vtpsl1sR67Yczp+h1Yr79O33vDCI4sAcj1vkzrmAxEzT+blYK5Ym3lzLulvWM6GYdg9ctmfW3jCE38wFLN58qiPvTRnBxxhC6hD3L2n+U3wwAACAASURBVApf+o1/gxl3f0FKdCjBgUFEpu/mjqQ3mJfUyT5BxGa+zerHfmJudKjlOM3+iUfdpkeFb2Qc44aeYtG0FR69nLFldag17eNnsnZJDN9OH0gXuzyvXvZMs99d5FLXBEZpKpl7j5rgwB48vCOMnKKZ9V2cDcrU9pDaBvXgkQz28SEioXddYGgJLsKbaE3zIzR5tlO+XqW87+ts+PCFunxd0/pVx9pyTie7wfi2Vk2fRlvTPDnGLU6bJ9eJVt3488JQ1g8Ktf72BMur/InsG8yW1P9XPxvZizGDx9ccty3A3vVfiqIorn4IDgyivKry6u35GrqkzyZ6SRDrbN2J3mLaQ07qF/TLea7Ji4v5lJbxz31NyvsvOC17AUPuc0zhlavW3/1r82uqm0L8tllftls6pokxje6ZDbmMiDtAcrH9WDMzRt3feLpqFKvkuiquEnf3IqlvLXaxibFVjlTtY0mJPUhhidNUfeNulhdqvNOcLoQQvyXmU2zP/5iLYx9joMMAfiPl+05wl/MrIoS4Bn71Q3psXYY1QN/AtPqZhi3dYLWWcQ6vf3ifnZVr2e3wTiBX/NGkvYPG+Wu/WLKKGswjEkII4dYFDLkpDJ2xj9Ckv5Ez9m7HrjHjQbaf/zMv9JPpQOLa+010BYpfF6mbQgghrjfpChRCCCGEuMoksBJCCCGE8BIJrIQQQgghvEQCKyGEEEIIL5HASgghhBDCSySwEkIIIYTwkkZfvRQcGHSt0iFEs0jdFEIIcSNqNLCSdwWJG5G8x0oIIcT15u4BX7oChRBCCCG8RAIrIYQQQggvkcBKCCGEEMJLJLASQgghhPASCayEEEIIIbxEAishhBBCCC+RwEoIIYQQwksksBJCCCGE8BIJrIQQQgghvEQCKyGEEEIIL5HASgghhBDCSySwEr8pZkMuCYFBBIdlojOaPVzrLLr0/gTH5WJwu4oZoy6T7oFJ5BkueCm119oFDLlJTZaNpQz7k6E769FWLct7oVxMBrZkzyb/isvXjMmwiZzMVXXHs7l58j4jhs2LmbGiDE9rpUfMZeTFhdM9XYfRm9sVQrglgdUN4pI+m6ie2egvXe+U/JpdoGJnMRV/foFnQ3axWf/D9U7QL5IqJJnCqm1kxba7hns1YyzJJ3VBKZeveFs/UJI3i0X7f/JCurzEqGf5i0s4cOWZE0JcZ7/gwMqEPvsBggM1jNN+08x1zZj0b5JwlZ7iLumziQoMIjhFS/VV2H4jOyanZxDBgUEEBz5Ajt50LffeOKOOjLg30Zu8+jzezDTsZvmcr4kb8mfiEm5jZfa6RlqghBBCiOb7BQdWvmjSPmJHzsDmr2r+hs3v6OmfGIkfQLWWcYG2gMQuIHL6PipbjycNSq00aZTszmZAM5LUSpNGyb40NK2anx27jZC6r5LyqlJWT+xwBRu6CvyieTJBT9Yag3e7OjxmxqjfShGxDNS0Rx0ziIgDxeyqaNitZK7WU5Q9lu6BQQQH9mZc9jr2V190Xgi9NptxYZa60T1lARv3nXJcxmRAl5vOsLo6FEdGrg5Do8HlRar1WnJSetfXx6Hp5OkMuAuT3Xa1GXVkhNl3b12kWv8BGUPDm0jPKfb/a0Fd3oLDxpKzuX7/rrrNzNV7yE+Ps263N+OyNzWaT8flwxmWnovO4O4xx4xRN5O+ycupZSdZ93W169oyYtDlepAnm7Po0keQkn8CDsxk6J2O5XNm3zq7sneVD0/L0DH9JoOOvLr8Oh1To46M6DGsrK2ldMYQutR1xXpYF8zV6FfU17PuKW+ypZGyNJXlMy6sN+MWfka1PFgI4X2KG+rOge5+uoH8Rzld+LIytvDrZqxzWTm/d6HywPBlyrHL9t9/p2yd9qKSe+wnp+V/Uo4te1Z5rvC4cllphtOFytinC5XTzVnHa84re+enKNl7z1+Xvbtz+WSh8lzkdGXrj80qyQZaVje/U7ZO66dETNuq/KgoinL5qJI7vKcSv+yo43E9/4WSPaSn8sC0QuXY+cuKovyoHCucrjzQOVBR19WZc8re+YmKesh0RXvsR0VRLivnjxUq6UPCFHXnx611yLJMxNMrlKPnLXu4fLpYyRziYp91rHWza4qy8IvT1mV+Vk5vnaU80DlRyd57znXWXOblsvLj1ulKRFdbef+snCxMVSLst33+kJL7dLQS8WyhcvKyoljq+uOKunOYXf4b7v/ysWVKfOd+SvrW7yx/nyxUnutqt47Tdi3LP153blmWj1bGLtilnL6sKIryo3J02bNKRNdURXvyZzfHz5ofu+3Ur1efp8undykLn45W1EMWKnvPu6tnlrqgtrsGWNIYaHdM64/XA/O/UM5bj0XTZejC+S+U7CH9lLHLDtVtx1Kmdnn5cauS3jXM7hh6WBcuH1e0z0bbpdupLC8fVXKHh1nr/WXl/NEVytiu0XZpEUK0lLt7kXdarOq6oCZQqN/Omw5PfFr0tqd9u+WKqi9Zv7J2m9l9Z+HiyTD7FYa56l6rOURR3dNgHBkr9jTyJPYDJQUf0TqhN2qH3N9MVGII2rzdjt2DplLWF7Zn1IA76pr3zNWf2eWxsRaFHziqzbQ+SYYzLP2D+rKoKzpb/oMIbmyMlcnAlrpWFOs+c9N52MNWNHuep78xtq5Y+7Rfolo7of47p+5IVfso7r97z3UZ22Q2rGNBPsQN6mZppVR1pk9CGKVzPmB73UBtM8aSdbx3/CEmTxlOiK8K8CMk/kUmJ3Wq35hxP4XvnOXRqS8SF+IHqPANGc7kqSPxqdvhGUp1laj79rJuB1QBA5mxQU9hcqibpuIfKClYw9eJjzO6V4B1mdYE9BvJqB6VbDt4xnVrny0vqzayv67l5Af0xTpIvBeNnwqMu/jHqxtRT3mZF2zb9g1n9PzXiduWzT+22w3a9hlpl/+m9n+Bio1r2IjdOr5dSUgaDBvWsKlBi+BZti/JZmPIM0we35sAFYAfoclZLEzUk7Fkl+fd80Y9+XP20/e1jLo8qQJ68+L813n0+FstaB3tZHdMQRUQw+OPhFGmO8xpM80rQzvm04fZdqQjMTFqfAFoTUDsNNZX5TM6pI2btHhWF8wVW8jd0Nou3X6EJj5CHBvJ3Vhpl//LmMo+4KWExTBlKfOTw61pEUJ4m3cCq1YaUvesZXzbAyxdso22zxdyrKqS8qqdzH8QCtPfsYytaaUhdd//MW/Q7+1WTaOkyvE7AIy7WDyzjKiFn1FeVUl51QqeDHJaBoCf2TLjH5REzWF/VSXHdr/CrdtmsdjNRQ7z9xwv9aF/9w5OmVfhGzmY+CP/h97hRvsp22KHEOVnXdq0h4XPLIdnbHmspPzD0XTct4Rp2hOOF/Liecwt6UnOoQrKv9rMzFu3Mm6R443Dkv9KysvXMv4mN+VrPkHR5JlsDXqZXbZ9rh9PuPEMp9ys4lZz0t8oXzRp69hf8BIRScvQ70tD06oVAfELObYrm8F3Z7Lhq09I1dhfvv3pGA4b9p1odjB4ZSyD1kt9Yhmoudn6XRtLd2Ctzi7QswQjtSFBdPC1rx2+dAjuaP23tUuxtiPBHezzpsK3QxDquj9vIyI2iNI5K/h4j44ijwLXdsTO2sbBWRrO6D6mSKulSPseGcMSyTrwcyPrtUE9eCSDj6+hsMSSF/OpHawuaMdTiZH41aW5Hd0Cb3Gs9763ExxylqLiQ00END9TUX66YR7Mx9lVuA8cykyFX+xMDroKHIyH2FxwAp+IQG5zTAgdgjtSW7DV7vxrjC1Pav4UfquLPOE6vS1hqOSk6VKLy1B1ezj9u+5jXt4nlOg+aaTL054ndcFar3Gqi36xZH152DGAr97Em6mvsyXkGSY/KUGVEFeTl8dYdWTU1MkkaWxPWCp8Q4bwZN+yugu+x2prOOPQuONHSPxM1i+NJ8Bhwd8RMWMOM+ND8QVUAb14+MEunKlxMyXbdJpyQ2eC2rt4UlQFcd8TRhYXWJ90zd+wddUp4h+MsF6ILIHWzoTU+idWAN8QBkx8Fs3m7VTY3xN6TCbnr/GWp3hVAFGjhqGprqG2eSXBpf1rmXNLCpOtebSkNYCoCe+wM02D58Oympn+JqnwjYwj+ftVFNaN77lAxcYt3PLig4Q0qF1taB8UTM3hE1zTSe22m3/tclK6q+ta1LrcN5NSTtTfEM3fc7y0qZRd5ExVhQfH0B9N2jI2LImipiibScmDifRgLJFl/EsPhiYvpaTGDPyRgTnvkdGj8UBB1b4voxKx5sXaihTyEMMi/e2WOsHK5F52LYpBBN85hKwDza2RV642fwwa+3QEdmXojJ3N2ELTx6G2tIozXh9D1IIy9O1F6odaFkbVUDR7Min39fBgbFbL64IrtcUfc7DHQwwwvE32R815gBJCNNeVDJV24Wb8b3LeZCtu8v+9+0DHnYABTH5mH6n39WASAD6EJk1m8uhEYq1N9bbt/9H/D16KEFvTfsAw7hrzGRVPhqKu2EI+97NAbQvCzNTWfMe+GUPoMsPV+g8xb9qfCQmwlsEf/bnpihN2ibMnThEe9TB+TS/chGam3xOqO7j3kfaMXlfKiLRe+Bp3s7wwhIT3b2563WvCjHH7B8w70JOMTUudWlBsg6KXUjg6mtEht9A5oh2UNra91twWqMaHCg/27UdIbDwhsfGMnnWRav0n/O+S2aTEVbJ0dyaxfk6Vw2xg7ZTX2d0/m+2L42lv+9moI8NQCxGN7etmNINi4fmt6F++jeOFXxKRkOHU3R3jogzsXeCMB7m6cj5EzChgrdsuUU80fRwatop5Q1Nl6IZvCLHxIcTGP0WWuRr9Ryt569UxjChfxo5ZsQ3P7SuqCw35JC1m5axoztyVwtBXs/n4ntnEtW/dvI0IITzi5cvOD9Scd+7kucT5mp+5zb+RC5H539R857yetYXK1l311Wayos7xwah5zXixows+ftzy+3IqT7kJ9PwiSei7k+Xbv6Fip567Hulbf1GjFbeG38OwGZ/Wd6M5fBYS15ygxCMqfPx90JeUe+HVEFcj/Sr8+o0ifsdqNp8yYihYxfdj4oj0dVW1LnCqshz/8E5cuzcgWccaDR3JfWrnOqjCT3MvcT770O48jtkanPgYKjnp0JJg4mT5107rfE35Sfs2AzOmk5WN3OZbE6AZzlNJg/GpreD4mYsNFzGdptwA6j91tY49sm7ZVMP3TebTmi50/GvZGrQHwoiP6VzXcmxJcwWfH/7WsbXCtIecoRoSclv4YkpVZ/ok9LR2l9VvwTJzMLzhdv26MTCxHRWfH3EaB1mDPntEEy9hdZFfl3mylmPw7V7s8vJiGaoC0MQ/wWOJndy3qnlUF6zd2TjVRetLQRuWZRtCRk5kfOeNzRvLJoRoFi8HVl+zevZc8vXV1guMGZPhU94vjyYpqn5sy/8EnGXf0W8x296APC6ZLIc3413AkDuGYena+qZy1c207/DfV57EVp2IHPYfth086eYi6E/kgzEczZvJ6wva2o3JsSZDPYCEEx+wUKu3uzGYMRm0ZMRlXVnQ55IKv35Pk/H9UuZqy+qb/00GdAXZjBvh6Y3IurWrkX5VCAkv+pG/ahVFTgP9HdXw9WEY2rOTt5tK3TMeYnMBxDkEyHb8IkkYexelhZ9RYbaUdVb/jUx46QPKTGbAiEH7JnPzT9it04fnXtNQ9HwGeWVGLOX3EXNnr6nvmjKXkRencazDJgNbi/e7CfKoCzpKV61hu3WSg7n6MxZPn81GT3rr/CJJGNuODxe8R2mPQfSx34dfH557LZodr2axeI/1/DSVUfT3WSxiDBkjQ1p4MWiDesgYkjp/zNx5Wy11ynyK7fmFlHZ91sV229Hv+TT6bpvNjEW26f5GDNpsMhbA+Mx4F13I4DiGzUytqRazn4akKZFOeTpM3kt/YWVnV/u2sR8zdwGTycMRfy0sQ0uQGUdG3flrxmTYwea9MHjMAEurou/tBIdYx5DWmjD5elYXVOrBTBztz8rZb6OrvghcpHr7GlYfuMt1WfreTcrfnqVj/pu8p6/xLN9CiOZp7jRCt/6zV8mOfFHRHjukaKcNV9SdAxV152hl7PyN1mnbNvZT0wMV9ZBpyoq9u5WCp3sq6s4vKtrT/1FsrzgYO3+Zkv10tHVbgUrE0wuVzdap0JZXCgy1/tbT+soF++8Clbvn71X+0yCh7l63YL/IcUX7bD/30+Ivn1b2Fs5XxnYNtMtnobL39M/Wopiv3G1Ng9r6ygWH7yLnK3v/YyuzwLr0Onxsy9icP6Zsnp+iRNh+75qiZK/dZle2jnl3/Ax1fPVCE+lvEetrCty/SuB6vG7B+vqARqfeN3x9gGNZhykPTHtf0c7/s8P0fEX5UTlWvLC+DIdMVwoK5zq+VuD0XkVrf8w8KOfLp79QVtSdP4FKxNPzlYKtOxSt/asiGmHJS7Sb14P8qBzbuqz+3GuQHmt5dXU6Rg5T9l2UV4N0W8rMtl3n1y1YrgFblVynfGr3nm78lSaXTypbp1vXqTsWznkarqQv2+p0zXFVzsVK5pAwRd3Z8oqD/7jIk+vyaKoMXflZOb230OFapu6aomQX7rW+bsK6zNZZltd6WMvK47pw+bSyd/k067q2a6q1LJ2OnWV52ysaLOeF5fiENXruCiEacncv+i9FURRXAVdwYBDlVZWeR2iX9OT0yiVow/yr0B3mZeYTFL2QSeXYbFI1/k0vL7zgAobc55jCK1c4tqYFdVMIIYTwMnf3ol/wm9evgOoOBo7VsK1gv4wzuFaMu1leqLmCLichhBDixuedFqtLenJ6jWDROdsXDzFv9y+g5Ur8IkmLlRBCiOvN3b3IO5GP9f+oS/XKxoQQQgghfpmkV0YIIYQQwksksBJCCCGE8BIJrIQQQgghvEQCKyGEEEIIL5HASgghhBDCSySwEkIIIYTwkkZftxAcGHSt0iFEs0jdFEIIcSNqNLCSlzCKG5G8IFQIIcT15u4BX7oChRBCCCG8RAIrIYQQQggvkcBKCCGEEMJLJLASQgghhPASCayEEEIIIbxEAishhBBCCC+RwEoIIYQQwksksBJCCCGE8BIJrIQQQgghvEQCKyGEEEIIL5HASvzKmTHqMukeGESwy09vxmVvwmAyX++E/kacRZfen+C4XAxXpciNGDYvZsaKMuSICtcuYMhNaqIO2q4b/cnQnfXivs2YDJvIyVx1leq/uBFIYCV+IzrxaO4eyqsq7T4V7N80lds2pjJi8keckgvdL59Rz/IXl3Dg8vVOyK/TJX02UT2z0V+63im52lT4xc7kYNU2smLbeXG7P1CSN4tF+3/y4jbFjebXF1iZ9pATl4XOaH+XNGPUZZGQvQfTdUuYuPGo8A0ZzuSpI2HDGjZVXLjeCRLimrmkzyYqMIjgFC3V13bH5PS0tRg/QI5ersri1+VXFlhd5FTxarbFDiHKzz5rKvz6jSJet4S1Brl5Cle+pvyk3QXeXI1+RTrDbF2GQ9PJ0xnqA3OjjoywcBKyC9iUPdba1RjOsPQP0FefxbD5TcaFWdbtnvIWJdUX7fZlxKDLJWNouPXmEkdGrq6uO9JsyCUhMIk857pq1JERZtc10VQaMWMy6MhLj6vv+mywjAvmavTa7Lr0W/KVi85gtC5g6857C92eD+rzETaWnM2O2zZX6ymqK5/ejMtex36Hsmiwc2sXTBwZ771hTUN9ns3Ve8ivy49Tuow6MqLHsLK2ltIZQ+gSlonO+K0lrWGZjg9bRh0ZYUF0T9dhdLvPIx7m0/l4OpeXu2K2L5uG9aCBai3j7LuxbQGR0/dR2Xo8aVBqpUmjZHc2AzxY1mGdfWloWjVjpYYbIXVfJeVVpaye2OEKNuRO4+dXvbN86XCevsmWumPmuiuw0fpXv5DDeVm/3bPo0keQkn8CDsxk6J3e7mYUNwzFDXXnQHc/3bjOf6FkD0lWco/95OLHn5WThanK3dO2Kj9e84QJb2pe3bys/Lh1uhLRuZ+SvvU7F7//pBxb9rii7vx4fb25fFzRPhutRDz9D2XP6Z8VRbmsnD+6QhnbNVp5rvC4cllRFOXHrUp610BF3Xm4kl54VDmvKMrl08VK5pAwRd05Whm7YJdy+rKiKOcPKblPRysRzxYqJy0rKkeXPatEdE1RFn5xWrmsKMrl07uUhU9HK+ohC5W95y8ryuWjSu7wnkr8sqOWfdnno+t0ZeuPlz1L4/kvlOwh/ZSxyw4p5xVFUZSfldNbZykP2Oe1gXPK3vmJdttVFOXySWXr9OH16VO+U7ZO66eoO4cpD0wrVI6dv2y37UQle+85y3rnv1Cyh/S0W+ZH5VjhdOWBzoGKevgy5dhlV/u3Ha9AJeLpFcrR85eVy6crlIrzl5XLJwuV57ralW1dWaYq2pPWtP64VUnvGmZXdta02srNxnr8IqZtVX50u88zHuTzsnJ+70Llga7PKrlHf3QsL7d5tDs2dXmx1Z+eygPzv7AeL1e+U7ZOe9HF8ftJObbs2fpj76nThcrYpwuV081Zx2vOK3vnpyjZe93ntvk8OL/qzvlAu3puq5v1x9X5uuFR/bOel+oh0xXtsR9dLGOtj43VDfGL4e5e5J0Wq7qm3QkU6rfzZkpvu4HBWvROT6iOUb/zk4IJffYDBAdqGFfwOSUL65/ouqdkU6SvdjMo1YyxZB3vte5PH3UbF7+3pn10fzQFW9EbZTCNALhI9Z5c5s7Zic/QkdynbgOYMW5/l4wNaiZNTSYqoDWgwjf0Md5YMpgdr77Ldrv645OUxuT4UHwBVUAPYu9uBz2eYfL43gSoAF81ffqqqd22j2MmMxj15M/ZT9/XMnihVwAqQBXQmxfnv86jx98ia40Bs6ozfRLCKF21kf11T9k/oC/WQeK9aPzwKI3m04fZdqQjMTFqfAFoTUDsNNZX5TM6xNU5Ahj3U/jOWeKSRlq3C6ja0y8pgYgjn1N62u5c9hnJ5CnDCfFVWbatiUHjc5RtB89gtp2Pxx+yW8aPkPgXmZzUyYNj04m4pCGE+qpQBdzJnb4/sH1JNhtD7MoWP0KTs1iYqCdjyS4abx/yhPM+VR7k8yKnD35OWcif6BPiV1desTOLKC9KJsTlFdZWNrE8lvwna15AFRDD44+EUaY7zGm3l6ibiUoMQZu32zG/plLWF7Zn1IA7rN0QF6ne81YjrY72fuCoNtPawmJrdXW8Ztd1GwYGEdzYGCuTgS32rXBD08nLTedhD1vR7JmrP7O7l3jY2urJ+WVb1ieerL8+Za3ntrp5lvcK9ruoS2c9qn/mii3kbmjNo1NfJC7Ez7JM4iPEsZHcjZUyoeI3wjuBVSsNqXvWMr7tAZYu2Ubb5ws5VlVJedVO5j8IhenvoLfdIEx7WJi+hTvGf1g3iPjgP0egys8kR18D+KJJW83qiR05/s5S1rUdx8avrMvlPAAFs1mor3GRiIucqaqgdWx3gtzlql0nwn6/h/3ltV7JtvglOcHK5F5OMwJD6Tt6L2GvrWXj4njaq8AWwNT6qOl8W2u79VX4dghCXatjs/6HFqbBjFG/laJaNX8Kv9Xx5PO9neAQqCg/jYk2qAePZPDxNRSWWPZlPrWD1QXteCoxEj8P06i6PZz+XfcxL+8TSnSfNNk1BYBfLFlfbiMr6hw6rZYirZai3HQeum8mpU2t65AHaxpDgujga59TXzoEd/SotBwYD7G54AQ+EYHc5lhwdAjuSO21fGByyGdrbu/+J0IPLGP5RzvRabd7MMPUOjD6y0yizuywlLG2gLz0Rxg6Y2eT6/pGDib+yP/Z5deMseRTuyEQZkz6t3nubRi3pcx6nS1l1egO7J8/h6JTTl2xxfOYW9KTnEMVlH+1mZm3bmXcIsdAtZUmjZKqSsrL1zL+JjdJM5+gaPJMtga9zC7bBJH14wk3nuFUE7lqwLSHhc8sh2ds95JKyj8cTcd9S5imPeH+4dqj88sqpAdhAfbnTyN1yaP6V0vFzmJK6UhwB9/6RfxiyfryMIXJob+2sTfCDS8f546MmjqZJE2AdcMqfEOG8GTfMusN4gKGNSv4PulpYu0rtKo9sc/155jTk4LPIy8z/YledU90+Iby0Oge7HT5RGHiZPkPaIJuxW33f6tbCbr733x5wlVgJn7d7GcFVrB/UzaPdm1LaOK99IvuVl/HbGqXk9Jd7RCIdWkQXPigDr4dXzxlCf4bC+trS6s4YwZV+76MSoSi4kMYuUDFxjVsDHmIYZH+nqfRtxepH2pZGFVD0ezJpNzXo+lxPBgpy32O7t0Gk/J2CTUA/oOZX5BBhPM4tMaYv+d4qffHj9Tmj0HjEBx39SAYuZpU+GpeYNWmLKJqNjA3dTRDu6mbbl0xHSYvJYbI+57jnyU/ACr8B2WxekYMGCo52VhwpgrivieMLC6wtr6Yv2HrqlPEPxhhrYs/UFJgIL6uNdOazpD7mPDSXWxxbjnpMZmcv8ZbWuRUAUSNGoamuqbReurKpf1rmXNLSl0LrmW3AURNeIedaRr31+UGLIHizoTUulYnAHxDGDDxWTSbt1Phsng8P79a6sarf+JGdCVDEF24Gf+bnDfZipv8f8+ZmgtAG86f+4lburvohmjXiS7bt1F+KdY6MLIVf/T/Q4PIT3WTP22tJ72fdxMvfjNU+IbEM/NdGD8ojeRqMx/mPEaofctKj0w2FLrryoGW9Tu15rZANT5UuF2i/on4ZjSDYuH5rehfvo3jhV8SkZCB2j49TaURwDeE2PgQYuOfIstcjf6jlbz16hhGlC9jx6zYBueQ2VDAKzP20zdnG4viO1nPPzNG3WYqgG6eZlV1C50j2tF0M1dz+BAxo4C1jT35X3l/YAuo8A3pR1xIP+KSZ2Gu1vPxyn+QkZxCee5aF9P1L2BY8zpZu6KZt2s2ce1twc9ZdOlfA+om9tea9gOGcdeYz6h4MhR1xRbyuZ8FdUMgLvBj9Q6y7utKlqvVB2Xzl+RQm4CKowAAB4JJREFUAmx//9Gfm674EfsSZ0+cIjzqYS9cl83U1nzHvhlD6DLD1e8PMW/anwkJcL7XeH5+nWlRupqqfxcwtGi74tfGyy1WP1Bz3rkn/RLna37mNv82QCtuavvffG9sODPPXHmQnf16Etyqfr3vav7doMnXfL6GcwH++DTYQhv8bgF95bfu+/IvfUvl3j8Q1snf3RLiN0TV/gGmL0mhY/HrvPTOXmvrgiWg8TEc4EuHcSZmTPo3GeZqtp7ne8RPcy9xPhV8fvhbx7ptOk25AbsWMOuy6PjXsjVoD4QRH9PZesK2MI2qADTxT/BYYic3T+5mTCcrqcC5K+USpprmRiy2NDq3vpg4Wf51M7cF+HVjYGI7Kj4/QrVDumvQZ49o5GWPLex6vAKqAA1xTz9OnM9ZDlV976LbyloGzl1R5lp+/L6xGZN2/CJJ6LuT5du/oWKnnrse6WvtygZoR1jfBDI2HXF6b5v1szS+PqjyGhU+/j7oS8q9ENu24tbwexg249P6bkCHz0LiGgRVljR4fn7homXQclx8Eu9F4+d0a/So/rVBHTOoYcuuuYy8uPCr+FJccaPxcmD1NatnzyW/boC5GZPhU94vjyYp6magDSEjn+CW/H9QZD/ew1TGx3kHiEmMdHjaqV31Bn9dsae+IpvK+Dj/ax58QuPiqciH4J69uKg7SKW7ynv2BF/+3IvI4IZhmfgtak1A7AtkTbyLsgV/Z6m+BsurOZ4mq/9uMqa/Z31NghmT4SNmT1sGEycywt3Ab0/4aUiaEsmOV7NYvMd6npgOk/fSX1jZ+VkyRobUn5R+kSSMbceHC96jtMcgu0kZnqXR8tqGODK0Zdag0YzJsIPNe2HwmAGOrV+27WruJc5nH6vzd1rPu4tU73mPGa9qm9k1ZEvjRia89AFlJjNgxKB9k7n5J1pQcO3o93wafbfNZsaiz6xpM2LQZpOxAMZnxlta7nxvJzjk95ZVak2YbDe72o18UHDEUg6mMormZLPSK0MtrW/xHpppd00zYtiyFT2DSR4c5OIiaw06DxTy/vZT1u68akoWZZGxwdM3SvkT+WAMR/Nm8vqCtgzU3Gz3WxvUQ/pwIu8dp8k+RgzaTBLSdVehYc9yvDO+X8rcuvoGmAzoCrIZN6J5QYVKPYCEEx+wUKu3C2TMmAxaMhq8p9BOc86v2jXMnfORtVvcSFluBhMKNGQ938fF/cWz+qdSD2biaH9Wzn4bXfVF4CLV29ew+sBd1mXsA/0LmEy/+jet/iZ5ObDqwbipidxU8AxdAoMIDozhpXV/4PFX/lzfzeLbiwmzhnA+74n6WYGpG7lp9ExSNfYtSa3o/Ewqj9+0jqfvtFsuaSKjQ101Nqvwi3qQpy5uY5fLFz1e5NTubehdPY2I3zB/NGNfYXzXoyyatthyMVR1Im7uchb2/YbM6FCCA9VExq3nlmeW8u7EXs0YU+WKH6HJs1m7JIZvpw+0nCfdXqW87+ts+PAFNA4Dvf2JfPAhIghoGAh5kEZVyGP8o2AMt6xLJjIwyG6ZbKYP7+T65Pfrwwv509B8MYG+d1re5zR9xy08ljePR5v7PGJNY074Nh7upiY4sDepJaGMmxjd3EKzbK79cOYUvU7Mt3+3pq0HI9b5M65gMRNs1w5VEPenjeDijCF0CXuWtRUXUIUkMufdx2DOg5ZyeHgF5+MmMK2HNx6w2hDy5Busfsaf9XE9rNc0a7ryp/JQ+9Yu1lHh1+85crMi2JMcY60DGey443Hey3ncRWu8m/JQDyCpzTHOTXyMfk7XNFXAQKaNj4FtGdZjH0Rw2CTWE8c//mrpAr6kzyYqOo0txWn0tb4Ty+E72+w/+xd6Bo9g0YmFjAp2MUNQ1Ym4uZncW/kGfWz7vGcO+7mbyXlPWrusbbO+gwgOjGDUgmIWJUY0fFmoqj2x6c/Rj21M72YbzxTDS+sg4e2pxLq9hv//9u4YJKo4jgP4ryVc20Ic7vIgyMEhjIZAEYcagiI6KAp1aagpGhvKrbjSE4JKHAo3adAWhRaJhoa4NYLEIGgNQhBE7hoqictnvvyndX4+271793gc/8f/y+/H//9yPF/dw3Gl92NUjnVGqdAd518diersyE+t2ab/e0vjrz36bj2O6YsrUTl++NsCmbsrcWH9nLboPDkcl1YrcerQ0bj8bDHqKlotZ1+j0Whs9EWpUIz3H5a2fqW1WlR7nkRxbjSjTJvHctTGBuNRcTwmznTk+N1qfJq5GdeWyjF1vWkCrL+Lp2fvRNx7mL3UnP9C7rEJkEs9viyMxImhxbjxYtKcwYay5qIWK93sj/aBcvQuzMeb5lfavJyOmb6r22vjALB3/LKlCfxemorVWi2qPefiwecfB07H/dd/WrlajtpYOcrjb79/PhD91ec5K1e0MhUr4K9ZfzXSwei/PRmjQ13bbP/TqrLmonStQNghxiYAu22PtAIBAHaPYAUAkIhgBQCQiGAFAJCIYAUAkIhgBQCQiGAFAJDIpjt4lgrFnboPyMXYBOBflLlBKAAA+WgFAgAkIlgBACQiWAEAJCJYAQAkIlgBACTyFVr+kJzNsOf4AAAAAElFTkSuQmCC\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n<p style=\"text-align: left;\">The method <code>staffRemoveCar</code> will remove a vehicle with a specified registration plate from the parking area (you can assume the vehicle is in the parking area):</p>\n<pre style=\"text-align: left;\">public static void staffRemoveCar(Stack stack, String reg){<br/>    Stack temp = new Stack();<br/>    //add code here<br/>}</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the <code>staffRemoveCar</code> method.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A stack can be implemented using an array or a singly linked list.<br/><br/>Compare the use of these two data structures for creating a stack.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for looping around the main stack using isEmpty() method;</em><br/><em>Award <strong>[1]</strong> for using \"while\" loop in both loops (note: not a \"do while\"!);</em><br/><em>Award <strong>[1]</strong> for breaking out of outer loop when car is removed and other cars are replaced;</em><br/><em>Award <strong>[1]</strong> for correctly removing cars before target car is found;</em><br/><em>Award <strong>[2]</strong> for replacing cars in same order by popping off temporary stack;</em></p>\n<p><strong>Example 1</strong>:</p>\n<pre>public void staffRemoveCar(Stack stack, String reg) {<br/>Stack temp = new Stack();<br/>while (!stack.isEmpty()) {<br/>    Vehicle v = (Vehicle) stack.pop();<br/>    if (v.getRegistration().equals(reg)) {<br/>        // correct car is now removed<br/>        // return others<br/>        while (!temp.isEmpty()) {<br/>            stack.push(temp.pop());<br/>        }<br/>        break; // don't keep repeating<br/>    }<br/>    temp.push(v);<br/>}<br/>}</pre>\n<p><strong>Example 2</strong>:</p>\n<pre>public static void staffRemoveCar(Stack&lt;Vehicle&gt; stack, String reg)<br/>{ Stack&lt;Vehicle&gt; temp = new Stack&lt;Vehicle&gt;();<br/>  boolean found = false;<br/>  while (!found &amp;&amp; !stack.isEmpty())<br/>  { Vehicle curr = stack.pop();<br/>    found = curr.getRegistration().equals(reg);<br/>    if (!found) temp.push(curr);<br/>  }<br/>  while (!temp.isEmpty()) stack.push(temp.pop());<br/>}</pre>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award [1] for an initial valid comparison and [1] for a valid expansion</em>.</p>\n<p><strong>Memory</strong><br/>Linked lists can deal with memory issues more efficiently than arrays;<br/>Arrays are static so will have a maximum size and cannot grow beyond that;<br/>Memory will be allocated for maximum size at instantiation so while it is not full memory is wasted Links are dynamic and can add/remove memory as required- no memory is wasted;</p>\n<p><strong>Implementation</strong><br/>No particular advantage is gained by the use of either structure;<br/>As additions and removals (push and pop) work on one end of the stack, navigation speed (i.e. O1) is not very helpful; Linked lists can adjust the pointer at the end of the list (can use a tail pointer) so slow navigation is not an issue;</p>\n<p><strong>Errors</strong><br/>The use of arrays is more likely to lead to run-time errors;<br/>As arrays can reach their maximum limit whilst linked lists are only limited in size by the specifications of the CPU;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>There were many full marks gained for this algorithm with students making correct use of the various stack methods.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most alluded to the memory issues regarding the two data structures, but too many dealt incorrectly with issues dealing with traversing the structures, which were largely irrelevant as access was only needed for the top of the stack.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "20N.2.HL.TZ0.17",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> feature of a word processor that could reduce the amount of typing required when writing letters.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the purpose of technical documentation provided with software.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Auto-correct/short sequences/codes;<br/>can be used to represent longer phrases;</p>\n<p>Mail merge;<br/>allows production of many letters by only typing the (body of a) letter once;</p>\n<p>Voice recognition;<br/>allows easy entry of text;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Technical documentation explains how to install software;<br/>Technical documentation describes the hardware configuration/operating system needed(to install this software);</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were generally able to name or describe a feature of a word processor that could reduce the amount of typing required when writing letters. Candidates who named a feature and described it achieved both marks.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A small number of candidates recognised the role of technical documentation in the installation of software or in identifying the hardware requirements of a program. However, many answers were seen in which candidates described user documentation.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "20N.1.SL.TZ0.1",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A school has 100 students. All student names (strings) and student ID numbers (five-digit integers) are held in two separate one-dimensional arrays named <code>SID</code> and <code>SNAMES</code>.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">For example, student Pia Baranger has ID number 11876.</p>\n<p style=\"text-align: left;\">A binary search algorithm is not used to find a particular name in array <code>SNAMES</code>.</p>\n</div><div class=\"specification\">\n<p>The school offers its sporting program to students and has a basketball team, a tennis team and a football team. Each student must choose at least one of these three sports.</p>\n<p>Three collections, <code>BASKETBALL, TENNIS</code> and <code>FOOTBALL</code>, are created. When a student chooses a sporting activity, their ID number is added to the appropriate collection.</p>\n<p>For example:</p>\n<pre>BASKETBALL={10011, 11876, 10122}<br/>TENNIS={10011, 11876, 10002}<br/>FOOTBALL={10011, 10002, 22103, 32000}</pre>\n<p>The method <code>isIn(X, COL)</code> is available, where:</p>\n<ul>\n<li><code>X </code>is a five-digit integer representing an ID number</li>\n<li><code>COL</code> is a collection that holds student ID numbers.</li>\n</ul>\n<p>The method <code>isIn(X, COL)</code> returns <code>True</code> if the ID number <code>X</code> is in the collection <code>COL; False</code> otherwise.</p>\n<p>For example:</p>\n<p><code>isIn(11876, BASKETBALL)</code> returns <code>True</code> <br/><code>isIn(11876, FOOTBALL)</code> returns <code>False </code></p>\n</div><div class=\"specification\">\n<p>The football and tennis training sessions are held at the same time. The football coach would like to know how many students will not be able to attend the football training session because they will be attending the tennis training session.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the reason for not using a binary search.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm in pseudocode for the method <code>isIn(X, COL)</code>.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm in pseudocode that will output the number of students who have chosen both tennis and football. The method <code>isIn()</code> should be used in your answer.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The school coordinator would like to check whether there are students who have not yet chosen any one of the three sports.<br/><br/>Construct an algorithm in pseudocode that will output the names of students who have not yet chosen any one of the three sports. An appropriate message should be displayed if every student has chosen a sport.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>Because the array SNAMES is not sorted/ordered;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for initializing and returning/outputting flag;</em><br/><em>Award <strong>[1]</strong> for a while loop;</em><br/><em>Award <strong>[1]</strong> for correct comparison and updating flag;</em><br/><em>Award <strong>[1]</strong> for the correct use of collection methods (resetNext(), hasNext() and getNext());</em></p>\n<p>Example 1:</p>\n<p><code>isIn(X,COL)</code><br/><code>   PRESENT=False</code><br/><code>   COL.resetNext()</code><br/><code>   loop while COL.hasNext()and not PRESENT</code><br/><code>     Y= COL.getNext()</code><br/><code>      if X== Y</code><br/><code>         then PRESENT=True</code><br/><code>      end if</code><br/><code>   end loop</code><br/><code>   return PRESENT</code><br/><code>end isIn</code></p>\n<p>Example 2:</p>\n<p><code>isIn(X,COL)</code><br/><code>   P=0</code><br/><code>   COL.resetNext()</code><br/><code>   loop while COL.hasNext()</code><br/><code>      if X== COL.getNext()</code><br/><code>         then P=1</code><br/><code>      end if</code><br/><code>   end loop</code><br/><code>   if P==1                              // NOTE: instead of if statement only</code><br/><code>     then return True                   // the statement : return P==1</code><br/><code>     else return False                  // may be written</code><br/><code>   end if</code><br/><code>end isIn</code></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for initializing, updating and returning/outputting COUNT;</em><br/><em>Award <strong>[1]</strong> for a while loop;</em><br/><em>Award <strong>[1]</strong> for calling method <code>isIn()</code>;</em><br/><em>Award <strong>[1]</strong> for correct use of collection methods (<code>resetNext(), hasNext()</code> and <code>getNext()</code>;</em></p>\n<p>Example 1 (loop through collection FOOTBALL):</p>\n<pre>COUNT=0<br/>FOOTBALL.resetNext()<br/>loop while FOOTBALL.hasNext()<br/>   X== FOOTBALL.getNext()<br/>   if isIn(X,TENNIS)<br/>      then COUNT=COUNT+1<br/>   end if<br/>end loop<br/>output COUNT</pre>\n<p>Example 2 (loop through collection TENNIS):</p>\n<pre>COUNT=0<br/>TENNIS.resetNext()<br/>loop while TENNIS.hasNext()<br/>   if isIn(TENNIS.getNext(),FOOTBALL)<br/>      then COUNT=COUNT+1<br/>   end if<br/>end loop<br/>output COUNT</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[7 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for the use of flag;</em><br/><em>Award <strong>[1]</strong> for looping through the array SID;</em><br/><em>Award <strong>[2]</strong> for correct use of logical operators not/and/or in if statement, 1 mark for minor error;</em><br/><em>Award <strong>[1]</strong> for the correct call of method <code>isIn()</code>;</em><br/><em>Award <strong>[1]</strong> for outputting the name from the array SNAMES within the loop;</em><br/><em>Award <strong>[1]</strong> for outputting message(if needed) after the loop;</em></p>\n<p>Example 1:</p>\n<pre>FLAG=True<br/>loop for K=0 to 99<br/>  if <strong>not</strong>( isIn(SID[K],FOOTBALL)or isIn(SID[K],BASKETBALL) <strong>or</strong> isIn(SID[K],TENNIS))<br/>     then<br/>       output SNAME[K]<br/>       FLAG=False<br/>   end if<br/>end loop<br/>if FLAG<br/>   output 'Each of the students is on at least one of the three teams'<br/>end if</pre>\n<p><em><strong>Note</strong>: the condition in if statement could be written as</em><br/><em><strong>not</strong>(isIn(SID[K],FOOTBALL))<strong>and not</strong>(isIn(SID[K],BASKETBALL))<strong>and not</strong>(isIn(SID[K],TENNIS))</em></p>\n<p>Example 2:</p>\n<p><em>Award <strong>[1]</strong> for the use of flag</em><br/><em>Award <strong>[1]</strong> for looping through the array SID</em><br/><em>Award <strong>[1]</strong> for nested if statements</em><br/><em>Award <strong>[1]</strong> for correct logical expressions in each of these ifs</em><br/><em>Award <strong>[1]</strong> for the correct call of method isIn()</em><br/><em>Award <strong>[1]</strong> for outputting the name from the array SNAMES within the loop</em><br/><em>Award <strong>[1]</strong> for outputting message(if needed) after the loop</em></p>\n<pre>X=1<br/>loop for K=0 to 99<br/>   if <strong>not</strong>(isIn(SID[K],FOOTBALL))<br/>     then if <strong>not</strong> (isIn(SID[K],BASKETBALL))<br/>          then if <strong>not</strong>(isIn(SID[K],TENNIS))<br/>                then<br/>                 output SNAME[K]<br/>                 X=0<br/>               end if<br/>          end if<br/>   end if<br/>end loop<br/>if X==1<br/>   output 'Every student has chosen a sport'<br/>end if</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates recognised that a binary search would only work with sorted data.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were required to produce an algorithm for the method described in the question. Most candidates achieved some marks for this algorithm, but full marks were rare. The scenario involved the use of a collection, so collection methods were expected within the algorithm. Many candidates failed to demonstrate the use of collection methods.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were required to construct a second algorithm involving the use of the method defined in part b. As with part b, the use of collection methods was also expected, but was often missing. Candidates who attempted the question, however, generally achieved some marks.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A final algorithm was required based on the question scenario. Some candidates did not attempt this question. A small number of candidates achieved high marks by demonstrating good programming skills. However, a large number of candidates scored low marks for the construction of a partial solution or a solution that was partially correct.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.1.SL.TZ0.9",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The World Wide Web can be divided into three categories: the surface web, the dark web and the deep web.</p>\n</div><div class=\"specification\">\n<p>The dark web is only accessible by using specialist software, such as TOR and I2P. Many users of the dark web use it to protect their anonymity.</p>\n</div><div class=\"specification\">\n<p>Many users of the dark web use peer-2-peer (P2P) networks for activities like torrent streaming. This opens up ports on the computer to upload and download data.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between the surface web and the deep web.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how a user’s anonymity can be maintained while accessing the dark web.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why users have concerns about opening up ports to upload and download data.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The founders of the World Wide Web intended it to be a decentralized and democratic environment.</p>\n<p>To what extent have the aspirations of the founders of the World Wide Web been met?</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The surface web is the part of the web that can be reached by a search engine whereas the deep web cannot;<br/>The deep web may consist of dynamic content such as the result of database queries or may be protected proprietary content;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The dark web uses a layered encryption system;<br/>Data is routed through a large number of intermediate servers;<br/>Which means it is almost impossible to decrypt the information layer by layer;<br/>With the result that the user’s details are practically untraceable, and their anonymity can be maintained;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>A port is used to facilitate the communication between a computer and an application;<br/>Certain ports such as Port 21 (FTP), 23 (Telnet) and 80 (HTTP) are reserved;<br/>Every time a port is opened on a computer it provides access to that computer;<br/>This means that the security of that computer may be potentially compromised every time a new port is opened;<br/>Or port conflicts may occur when more than one application tries to use a specified port;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>World Wide Web has enabled citizens to communicate easily and for ‘ordinary’ citizens to express their opinions;<br/>Therefore, the ability to publish is not confined to certain ‘privileged’ groups such as broadcasters and journalists;<br/>The World Wide Web has to a large degree given access to common resources to all citizens globally who have access to the Internet;<br/>However, it can be argued that the evolution of the World Wide Web has led to a greater centralization of power, for example the digital oligarchs (Microsoft, Google, Amazon, Apple, Facebook);<br/>This centralization has led to a reduction in democracy as the digital oligarchs have an increasing stranglehold over the lives of their ‘digital subjects’ through the aggregation, analysis and monetarization of their data;<br/>There are still issues with a lack of digital democracy, for example, many citizens may not have access to the World Wide Web, either through income, geography or necessary skills;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Dark web and deep web are clearly concepts that engage the interest of our students. Search engines were correctly brought into the answers to part (a) whilst various ruses, some of which were correct, were present in the answers to part (b).</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Being an \"explain\" question the answers required knowledge of \"how\" or \"why\". Not that many explained the importance of the translation feature of a browser.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students identified the dangers of opening these ports to potential hackers with the consequent dangers.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In a 6-mark discussion question, the examiner will look for six different ideas that are both relevant to the question and reasonably well-explained. Students need to bear this in mind when planning the way in which they are going to answer. Short phrases or bullet points are not appropriate.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.9",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-4-the-evolving-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A telecommunication company stores a large amount of data in three databases.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>The database segmentation is carried out on the CUSTOMERS database.</p>\n</div><div class=\"specification\">\n<p>Data mining is used to extract knowledge hidden in this large amount of data. Before using data mining processes the existing data should be cleaned up.</p>\n</div><div class=\"specification\">\n<p>Customers committing fraud is a risk to the company.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of an object-oriented database as opposed to a relational database.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define a <em>spatial database</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State what is meant by database segmentation.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> benefit of database segmentation to the telecommunication company.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how ETL processes could be used in data preparation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between the use of association and sequential patterns as data mining techniques.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how deviation detection could be used to detect fraud at the telecommunications company.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Customers who decide to leave the telecommunication company for a competitor may result in huge losses for the telecommunications company.</p>\n<p>Explain with the use of an example, how predictive modelling could be used to optimize information sent to existing customers.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Object-oriented databases store objects rather than data such as integers, strings or real numbers;<br/>Objects are used in object-oriented languages such as C++, Java, and others;<br/>Objects consist of attributes (data which defines the characteristics of an object) and methods (procedures or functions which define the behaviour of an object) / the data is closely related to the programs that process them;<br/>Which may be a more accurate representation of the real-world objects they represent;</p>\n<p>Relational databases store data such as integers, strings or real numbers in tables / uses tables with rows and columns);<br/>Relational database is normalized so data is not repeated more often than necessary;<br/>All columns depend on a primary key (a unique value in the column) to identify the column;<br/>Once the specific column is identified, data from one or more rows associated with that column may be obtained or changed;<br/>There is a distinct separation between the storage of data and any application using them;</p>\n<p><em>Disadvantages of object-oriented database over relational database</em><br/>Lower efficiency when data is simple and relationships are simple;<br/>Relational tables are simpler;<br/>More user tools exist for relational databases;<br/>Standards and support for relational databases are more stable so changes in database are less likely to be required;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>A spatial database stores</em>:<br/>Spatial data / locational data / geometric data types;<br/>Such as point, polygons, lines, 3D shapes, coordinates etc.; <em>//</em> <em>allow any one of these</em>;<br/>That model the structure of geometric/3D objects;<br/>And make use of spatial indexes to access data;<br/>And supports geometric functions;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Database segmentation means dividing entities into groups with similar characteristics/common attributes;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying a benefit and <strong>[1]</strong> for a further explanation, up to <strong>[2 max]</strong></em>.</p>\n<p><em><strong>Benefit</strong></em><br/>Increased profit;<br/>Better reputation;<br/>Increased number of customers;<br/>Better opportunities for growth;<br/>To gain a larger share of the market;</p>\n<p><em><strong>Explanation</strong></em><br/>By studying the groups and behaviour of the customers in the group (for example, average call time of customers younger than 30 or which services are purchased by customers older than 60, etc.);<br/>The company will better understand customers and serve them better;<br/>Can better decide which marketing actions should be taken;<br/>Can better predict which group of customers new products/services should be offered to;<br/>Can better decide which new products/services are needed;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Useful data should be discovered then <strong>extracted</strong> from various sources;<br/><strong>Transformations</strong> performed;<br/>Repair inaccuracy between data formats;<br/>Remove mistakes/spelling mistakes;<br/>Delete unnecessary data/data fields / clean up the data;<br/>(<em>accept other examples</em>)<br/><strong>Load</strong> data into data warehouse;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Association is a (rule based) data mining technique which searches for relationships between data items;<br/>In the same transaction;<br/>Sequential patterns is a technique that seeks to discover/identify similar patterns in transaction data;<br/>Over a period of time;</p>\n<p><em><strong>Note</strong>: Accept examples in given situation</em>.<br/>Association could find out that customers who buy a new mobile phone also change the subscription type;<br/>Sequential patterns could discover that twice a year number and duration of calls increase (e.g., customers’ birthday and New Year’s Eve);</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for creating a model and <strong>[1]</strong> for detecting changes/differences in the data/model from previously measured values, up to <strong>[2 max]</strong></em>.</p>\n<p><em><strong>Example answer</strong></em><br/>The customer call records and calling behaviour (other activities) could be summarized to obtain their calling pattern;<br/>The (previous) calling pattern could be compared with the customers’ recent call/activity;<br/>In case of significant changes/differences the activity is suspicious/fraud detected;</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for creating a statistical model, <strong>[1]</strong> for analysing the model and forecasting probabilities/trends and <strong>[1]</strong> for an appropriate example in the given situation</em>.</p>\n<p><em><strong>Example answer 1</strong></em><br/>Customer’s online behaviour pattern could be created and considered;<br/>Customers who spend less time logged on may be less likely to renew their annual subscription;<br/>In this case, the ability to keep the customer can be increased by offering new/better/cheaper services and products;</p>\n<p><em><strong>Example answer 2</strong></em><br/>Customer’s calling pattern could be created and analysed;<br/>If, for example, the customer has high monthly usage of services then the customer will probably renew his annual subscription;<br/>(No actions made by the company needed);</p>\n<p>If a customer is younger than 20, may spend more time exploring new service features, and the customer is at a greater risk to cancel the contract/the customer will probably not renew annual subscription;<br/>(Actions made by the company might be needed, for example: offering new/better services and products;)</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">g.</div>\n</div>",
    "question_id": "17N.2.HL.TZ0.4",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-4-further-database-models-and-database-analysis"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An airline has a server that holds the flight database. Passengers can check in using a number of self-service client kiosks located in the airport.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>client</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>server</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the functions performed by the server in this situation.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>A client is a simple application or a whole system that accesses services being provided by a (remote) computer/server;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>A server is a computer / software which manages access to a centralized resource or service in a network.<br/>A server is a computer/software that provides data to other computers/clients in a network;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p>the central computer receives requests from the kiosks/terminals at the check-in desks/mobile phones;<br/>can manage several clients simultaneously (in order, each providing a different set of services);<br/>makes sure that the request is valid by accessing its database;<br/>processes the request;<br/>sends information to the kiosk;<br/>all requests/information are sent over the network connection;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of candidates recognised a <em>client</em> as a system that accesses the services of a <em>server</em>.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of candidates recognised a <em>server</em> as a computer that provides data to a <em>client</em>.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates failed to understand that answers were required to explain the functions of the server in an airline's system that holds its flight database. Some candidates recognised that the server receives requests from clients, processes requests, accesses the database, or works over the network. However, most candidates only described one or two of these points and rarely stated where the requests came from i.e. the kiosks, check-in desks, or mobile phones.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "20N.1.SL.TZ0.2",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following binary tree.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the result of inorder traversal of this binary tree.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the result of preorder traversal of this binary tree.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>B, A, D, C;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>A, B, C, D;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Straightforward question which caused few problems. Some candidates confused in-order with pre-order traversal.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Straightforward question which caused few problems. Some candidates confused in-order with pre-order traversal.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "20N.1.HL.TZ0.2",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Compare direct changeover with parallel running as a method of implementation.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>there is no extra cost;<br/>for running two systems/for extra salaries (no need to increase the number of employees);</p>\n<p>benefits can be gained immediately;<br/>because new system is better than the old;</p>\n<p>if there is a bug in the new system;<br/>there is not a second system to fall back on/could be disastrous for the company;</p>\n<p>employees will need to be capable of using the new system immediately;<br/>without training/with training in advance but not on the system;</p>\n<p><em>Mark as 2 and 2</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A large number of candidates scored full or nearly full marks for this question because they were able to describe the different benefits and drawbacks of the direct changeover and parallel running methods of implementation. If marks were lost, it was most often because candidates gave a description to show why one method may be better than the other, but then gave the reverse argument, as to why the other system may not be as good as the first, rather than offering two distinct arguments. However, many good and comprehensive answers were seen.</p>\n</div>",
    "question_id": "20N.1.SL.TZ0.3",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Describe how an operating system uses paging when running a program.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em></p>\n<p>memory management method that uses secondary memory to increase the amount of primary memory;<br/>transfers data blocks of the same size (“pages”);<br/>from secondary storage to main storage when they are required;<br/>and returns them to secondary storage when they are not;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Not all candidates answered this question correctly, but most of those who did, scored good marks.</p>\n</div>",
    "question_id": "20N.1.HL.TZ0.3",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline <strong>one</strong> feature of autonomous agents.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max] </strong></em><br/>Reactive behavior;<br/>autonomous agent senses the environment and reacts;</p>\n<p>Autonomy;<br/>autonomous agent activates alone for a task / is not invoked for a task / selects the task itself / operates without human supervision;</p>\n<p>Persistence;<br/>autonomous agent is a programmed device and the software describing an agent runs continuously;</p>\n<p>Sociality;<br/>autonomous agent can interact with other agents through communication / it can work in coordination and cooperation with other agents;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates were able to outline one feature of autonomous agents. Only a few candidates did not attempt this question.</p>\n</div>",
    "question_id": "20N.1.HL.TZ0.5",
    "topics": [
      "topic-7-control"
    ],
    "subtopics": [
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A company produces and sells domestic floor-cleaning robots.</p>\n<p>The floor-cleaning robots can clean different surfaces like wood and carpet. The floor‑cleaning robots can also avoid obstacles or stairs.</p>\n<p>Sensors are used by the processor that controls the floor-cleaning robot so that it can move safely.</p>\n</div><div class=\"specification\">\n<p>A computerized security system for the company’s headquarters protects against unauthorized access using a swipe-card system. Each door has a swipe-card reader that is connected to the central computer. A database stores the IDs of all employees and the rooms they are allowed to access.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> types of sensors used in the floor-cleaning robots.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the function of an output transducer in this situation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> alternative computerized method that could be used in place of the swipe-card readers.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how the method identified in (c)(i) functions.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare polling <strong>and</strong> interrupts as mechanisms for the swipe-card readers to interact with the central computer.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>Proximity sensors/ range sensors;<br/>Which are used to determine how close an object is to the sensor;</p>\n<p>Optical sensors /Photocells and other photometric devices (often used in conjunction to proximity sensors);<br/>which are used to detect the presence or absence of objects;</p>\n<p>Tactile sensors / Contact sensors / Bumpers;<br/>which are used to determine whether contact is made between sensor and another object;</p>\n<p>Touch sensors;<br/>which indicate when contact is made;</p>\n<p>Force sensors;<br/>which indicate the magnitude of the force with the object;</p>\n<p>Machine vision;<br/>which is used in robotics for inspection / parts identification / guidance (accept other uses);</p>\n<p><em>Mark as 2 and 2</em></p>\n<p><em>Award marks for other miscellaneous category of sensors which also are used in robots. For example, devices for detecting / measuring temperature / fluid pressure / fluid flow / electrical voltage / other physical properties/</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/>Output transducer is a device that accepts a (digital) signal from processor;<br/>and turns it into a physical movement;<br/>to make the floor cleaning robot move in different direction;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>Any biometric device (finger print, eye scanner);<br/>pin pads/Key pads on doors;<br/>smartphone access(cloud-based) / mobile access control;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/><em>The answer to partc (ii) should match the candidate’s answer to part (i). For example</em>:<br/>a camera is used to scan the iris / finger print is scanned;<br/>a database stores images (scanned iris/finger print of each employee) and the rooms they are allowed access to;<br/>computer compares the scanned image to images stored in the database;<br/>if found, the employee is allowed to enter;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>Polling requires the computer to actively interact with each swipe device;<br/>the computer periodically checks each swipe device whether it has requested service, if it does not require servicing, the computer examines the next one, etc. If one of them requires servicing, the computer switches to running the serving program;<br/>polling will waste processing time whilst the device is idle;</p>\n<p>whilst:<br/>interrupt requires the device to flag the computer;<br/>processor receives interruption signal and services the interrupt by calling the appropriate system subroutine for interrupt processing/interrupt handler;<br/>and hence the computer’s time is not wasted whilst the swipe device is idle;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly a well answered question.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly gave good responses for this question. Some candidates confused output transducers with sensors.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Almost all were able to identify one alternative method and to give a good description.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Almost all were able to identify one alternative method and to give a good description.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>It is evident that students generally have problems with compare questions, describing the issues individually without performing any comparison, as was the case here.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.1.HL.TZ0.11",
    "topics": [
      "topic-7-control",
      "topic-6-resource-management"
    ],
    "subtopics": [
      "topic-7-1-control",
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>one</strong> difference between stack and queue data structures.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the purpose of the following queue method:</p>\n<p><code>enqueue()</code></p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the purpose of the following queue method:</p>\n<p><code>dequeue()</code></p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the purpose of the following queue method:</p>\n<p><code>isEmpty()</code></p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Assume that the queue Q holds the following data:</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWMAAABdCAYAAABwxQeIAAAMa0lEQVR4nO3df3BU5b3H8bc7HS7DbGMrtEaokJ9NBQI1yAja4celRSLVAP4aaSihBLCVekULXiQ/4BJBFElQfpRqIZEI0oshqXNFErwsDBolmCka1LhJE+BCgg0Rk52E5t6cc//YJCUgaHGT82T5vGZ2Jjnn7Mn32Tz7OWef8+zuNbZt24iIiKNcThcgIiIKYxERIyiMRUQMoDAWETGAwlhExAAKYxERAyiMRUQMoDAWETGAwlhExAAKYxERA3zrciujwsK7qw4RkaBWUV112fWXDeOvs4OeLiosPKjbqPb1fMHeRrXPT8MUIiIGUBiLiBhAYSwiYgCFsYiIARTGIiIGUBiLiBhAYSwiYgCFsYiIARTGIiIGUBiLiBjAuDC2TuXzm8FjSfXUOV1KgFj4vB5yUhKICgv33wbPIestLz6nSwsgq7aE3I42DmFyyjZKa1ucLqsLnKU08x6GpXhocLqUgLHweYvISh7d9v8bzdzMIrw+y+nCAsbyZjO1/fl3/i0hG68hzTQrjK3jvL58FYVNThcSONapP7MoYTFvf//fOfjXKiqqyzmYM4KPfvtLFuUfx5B+8I1Yp/KZH7+Z1hlbqaiuoqIsj7ns4P5524zp6IHRQPnW1Sx/4xOnCwkofx9dx5mfZ/OX6ioqyrKZfGYd8fetpzQoAtnCd7KKyuHp7P5rlb+Ptt8Kkog2JAUNKQOggfKXV7K8+rtEOl1KwJyjsnAnhUxketKthLoAehE6MomFiyIpfPKPHGjo6Z29jgMbMjk4+QGmxoT4F7ljSFi0gAe9W3j5QHC8wvGf+aez+0eTmdLH6WoCqe3/N20BC6fE4AZwx3DXzKnEfryTXYfrnS4wAOop3euB2DCuNyjxLvSVn9rWPSx8pTk89kxvlrxwNy/P3u50QQHSm+ikXCqSLrG6qZJjp1sgpHe3VhVQDWW8lQcJG4YScv7ykHEs/2i/U1UFllXO1nlPU7XgBdJGNLLV6XoCqh/jMvbzgdNldCXrDMc+bCRy6g3+g42hzDhO+N7npSU7GbRiAXcN7MHB9M8a/lNui+zZ7bVOV1PWdCNRIafwZKcwORjHHF0DuGNTDkvH9TfkCdO1rNpi1q/aRGX8An49pp/T5XxzvhoqvP/C9/62k4cGn3fdJr+UWoO6qAF96yylL66haOJqnpky0ISCupx16k0yn6lk4qx/JbJHN7htLI4T/GnJSjxhv+H16ioqqvfw+HfzeHDhnzllUGe/cm5CQ00+pwoQq5ychCH8cNR01r4zlDm/GtU2tNaz+U8Yvk//kbP4/UdtY8WHfkf44aeYvbbEmAvpDj/ULZzKX0ZS4W0snzPC6JcQAeM7yta0TI7dl0Ha3cFy8Kmj1wNLSes4cwwhZtoDxO/PZGOQjBlfFVwxzCw46r/IvCGMN++byvwguMjsik5iV3VB51c27mjG/3QoJ9au5TXvOSfL6+BoFlin3iDjyeP86qkZxLmDI5Yuy3eUnEeTeY45rFk4PijOOvz6MTSsb+fO5L6BqOhGyqrP9Pgn89WnF6Hj5rEwsReFW/6byqD8B7pwDwgnkhNUnDTj3NjBC3htMw2aSimcdjPrLlj7YdJItg9PZ/cuc6aefBNWbTHrUx7jRR7mP7OmExMUBx8XIXHjSejjcboQ6SreKk76LKJDgqG/ms3BR7htpkF153l/nxalE8tAHswuMWoO4JVrodbzFHeNSubN0Cd4LWiCuI07glvGtlCwt6zzmyB8NVR4wxk77PogGYoJUm3jxJd6E0ufaeOJ69FBfA5vdiJRg9PxdJpG2na9o884JsRd51h15+vJj3IPYOEr3cTspJc4Ef8UL/7HFKKDKYgBXD9gwpzp3Jj7AptLz/qXWbUczn6F3WNncP+Pv+NsfXJ5rmjuSX+IG/N2sKu8PY5bqPVs4tm8wTw+I67zlMUepzfR9/4b8wd52Jb38T8u1vk+ZlduCT9ZMZsxhhxszKgiWFleXlv2e8qBpt0LGBNx4dsxg+Ft3y7ccQ+zo+gh2BDvb1fEVP7Qej/bn72b/uphhnPhjpvHH3Pv4PNn72jrlzcze+8AFhasYmZMz45iANwjeWRLJpM/z+K29ufefdshca1RM7iusW3bvtTKYP8KbQj+Nqp9PV+wt1Ht8zPloCAiclVTGIuIGEBhLCJiAIWxiIgBFMYiIgZQGIuIGEBhLCJiAIWxiIgBFMYiIgZQGIuIGOAr3w4tIiLf3Fe9JfqyYdzdUpYs4dprr2XhokVOlyIiQcjr9RL/s4nsf/sgAwYMcLqcTowZpvB6vbz6yjY2bdiI1+t1uhwRCUI52dkAbNywweFKLmZMGKelpHT8nJWZ6WAlIhKMiouLefWVbQC8+so2jhw54nBFnRkRxnv37qXkvUMdv+95YzfFxcUOViQiwaS5uZklixd3Wvb0ihUOVfPlHA/j5uZmVmRkXLR8yeLFNDc3O1CRiASbwj17OF59rNOykvcOUZCf71BFF3M8jHOycy56kACOVx+jcM8eByoSkWBSX1/P448u+NJ1a7OyjDnpc3w2xfkX6+J/NpHdRYWd1kdHR3d3SSISROrr6zlz5kzH7xfmTN++fbnuOue/lNTxMD5fsH/9iog4z9SccXyYQkREFMYiIkZQGIuIGEBhLCJiAIWxiIgBFMYiIgZQGIuIGEBhLCJiAIWxiIgBFMYiIgZQGIuIGEBhLCJiAIWxiIgBFMYiIgYIfBj7yilISSAqLJyosERyvOe+9l2v7GPtLHzeIrLSd+C1ruDuInJVMfHjMyHgYXwO787lPJ4XwXPvlFNRncvM6N6B/RMXqedwTgbr/mLGp/WLiFyJLhqmCOFa97e6ZtciIkEocGFslZOTMIL4pW9D08skD4tkWMpb/I8nnWFhCaRuXs3cweFEhY0l1VOHf3jBQ07HkMYQJqdk4/E2tO+QBk86w8IS2eI5QG7HdqOZm1mE12cBdXhS7iE59zgcWUZ8RPu+RSS4tefDl2VLC7Wl20iNH9KWGQmkZnvaMqNdC7Wl+WQlj27bJpyo+BRyPF58X7n/LmIHVLP96ZZf2JE3pdn7vmi1bbvV/mJfmh07KMyOnb3V/qSx1W6tqbQrG//Xbvxkqz3nplH2nLXv2DWttm231tgla5Pt2EHT7Mz3P+9038hJaXb+p1/Ytm3brTV77fRJN9t3rjlkN9q2bdt/s/ctGWNH3r3F/rQ1sK0REVNdKlua7ZO7HrVjb0q2nz9UY7fatm03ltnZs0fZsQ/tsk+2+u/b+P7z9p3nb2P/3a7Zl2Hf+SX503n/XRcy3TSbYiAJiZOIcbtwhUYQ4T7L4a0v8u7YJ1g6fzShLsAVyi2PrOT5xDrWLcs/72LcQB584rckRIcA4AodzrgR36bcc5QaXbATucpdkC3Wu2x8spDIRb/j4ZGh/pf+7iHMXLOShP2ZbDxQB9RzOG8nJ6b9gpnt29CL0DH3cv/wKvZ/cBrrUvt3d11kOjO1raGMt/LqiLz1Jn8Qd3AzIOpG8FZx0neppP0624jI1ceioXQfBU39GBrWt3O4uW8gKrqOgr1lNNCPcRn7+SAjjtOe1ynIz6cgfzOpk6ex/MjfnSremTC2TldT1nSZDZoqOXa6pdvqEZFgcpztSSP/MRYcFk5UxCSWH2kPHQtfeS5zBw8nPuklDp+1gO8xIWszqcOhsqKmbdy4ezky5cF1fRhD+0DZpTboE8mg63vBye6sSkSCw+2kFr106Wm1Vjk5i1by7thMDqyfQv/2U9IGD6neJojttkI7cWaYImQoE6b1o/K9j6ntNNLg42TFCYgOZ0AXjs2ISDByERI3noQ+lbx39DM6R0sJWfFxTM0ux/LVUOHlomFSy3eWM91d8nkcSrzruGXGHEbtX8XSdcVtgdxAeXYqj+T2Y376FKK/dmVtY8gAnMPn+78uqVhEeoCQ2/j1ilEcfHI560tq/YHsK6fg6QzWMYvUe6NxtZ0MfrhjJwdq/cOhVm0x69NWUXi54dMu5lAYu3DHTGdNwUpu/+xpfhLhnz/8WMWtPF+0hUfjvvNP7Ks3kZNmkdjyLPERI5ixsxJd1hO5WvWi/5RlvLbhdj5Lm8APw8KJGprEf/WdxZ+2zCPO7QL6MWb+apaOOETyqBiiwsL5ccq7/CBxNc8lDnSs8mts27Yd++siIgLoU9tERIygMBYRMYDCWETEAApjEREDKIxFRAygMBYRMYDCWETEAApjEREDKIxFRAygMBYRMYDCWETEAP8PNrte0gBapegAAAAASUVORK5CYII=\"/></p>\n<p style=\"text-align:left;\">The reversed queue Q would be:</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p>Construct an algorithm in pseudocode for reversing the queue using a stack data structure.</p>\n<p>You may assume that the data in the queue is input and a new empty stack is created.<br/>Only the standard queue and stack operations are allowed.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Consider the following recursive method:</p>\n<pre>mystery(N)<br/>    if N&gt;0 then<br/>        return 3 + mystery(N-3)<br/>    else<br/>        return 3<br/>    end if<br/>end mystery</pre>\n<p>where <code>N</code> is an integer.</p>\n<p>Determine the value of <code>mystery(7)</code>. Show all your working.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> disadvantage of solving problems recursively.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em><br/>In a stack, the same end is used to insert and delete the elements;<br/>whilst two different ends are used in the queue to insert and delete the elements;</p>\n<p>Stack has only one open end so only one pointer is used to refer to the top of the stack;<br/>but queue has two open ends and two pointers should be used (to refer front and the rear end of the queue);</p>\n<p>Queue is a first-in-first-out (FIFO) data structure;<br/>and stack is last-in-first-out (LIFO) data structure;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>(<code>enqueue</code>) adds an item to rear/tail of the queue;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>(<code>dequeue</code>) removes an item from front/head of the queue;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>(<code>isEmpty</code>)<br/>checks if the queue is empty or not;<br/>returns <code>TRUE</code> if the queue is empty, <code>FALSE</code> otherwise;</p>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max] </strong></em><br/><em>Award <strong>[1]</strong> for a while loop (through queue);</em><br/><em>Award <strong>[1]</strong> for placing each item removed from the beginning of the queue to the top of the stack;</em><br/><em>Award <strong>[1]</strong> for a while loop (through stack);</em><br/><em>Award <strong>[1]</strong> for adding each element popped from the stack to the end of the queue;</em><br/><em>Award <strong>[1]</strong> for the correct use of stack and queue methods;</em></p>\n<p>Example:</p>\n<pre>//S is a new/created empty stack<br/>                     // push all of the elements of Q to stack<br/>while not Q.isEmpty()<br/>        X=Q.dequeue()<br/>        S.push(X)         // <strong>OR</strong> S.push(Q.dequeue())<br/>end while<br/><br/>                    //then, pop each element from the stack and add it to Q<br/><br/>while not S.isEmpty()<br/>        X=S.pop() <br/>        Q.enqueue(X);      //<strong>OR</strong> Q.enqueue(S.pop())<br/>end while</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em></p>\n<pre>mystery(7)= 3 + mystery(4);<br/>          = 3 +3 + mystery(1) ;<br/>          = 3 +3 + 3 +mystery(-2)=3+3+3+3=12  <em>;</em></pre>\n<p><em><strong>NOTE</strong>: Award only [<strong>1</strong>] if the working is not shown and only the final result is given</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]  </strong></em><br/>More difficult/complicated to code;<br/>if the data structure being processed is not recursive;</p>\n<p>It is difficult to find bugs;<br/>particularly while using global variables;</p>\n<p>Can use more memory (than iterative solution) when executed;<br/>because every recursive call increases the call stack;</p>\n<p>Recursion to a deeper level will be difficult/impossible to implement;<br/>if the call stack space on the system is limited;</p>\n<p>Slower execution / using a recursive function takes more time to execute;<br/>as compared to its non- recursive/ iterative counterpart;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Well answered question.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly gave good responses for this question.</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly gave good responses for this question.</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly gave good responses for this question.</p>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A few candidates did not attempt this question. However, those who did generally answered well. A few candidates incorrectly used 'enqueue' operations also for the stack. </p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A simple enough recursive algorithm, leading to a high number of correct answers.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "20N.1.HL.TZ0.12",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Images in computers are stored as two-dimensional arrays.</p>\n<p>A black-and-white image (<strong>Figure 1</strong>) is stored as a 10 × 10 two-dimensional array named MAT (<strong>Figure 2</strong>).</p>\n<p>Each element of MAT holds a number for a colour; 1 represents the colour black and 0 represents the colour white.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAwQAAAGKCAYAAACy3oZjAAAgAElEQVR4nOzde3hU5b3//Xdn80vp7JgHCj+JIJCYxJRTkBRaFR+IiGgEDSBCwSixjWgrBSIitiYCprYFigEKtSqWAFG2FEPiCTfH6INAC47KoTTMxAQoELpBaMieTbPpzPPHzCSTE4Qwa4bJ+ryuK9cFc/qu+a57rXt917rvNd9wu91uRERERETElCyhXgAREREREQkdFQQiIiIiIiamgkBERERExMRUEIiIiIiImJgKAhERERERE1NBICIiIiJiYioIRERERERMTAWBiIiIiIiJNVsQXLTlMTAmlvjm/jKLqASoLGJKTCzxMdMorrwYvCUPpKoScnrHEj8gD9sVf4VqbHn3NZ8n39+APGwXL1JZNK1+/kLibxRnJhMfk8yUor8FL2zA2opfzi+XR0PbZ4jyeAVqt+NWte3LaHFum8qTi2r7J2zIy+a3tuqrXJBrfz2Eq7bfD7iotpewKjut9jslZeZRbKvEdUWfE479gLRW4/1qCPdB1XZKCvOYvtTGtbfl1W0XA/O8yxe2+wqfa2j7DXAudYWg+iCrnv4Za52hXhARE7n4BSvGT2bWEhv/CvWyiEm5qC59i6fTHiO3YF/to84tS5k59imW2s6FcNlEWqIa2+vTyXx6KZ9qRypXqd1lX9FxGuv2ZJHc3CujR/NaxejALlVQ1FBpW8/y519i7aGrqQYiSc76EEeW978XbSwe9CDLzvZiauE6ZiRH+r324jVyNuhG0lbYSAv1YohJGN3e1J4N1yb7ga/Zu+Z1tjnBOjyXPy6eRKL1bxQ/NYGZG238ofALfpicQlSLPisc+wEJHO2DWiws9xX+2hE9eimO0UtDvSABd/VXCJq8ZOGi2r6ZxZm3eS6rpGZTYLOzt4WXjmovx9VejvFdjruP3xQVMje1D/ExfRiTX4qr0SXfPozMzqfEXnXJxb5oW86osc+z9lA8D6WnYG3yVU1c7gogV+UeCnzLnZpNQcPL1NV2SvKzGem73JyazaoSO5ceYFGFvSSfnNQ+dZepG72v8eXNukugv6HkkP+6m0OxvQqq7WzLe5ykmFjiY9LIWbOHSt/C1q7HH7Oq5MO69zZ8XdNZaNX6q/NPTuz5PVN6+y73/5Ztl31vg/YZE0t878dZXGSrv6yuSmxFebWfHR9zG1PyNmOvbv4LuSq3etvnbUzJP3j5dbX1t36f33C4gv+lybex2Yrqlrn34/x2T+P2UreOvMta1YLTRr4hc73nUFLl/URXKavS/LczgAvY89MbPOaXqzW+ttpwvTdob5VFTIl/kGVnAQ6xbGw/v0vvDdtvGjn5JZfMecDbc6Nc+vJZhK2yppn114eR2W9h+9suFg+IJT7mPhbXDoW62jYeBoLRD/heN+Alije/5G1r6ayyX6BVOXad4cj+00AP0tLvJTHSApaufP+eQQA491dwygXh2Q/Qwv2Xb+ieX1tvtr9oov+96nVyhf1V/ma/7dK7zdXbJqkdQlP3vRvuQ4zYrzYxZMivXW+w/Yli/89YuqtBv9jSfUnDmEMYv+QQAOeWPMh3Yu7iZ9k/JikmlqTsEnyZdtnzGVNv3eC3j/d7jCrsJX/06xuvZF/VYHvv/TiLtx6m0Tsbbe/+29dWDtXLQxH26otN7Ef811EL2lmL10XjIYSXbj/+Q4Zasi1dSdtr4XFKAF3+CkEruE68y7NpWWzynXg/9CZz08sY9v2zV/nJh/j9jJnef/egb0wnaBgLJ6UF88gs/BM5G+YzObG58zvfYvD0PCZPvI+kE8vZXFBCUEcNHS9m7mN/Zpvv6sShN5k7toJ/bV7B5IT24DpK8axHmbnR71zSoTfJzdjKp3NX8HJGHyIbfaiLatsqZmS8TKn/w4feJDfjEGcL32BGcodLL9fZ5WSm+r93NTOnVbG3227WbvEtyz7W5jwD3d8hN6Wz34s/IjfjI7//72NtTga20/m8nTWoieVtoq20eP15bXmO8Vvq/uvc8jJTdv7lku9tHBNwbmHZjAMc5m2Wje6BhXPYljzF+CU2vxdVsm3JFHYfzuOj5aPp2qCcdlVu5cXHplFwCBKnL21mHfnUcKJoDg/OKKL+Yixl5s6jsGU+af4BGnxPnFtY8tD/ENVse/Es67Zm4/uJ6stdY3uwtqCErbafkpLSGVfZLoq+9CzZ/h2H+HtGItGc5i87/gJ0pm9MJ7+zCQ4+eGEK27Yc8v7fs94P/KuQdzISr+CsQ1M52cfauY9RvMN7BjfyCs9htKY9N7XtUcm2JVnsPox33TdcVielBc+TcXw4A/9ZfxGuuo2HKcP6gbMrmPm499/WOHp2iWhdji2JTC4+yOR6D1Zzovy456P7xdDF6EG1hvQDQIv2X94hU2Ny2Oa/Ezr0JrkZG1g3veF+u3H/W5ueVq2TyCvsr86yba7/Ps2zzY3/7Azr/vgUyZEWzxDgGZnkbvHfdj37kLVvP+19nd9TRu5Xa73HrLHv+f2/km0vZ/JfUb79Y8v3JZf3TbrceS9phR+xtnA7tmeHkBJVQ9mnW9gPwF/49OBpJifcCH8/xKdfOmvXF1RRmj+bh+Z+5Lf/9a6zgveYepnjh0br27mFZT/a0uzrm3JuSSb314v9PDMu7qbbu2/XtdFDbzI3/d+4cfccUqIsV7jtX2ZdVH/GimlPsazeqJF9rJ37FLazzR/H1A4/bPG2xGXbXsuOUwLr8p93dinj4xtOjrrUBIYLlG1a7/kSvTJZsbsUR0UpO37Xl+PbA3ChtNfTrDtQhuOrjcz+notPfpfHJmc0w+a+zxcV5TgqvuSDufdidX7EojW2xtWpV7vkJ1mSNZrk6IhLBPNeBq4oZ29WcmCrp0Nf0+WJQr6oKOfw7hWk97Li21jBRdUnb5CzsRLr8Fw+OFCGo6KML/4zl2HWSrYtWMfeqqZKRCeHP95IKWBNX4mtohxHxeesm54M2Fg2rwj7ZStLXy7L+KLwaRIBDhVRzFOe5Tiwjqm9rMBRNn5+tMHZMiuJk1ew46tyHF/t4j+eHo4VJ6Wvv9/M8p5u9fqrk0R6/qcc9s/jJd/ra59WEqev88T86mMWpUYDlfy5/DQuwGUvIneJrcnPd27M45VPTtf/2HOfsyY721MMTF7KG9Ob23F4ucrZvHITTpKZWvg5jopyDu/MY4QVcJZS3vCsF0lMXPyRp734XtdEe6m/vG8xfXj0ZTMI3yZ5eApWjlK85QBVXOTvB23eDgTY+Tl/rXJBlYO9O8+CNYW7kr/t9/4jHI+ewsYDZTi++pQVk5MAp6eQaCpc9Ghec7zD1I4AvZhauB/H51kkO3fyys+LcFrvJec/v8RRUY7jwPvkDI/GueV1CvZ+3YLv0ijYFbdnV9k28jdW1u1rKkr5ZPForIBzd7nn7Ezt+vNv85+ytMff+aTemYVAtPFrwDXVD9Rtu4f/PIvvRX0doBzXUFmynJwlNiCZH469xTtcKNz6gRbuv1x23nn2V7VDpj7wtvUd+Zkk4qR0ya9Z0XAeRb3+138f0Jp10or+yr8t/XE2w6zAofVs2Ps1cAH7+l95igG/fUhtXg/9npzXP2twZcXI/apfbtLzvPtHX1/jt39s8b6koRtJW/EJ66b3AqDD9Hf4a8WHzLhrMHeN7QHOErbavobaEzkAZ9m910EVLqr++jm7AevYO0mOsuCyF/Lc3I9w4rfOavfnNpY9vwZbs1dqffu5ZtZRi1PlW2++duCk9D8+hmcb7MOde/jC4eTK96+XXhcXD++g4JATrI+yYp93eyt82rs9LOGd2ispDbRmW7pk22vZcUqgBf78h+sIOzd8DljpN2EcQ6IjgAiiU55gVnqPq/xwK/0mjOCWSAtYrERG/I0vPjgKVLJt7ihuiYklPqY/I70VrrNwO7Zmdpgh138Mjz6QSCRgie5Pynf9z7Q7cXy+x/MdtuQwsm8c8TFx3HKPt/qs3dAbiiA6NtFz4FLwGHdk5rGhqIRj8c97djLFGSRcbo1bRzBpbC8isRCZNJi7O0K9S+qRvRgyomczb76LKT8eSrQFsEQzMONh0qz4bbwNXDx69etv+GM8mdIVT8g7eXr2OM/3/+BzHE0eq7QnIaMAR8XnvDH0DFuLCln1wk9rzwDVnKnC6X9AXO/z72LuxoM4Kj5ucGXkLNvmTvN2RCOY8sT/68nBpXjPTjq+Ws6Q4yUUF/2BOT963ns24L85U9Vgx+PfXroO5J7bO/o9WcOpijLPWZ16y/t9JqePaGY4XL2FIeo7A7gV3zCJc/x175dAL4YN7+Vdf9VU2bZT7KzrQOoMYPzke0mItIAlmuQ7b2lBzMYuOj5noxNwfkTuPf09B519R3nP9vmKlSvUivZsSchgQ0U5h1cO5sSWdynOf5HHfWfv/nmOKqfL7wrKAManD/a2+a6kzMxiov+XD0QbD0eG9gMDGD+qn2dbiIzEGpAc11BZspAfZaygFCuJ058j83JXUwPBkH6gZfuvujY8mJmzx3mvvvmvIxsFH3/ld9KnQf9rbbAPuOJ10u4K+6uODHtiMim+tjRoHJPG9qC2mPdvc8/O4BHvmeG6fsFJ6eodHPbvFwzdr/rlpnb/WDckzafF+5IW68B3BvYHTnOg4gwu34mcXikM62X19o1fY9tSgpMepA3vSxQX6q4i9H+CWY96rzz5L8ehjXxyuJkKpXb4XXPrqGWsYycwJjEK6EDS0DvoAM3sw72ueNu/9LqwRMfyPSvgXE3mrU+wuPBdth5NIHd3KY6KAs9Vo6a+/hVvS1ym7bXkOCXwrn5ScUOu/+bsUSeNLinSnqhO/96CD7jI6aPlNH1/hwZDFU4f5S+XuvrsPMs/nC6IMvq6byv83w5cV7tYvtyc9/7/HMcOXuos2nlOnWuqUo2g6wOzyT//7+TkvEnplqXM8l2S+mkSExcvYPboxEufuf5mB6KsDfP173SKanpDqKdjLN07+zUUaxSdvgnNttwArL8OfXpQ14VasEZ1IOISIZu9tOcV0SkKKxdwlDsusWCXWuZNvPb2JO6qvTxYjS1vfO04TwCG57FjxTD+0ejyrE8T+a7XXjrQvU801F4Wv8j5s54DA2t0B7+Oqol81E529D3QkWGL3+W1B3oxuL+VbV9uYWdpAmc/OAr9c5gy8Qi7t6zn432l3HF2D056MHF43wYTLb9Nh+vaNR+zRS613Xs4K8/hhBZO8vRqTXtucthB/c9znT/LMfC73O7VsM2H8z7K37XUDzTM+VXnuEEx0JIrfIFiSD9wgRMt2H/VtuGO36VfrP/2ULeOzh08ymmu9z7ecKign1atk3ZX2F9F07uHf5HWoC3VtrleDE3q5recfvuks+UcO32Rrr6nArVfvST//WM7OveIpQPUtu0W70tarB3X90mmH++xf8MuSvucZaPTSr8Jj/EDx1dsK/iM/fbvefbx1ke9V3udtd+1Q0oSsf4ruXY5KvnL0XNc5HVuHbvUb9u8n0U7HvbmvjNdOjTdllrC0/820OQ+3Kul236tS68LS9f7eKHASafnX2LtoS0se9rXIK0kpr/E4uceIKGJHUPLt6Wkuqcu2fZacpwSeIGfQ2D5dzr2sMJZb3VKZ++GeYGqM/99lR/eoCO3dqCLFXA2dSeHcNae/yf6OuAsHaa/w+4ruUxtiSb5kV/wwSMvUm3fwdaDX+Mq/4A5S7awdkYu8X1WMDnBoMU++xn7yy+Q7KuinVWcudQYyACsv3Ml+yifnuw9k+TCWXWOhoNt6qm9tGclMX0mU75/C98feTMnlvgftLena2w8cAj+6xznXVz+jH+vp1n3Rm82DM9k7ZIlvDNqRbNnEzyL4bs8m8TEuZP5ftLt3Jd0gqX1DtZbqh3XdfRcvvdNhIxqaT58LD25fcwA+PIvfPxmEWfPQodHb+GWQTeRZl3N2o/fofjrI2Ad12C4UKBYsHbo6DlbeKUHnwHlN+yg18PkPHEH/W4dRtKJ5fU6Qct1HekOnHOWceRUDUQ10+bb7D7qMozsBxoeIFxVjv2LgWiGXXJcfrC1th9o2f6rtg033G/7raP6J1wuUUi3ep1cSX91hI/3HWdacmLTbam2zTV8nd9+sOFJq0sKwH61BVq8L7mSz4y7jdH9rez/cgtvFf8P5+hJetItDIxJwVrwXu0+3pruu9pb913r96n+y9GwIPPzjUgDj/suoaXtrMWjFCOITp5E7sZJ5FbbKdlykH+4yvkgeynbCp7n2fhevJMR1+hdV7ItnW707ia06Dgl8AJ/Wsp3YIGT/W+v55PKGjw73VdZWHC0/mt9K5M9/OfuE56x25Uf8/tXt7YslncyJBziD78rorTaBa4TlMzxzBD3n2UfXnxjuuHc66/zZmkVnhx67+LgfzcYf66jFP/4Ns8s+znbqY4bQtro0aRNGMkdRpSTjXzOulUfeWbjuyrZm/8mxU7AOohb4ptYgECsvy83sPrdUqoBV+V2Xp6/HidgHTmA+Kb2+9Uncdg955l7DhzO/fcnc/3f/8S7m474vch3hsXz+W9+4mmbVB9kVeZtTdxlpyPDnhhDctf/lx//cjRWPmXRyx9xwgX+449r/1Y8QOTxcsoArDcxcMQoRiV/m7/v2MjmVs23bE/c4OGNltdV+SdWFWyqf4KpXTIzPvdblgobr42+0e8zzvJJwXr204PUAT1oFxXPwNs7wva3WfulE24fwHcMOZttISr5Ts8Qs7Pr+H3BQe869d21aSg5JS3alV6lao47jgFgjRnI3Q/cS/L1p9lRXFLvbLWvw4XPWVfwqXdewQlKFuXV/02TNruPuoyw6AdcVNte9RsmdLkbAQRbK/uBFu6/qG3Dn7Jo/npP3uqto2TSh97UurOGLVknV9xfOdn/9lu8Z/fmYc963io8Cr59lX+bW7CYNaWetV7XL1hJfPQObm7xF7qC/epVaPG+5Io+1JeLT1lbYPP2wZHeoaG+fXxHbh0Y773i6v9dX2Xhau8d8vyXo1cqQ2620i45i73+/VnFUtK6xTW9vdeuI4MEdP9aw4miLM8dglJfoqS6JymjR5M2+kFGDr30nBFLoLelFh2nBJ4BPXt74kaM80yQOLSCzFsTiY9J5I6fHKDbnQ2SGnkTA4d6JklsmjGUm2NiufnWaeyiawsvh3ybgY88zjCr3xjLmwaTuWofWO9l5iPJVza8oBFjbzfXPAtRA8czc3i033jqRO7wncV6djwDmzoos9zIXY9P8kxiWZXJHTd5Jv/dfLtnpro1dRx3x7Vg6E+rOSktyCK1bxzxN93GD17egpNoRvzyRwxp8iAyEOtvH2tn3MstMbHcfGumd0LQaHJ/cnvT771Mm/ONzbMkjCZnejKwj4KMwdzsP5691ySm3xvbxMYTQdfhk/hhLyvOjWtY90VzA2AsRN48wNPpOYuYebt3G8nYBL060uQcgsuwxA0jIzW63vLefOskljQ17KXZz/Dt1AD6M/A7HYDO9L6jt/cxK/3u6FU7gOCq+M7m+d921JpM+rP3YvUbD+pbp9bhj5M+0IgrEw1FcfPAZM+Vio1ZDLkp1tMmd/4viVZq5xBgieXux0Z4xiX7trWbBjPt6PUMqbfzMnofda0Kg37A9Te2vv6W9w43TkqXjPeOQfb/VWEIu36AFu6/LAk8OOdJEvGfo+D7/KudR9GCddKa/urQambe7c3DQ/M9kzhTs/jxkM5AexLGTffcJMBvHlJtv9DrSXIf/+4VFXyB2K9ePkhL9yVN8Tuzv+RBvlN7i1K/A3yoO5FzvWdoqEdvBvfxG3Bb22b8xuP71hnJTH3pEc+dnJrUzPbuXUfGCeT+ta7/rvsOscTfNNQzdt86gowRTfX7QKC3pRYepwSaIQNXLV0fYEHxa0z1zsS3Dn+a14oX8WRSxwYv7MH9c5YyN903riqJiXOXs3j2PVzq3j9+H0Bk4iReLl5JTnrd2CxPvDC/nV9kHyYvXs2KuQ97ZtUDWIcz9Y3VlziLZSEy+Sne3lw/H568ruSdhQ80ulVmYN3PwsK36tand3kXNHt7rACsv+G/Zl3hS0zsZfV77zzSujbTgppqc7n5rHrjKc+k2tpJSB1Inr6cdYun+d0lIZph0/NYt3KWd9JUEyK/S+ZLT5J4mbs6ecYq1i03vR5mbuGbvP7EHbRqAq2lB2kLV/Pa9OHeg6hohk1/jffyn6LFu6Has0pA/2R6X9+O+h3LAEYP7hmYnYYllnuyf1qX2+h2cCGSxIz5vJM/py4v3u/xTmtuOdoq3nk4ub7tzjN2dN2aZUy5vaPfRM4Iuo6exztvPO39Dt7lzJtC0jfrfdG2u4+6jGu+H/i7jQ82BvDAzgit6gegZfsvX3+xioW1+w08Q+Xyiy5xi8WWaMk6udL+qiPDXv4P1tVum95tzv91kYOY8cciVrzs/709n7fRd2vSK/oaAdivXlZL9yVNaU/cvU/53fXoW+AtWetO8PidyKm3jx/O7fUKrg4kZ61kY/6C2m3Ws/+bw4rNKy97y/KG27unnb7DCu9dkIwR4P2rr/34b2++HFzquCLQ21KLj1MCzB1w592fvZzqjusZ447rNcNddPyf3of/7M67t7c7rucA9+MbjgU+rITOyQ3ux3vGuON6/tRddPJ/Q700Isb738/cebfEuON6xrj7PbnBffxfbrfb/S/3+c+Wuu/TtuBWPyCBc8xd9KMBbbfNaF8i1wgDpu1ZuXloKolLDlHqLGLm7UXMrPf0CEZ+LyCDDkREQqPdTQx5NJllS2zeoUVZ9Z62pg5j0PUhmRV9jVA/INIi2pfINcKAa/AWIpOf4I3CPL/LTtCyyy4iIuGgqSEZELzhedc69QMiLaN9iVwbvuF2u92hXggREREREQkN1Z0iIiIiIiamgkBERERExMRUEIiIiIiImJgKAhERERERE1NBICIiIiZ3kcqiaXW/VB0zjeJK/9+ldlFVMoek2ud9vwrs7wL2/PS6z+g9h5LaH5Cq+8XrS/7V/kK2SHCpIBARERGp50v2/vWc3/+dOD7fg/NSb3EdYeeGz/3esp4lhXYM+E1ZkYBTQSAiIiJSz1GKtxygyvdf13H2lxy55DtcZbso+tIJ1nE8/8L9WHGyf8MuylwAkSRnfYijotzz53iHqR0BejG1cH/d459nkazfIZMQUEEgIiIiAeSi2r6ZxZm3+Q2feZzFRTYqfafLK4uYEhNL/ICXKN78EiNjYomPSWeVvbp26M7AhYVsnpPmeX9aPnYXQBX2rb9lSu+6YTZJmXkU2ypx+Q/ZqX29b5FKWZXWh/iYoeSUnL704t85gYn9rTgLt2PzDvnxHexbH3qYhzo29aYLlH26hf2AdexIHhw3ljQr8OUWdpZduMp8ihhPBYGIiIgEjOvEuzybNoVlWyrrHnRuYdmMp3jx3aP1h9CcXcHMx1dQCmCNo2eXul+wPrd8Jj9etQ8Aa78YulhqOFE0hwd/9DLbnP4fvZSZ6fN574SFhLGZTGziQLzu7H0KdyV/+9Jf4N+SuGvMAHDu4QuHk7qD/R6kjbidLk1+ad9woR6kDe9LVFRf7hrbA/icde/vp+FsA5FrjQoCERERCZALlG1azyanlcTp6/iiohzHVx+zKDUaqOTP5acbjKmve93hP8/ie1H+hyXJTC38HEeFnZ3P3UqUq5zNKzfhrH28nMM78xhhBZyllFfWgN+BeNGnR7yxfAf0Vvo9O4khUZc79LESn/RdOnCEj/cdx0U1xx3HgP4M7NWpyXc0Lji+TfLwFKw4KX39ffZWaSaBXNtUEIiIiEiAtCchowBHxee8MfQMW4sKWfXCT5m50XO1oOZMVYOJuQMYP6ofkYAlMhKr/1P972fkLR2AdkRGtgdLIpOLD+L4ajlDjpdQXPQH5vzoeTY5Af6bM1UX8D8Qrx2/X72fD97+HBjA6ME9W3Tg0y5+AKlW72ecO8DWwqPQP5ne1/+fJl59ji/ef887XOhOkqMsgIWo5Ds9w4acJWy1fX1laRQJMk1dERERkQBxUV36Fk+Pyak3rMcnolNU/YP+BsOE6j3VL4Yu9Y7eqyjNn81Dcz9q4m4//06nqPaAhaiBo/hhr/Us+3ILO8vG0+X4+/zhkBNr6jjujmvfsq/hvdKwtuA/KV4ZTbHTSr8xtxFn+e/Gr636gg2v2wBwFjxGckHDF3gmKM9KSSGqZdFFgk5XCERERCQwXHbeefZXbHNaSUzPYdFv32HHV/tZN71X06//ZgeirE0fijQsHlz2Qp6b+xFOkpg4dxGLC3dxuPZuPX4i+zFywgA8w4b2sndLCU56kDbhDrq2+KinA98Z2B/Yze+XFOFs9uqCiyrbdooveT9ScBYWs/1ETUuDiwSdCgIREREJjOqTOOxOIIqeA4dz//3JXP/3P/HupkvfsvPyXFQfL6cMwHoTA0eMYlTyt/n7jo1sPtvwte2JGzycfjjZ/+avWVR4tGWTietpx/V9kunn+2+zVzK+xralBCdgTV+JzXf7UN/fvpWeSc7OTeRvKtdvEsg1SwWBiIiIBEbkTQwc6plAvGnGUG6OieXmW6exi65YaWoOQUtZiLx5AHdYAWcRM29PJD4mkTsyNkGvjtTNIfC+Om4YGanRYD9EqbOlk4kbRIy7jdH9Pdco6uYGNFDlnV9AR24dGN94SFDtJGf/3yQQufaoIBAREZHAsPTg/jlLmZue5H0giYm5+ax64yluhXr39r/ij+56Hy8UvMTEXt6BRL0eZm7hm7z+xB00+iExy43cOWGEd8hRyycT1w/Yk9vHDKD2VqKNXuA/XKg3g/t0buJDfJOc0W8SyDXtG2632x3qhRAREREJHL8JyP3nsHFDBgk6BSrSLN1lSERERNqIv1Gc+QAzt/gmFvRgYtYoFQMil6FNRERERNqIDnTvE+39dxITF7/G7JSmhvKIiD8NGRIRERERMTFdIRARERERMTEVBCIiIiIiJqaCQERERETExFQQiIiIiIiYmAoCERERERETU6Y0OVYAACAASURBVEEgIiIiImJiKghERERERExMBYGIiIiIiImpIBARERERMTEVBCIiIiIiJqaCQERERETExFQQiIiIiIiYmAoCERERERETU0EgIiIiImJiKghERERERExMBYGIiEgLuez5jImJJT4mlviYdFbZLwA1VNreIie1j/fxPozM/gPv2ypxAVSVkNPb956h5JScbkXgUlal1X3+mPxSz2fXvYBq228Z2XsOJVW+Z05Tkj3U+55YkrJLqLriwBew56fXfkZ8Wj52F1BtpyQ/m5G1ubiNKXmbsVe7ABdVJXNI8j1Xb5mu4Cs3mWsX1fbNLM68rYm4BCnXgOsoxT++zS+nBua63vL4/6Wzyu40MNeAqxLbGr/1nJpNgaHt+v9jm18eG/8NJafkkHG5brQtp5GzZg+VLjC+XZewKjvNuHZ9OW4RERFpkX8dXuke3XOIO3v7f9U9dnyD+8e9HnBnr/6z++S/3G73v0669yzJdPfr9YJ7+z/+5XuV+x/bX3D3a/Delgf+qzv/gd7ufs9vd/+j8ZPu83/9D3f2A0PccfViev1juzu7V0wz772c/3EfXvlw/c/91xF30ZO3uvv96BX3npP/9Dx0cqd76Y9udfd7coP7eG34/3Jvf76ZZWqBS+Z6w1/d591ut/v8X91Fzz/gjrt3qfuz88HItdvtdv/TfXzDDHe/nk3kNNC5rv3Mh935h//nEu8NfK496znV/fjKA55cu//hPrzhBfd9Pf2Xxehce53/szvv3gHu+17+s3dZ3Abk+l/u858tdd/Xa4a76Pg/vQ8dcRc9OaR+3GC0a/c/3Se3/8J9XyDb9WXoCoGIiEirXaBs03o2JYzh0YcHEW0BLNEMnPoMMxN2stX2tbHhXZXY1szj6Q9vYMyY7sbG8oUs20b+xgjS0scxMDoCAEv0bTw1+wniNubxyicGnL0E4DSf/C6PHWOzmDU6kUiAyETunzyGfofWs2GvwbkGwEV16TrmzjtA9wRrUOJV2bZTTBw9u0QEIZ5f3E/eIOfjQUwa28uTa6JIGP1TZqUfY9Gq3a04K99a57C9/muW8Ri5j3/XuyxG+Jq9hes5NjaNO7t6c225kTsnpHDs9ffZ24qrAS3jadebEsbw6APedk0E0UPGMT5iJbnr7Y2vUBlABYGIiEirVXPccQxrvxi6+Peolk707FdD8ZYDBh44XcC++mfkOIbw4vTvcZ1hceqzJGSwoeJjclM6N/HsaQ5UnDHoAKYzKb/4mH2/SCHKkM9vgerPWDHjddrlZDEuGPUANZyqKMOZEEu3yGAesn2NbUsJjL2T5Cj/uMFeBy6qbWvIWXKaibMnkhzUHPhxlnHkVI0xn+06w5H9p5vZh1zH/g27KAtCRaCCQEREpLW8nXlznPsrOGVYZx7BDffO5+15d3muTFwTBjB6cM+gHVy4KnexfP6rlKVm8eMhTRUogeQ5U/2HmCyyH4gL0nf0Fpz/9zjFU/znTRRhqzToABW87fo8cfHf4mRJfu2Y+qTM37LNHrxrA7j+xtbX3+JYUNbvtxk4dhzdC4vZfsKbW1cltu0H6D59Og8mtDc4fjPs5RyvNr4iuGZ2ISIiImGn+iQOuzNEwS1ERl9v4BCKK+A6ynsvv0pZ6jjujgvCgZN3MurNt05iyc6+PP7DWw0uirxnqjelkL/wAboGreLxHJh373Yr6a/twlFRjqPiP5kV+zk5j72KzagDxeqTOOz/xPn2PJ7b3p2nPjiIo6KMnbM68h/j51B8wsBixE/t8LQJdwQh5xYikx9n8bP/xczbEz3F102DyfzifhZPH2TcdmbpRM9+TRQ7lznZEPDFCFokERERaYOqKF39K3IqRrF0zn3BOVi2JDK5+CCOilJ2/C6Gjx4aw9Sio4aNtXadeJdn00u4+6VHgjtsxZLI5GIbH9S7ChRFwrA7ST7ye4PHlzspixjPgjm+2BYiE+9l0kgbOb/bGYQ5BBco+3QL+3uNY8zAbxseDc5hy3uYB3fcywcHyrzF15d8MOZPTJxSQKlhZ+k7M+QnWdxRmMfLJSc869NVyd5lyymqMfqqSB0VBCIiIq0VeQPxQZlceq2qojR/Ng8tgJmLf0pKdDAnvgJEEJ3yBLPSI9i0cpsxY61dR3kvN48jjz9HZnIHAwK0QuQNxCdAmeMk1QaGaTSunUi6xXc3eCicl+sIOzf8hX4TRnBLMIqwqi/Y8Ppp0tLvJbE2XhSJYyeQuvN1CgyctG7p+gALiqfSoeBBbo6JJX7kMv56Rxa/ntAdgjR/RAWBiIhIazV3ud+r8QFVG+KqZO/SmZ5iYMN8JieGbKqvh0FjrT3DVo5SumQ8t/juH3/TveR+6cRZ8BjJ/vftbyui+nLX2B4hXQRX2S6KvryOvjGdgnCw6r2bU1Oj/yJvID7htME3CLAQmXA3M1Z4h4Vt/AXpyddxsqkbFhi2BCIiItJKzZwxrZ2UecO1McY/wFyVW5k78i5+sLErucVBKga88waa+yEqa6M74gSG565K5d4hJN6/rz4ip78Va/pKbBUFTDZowqnnR6ya+CGq6pM47J1JG97XoLv9RHHzwGQo3I6t3u02qznuOEViSh9uMPQI0jtcyJrCXcnBGC5kISr5TtKauthneK49P5LWqF27znBkf4SBcetTQSAiItJq7YkbMY4R9ldZuPqgZ/iGq5K9y37DIvv9TB+b0PY62uo9LH1sGgVHRrDojedJSwjSlQFLAg/OeZLuhW+zodR36FRDZcmrLCzszcxHkkN3O1KDWBJGkzO9M8UFH/mNYa+itPBtNg418s47EXQdPokf9nyPhW985h2WVEPlnvW89UFfpkzob3Ch67m7UrCGywAQlUz6s/05sOV9SmrvpOTJdXFPI+cxtCdu8HDiCurPIbC9uYairk8F4e5KHm1uPyUiIhJMlq7DyPrdE3R52zuk5Kbb+OG+3uQW/JghBpyxDq0L2NcvYdkhJziL6u7G4vfX3Bn8q2chMvkJ3ii4h7ML7/HGG8CPtnRjVrCuUgRdB5KnLyd/1H+x6Htx3u/8CAVMYK3RdzuKHMSMP77NTF7j9phY4mMSGfHq//KDdfNI6xrsuSLBEEVixi9YMBy2TvPd4vUeFp0dyzt/fMrQyeSWhEm8UjiBf82/2zOH4KYn2EAarywfHbQ7WrULThgREZG2KoqElAxyUzLIDdkytCchowBHRluJ05wIopNHM2PFaGaEahGg9i5Hk4MSK5rk0Vm8NjorGNHqi0xgWNbr7At6aM8PoDmCHTZk23IE0cmTyN04KWT7kLZ26kJERERERK6ACgIREZErcpS1GYOIb+ndZapKyOkdR3LGaq72J8w8d7Xpw5j80hbcf/40JdlDiU96jLVXHXg1mUlxxKflY79sYBdVJXNIihlEZsHRqwysXF+acn11gc2X6+Z8w+12uw36bBERERERucbpCoGIiIiIiImpIBARERERMTEVBCIiIiIiJqaCQERERETExFQQiIiIiIiYWNj8MFl8TGyoF0FEpNUcFeWhXgRpIfU3IhLOWtPfhE1BAKHrUONjYkMS22xxQxnbbHF9sUNFuZZrnfaBbTtuKGObLW4oY5stri92a2jIkIiIiIiIiakgEBERERExMRUEIiIiIiImpoJARERERMTEVBCIiIiIiJiYCgIRERERERNTQSAiIiIiYmIqCERERERETEwFgYiIhJkL2PPTiY+J9fyl5WN3Aa5KbGuyGel7PCaNnPwPsVXWAC6qSuaQ5Huu9xxKqlwBWp4aKm1vkZPaxxu3DyOz/8D7tkoCFaE5rso9FGSn1eUiNZtV79moND7wJXJtJOXaDLmuc5qS7KHEx9zHYlu18eFMnGsVBCIiEp6sj7JiXxmO4gwSLDWceHc+GW/BxMJdHK4o5/Du57h+Ry4Zy3ZShYWolHnsq9jDivQeAV0M14kPeTH9bZiUz46vynF8tZV51+/i5+mv8EnAio6mAh/lvXnPsJYJrNtdiqOilB0v3sins7NY/slp4+JyuVwbR7k2Qa5rVVGan8O0gqNBiAXmzrUKAhERaQtc5Wxe+TFxEx5hUnI0FsASfRtPzX6CuMLt2AzrVC9Qtmk9mxLG8OjDg4j2BGbg1GeYmbCTrbavDYoLrrJt5G/szvjJY0mOjgAiiB6Uwaxnu1O85YBxBzDKNcq1cbkG39WYRxg59yOchkbyD2rOXPuoIBARkfBXfRKH/Tr6xnSq17FZusTQlxIDO9VqjjuOYe0XQ5d6gTvRs1+NgQeLLqqPl1NmjaNnlwi/xyPoEhMHRh7AKNdeyrUhLtpYmjqeuQUOEn8wgWFWowI1YMZc+4cLQgwRERFDuU5VcKDZU4mnOVBxxpixuK4zHNnf/JAR5/4KThkSuIZTFWXNnz11lnHklDHjnpXrhoGV60D7tzumsahwK+/9Yjy9v2lcHH9mzbWPCgIREQlz3jO4oQhdfRKHPWiDGvwDc9xxLARxlevgMWOugXbJ/PS3WaR5h+0Eh0lz7UcFgYiIiIiIiakgEBGRMGchslsscaEIHXkD8QnBGuRcLzDd4ruHIK5yHTxmzHWoKNcqCEREJOxZusTQt9k+tXOjiYKBC9yJnv06N/t0o4mCAeOZ0NrsV240ATZwlOuGgZXrtsDsuW5jq1NEREwp8gbiE843mvjnmSjYnfhukUYFplt898YT/1xnOLL/PHHxN2BMZO8ZzUYTWr0TYBNi6RZpUBevXHsp122KyXOtgkBERMKfJZa7HxtK2YLFrCn13KTPVbmL5fNfpSw9kzEJ7Q0K3J64EeMYYX+VhasPUu0JzN5lv2GR/X6mj00wrKO1xA0jI7WMRfPXU1rtAmqo3JPPwgXHmJg1igTDAivXyrVxuQ4Zk+e6XRBiiIiIGCyCrsN/wtJzq1h4T39yAYhm2PTZ5E+8nSgDI1u6DiPrd+dYPX88t8z13C3EOnwauQUTGRJlYFduuZG7nv4V51blMbJvDt7ATP1lHj8Y0vwQhKunXHsDK9dtirlz/Q232+0OSqSrFB8Ti6Oi3FSxzRY3lLHNFtcXO1TMmOtQxW6bLmDPzyR1QRwrds8h5Yo6zNOUZD9IZmFKs+81Y1sxW9xQxjZb3FDGNlvcq4ndFks8ERERERFpIRUEIiISnpyryUyKIz4tH/tlf8nTRVXJHJJiBpFZcDQYSyciEjY0h0BERMJMexIyCnBkXMl7LESlzGNfxTyjFkpEJGzpCoGIiIiIiImpIBARERERMTEVBCIiIiIiJqaCQERERETExMLqdwhERMKVfocgfKi/EZFw1pr+JqzuMhRuP/IQznFDyWy5NmO7DhUz/gictI4Zt0szxQ1lbLPFDWVss8X1xW4NDRkSERERETExFQQiIiIiIiamgkBERERExMRUEIiIiIiImJgKAhERERERE1NBICIiIiJiYioIRERERERMTAWBiIiIiIiJqSAQEZEwcwF7fjrxMbGev7R87K4GL6new+LUu8kpOe19wEVVyRySfO/pPYeSqoZvaq0aKm1vkZPax7tMfRiZ/Qfet1USqAjNcVXuoSA7rS4Xqdmses9GpfGBsa3JZqQvbkwaOfkfYqusMTiwn6oScnrHEj8gD9tF48OZL9cmbNd1S1C7vxiYZ8P45hW6XPuoIBARkfBkfZQV+8pwFGeQ4N+bVR+k4Ncvs/GQ/wGThaiUeeyr2MOK9B4BXQzXiQ95Mf1tmJTPjq/KcXy1lXnX7+Ln6a/wScCKjqYCH+W9ec+wlgms212Ko6KUHS/eyKezs1j+yenLv7/Vajjx7nwy3oKJhbs4XFHO4d3Pcf2OXDKW7aTKwMi1qg+y6umfsdYZjGCYMtfma9e1C0B16Vs885PVBK15hSrXflQQiIhIG+E9yzajhO+MvRtrUGJeoGzTejYljOHRhwcRbQEs0Qyc+gwzE3ay1fa1YZFdZdvI39id8ZPHkhwdAUQQPSiDWc92p3jLAeMOFl3lbF75MXETHmFScjSer3wbT81+grjC7dgMPYDxruOHxpO7pdLAOPWZL9cmbNfgvRrzAhPuyWFbsKqBEObanwoCERFpE1z2t/jx8+Xc9YvHSb4uWN1bNccdx7D2i6GLf0hLJ3r2qzHwAMZF9fFyyqxx9OwS4fd4BF1i4sDIg8Xqkzjs19E3plO9gwhLlxj6UmLoAcxF23JGjX2etYfieSg9JUhFnxlzbcJ2TTW2JT9kfM6blPYazcTh0QbFaRw3NLmuTwWBiIi0CZYb7uOVP/6MlOiIy784UFxnOLK/+WEMzv0VnDLk+KWGUxVlzQ9pcJZx5JQxY8xdpyo40Gzg0xyoOGPguOdvMXh6Hut2/5HcsUkEZ02bMNcmbNcA/FsyUxe/w44PchnTp6NxcfyFLNf1tTM+hIiISBBEXk+wzunVqj6Jw+6EfkEPzHHHMSAuyHG9Z3CBvkGODNAu+UmWJHv+ffFEsKKaMNema9cAkSRP+wXJ3uUIXvMKVa7r0xUCERERERETU0EgIiLSWpE3EJ8QnJHsDQLTLb57COJaiOwWG5Lzt6Fjwlybrl2HUMhyXZ8KAhERkdaydKJnv87NPt1oomDAeCZZNnsY0WhSZuBYusTQt9nAnRtNgA1/Jsy1Cdt1yIQs1w0Ww/gQIiIibZXnjGajiX+uMxzZf564+BuINCSu9+xxo0mW3kmZCbF0izSoi4+8gfiE840mtHomwHYnvpsx3zh0zJhrE7brkAlVrutra1kVEREJovbEjRjHCPurLFx9kGoAVyV7l/2GRfb7mT42wbCO1hI3jIzUMhbNX09ptQuooXJPPgsXHGNi1qj6P9YW0MCx3P3YUMoWLGZNqeeGiK7KXSyf/ypl6ZmMSWhvUODQMV+uTdiuQyZ0ufanuwyJiIhcBUvXYWT97hyr54/nlrmeGyZah08jt2AiQ6IM7MotN3LX07/i3Ko8RvbNwRuYqb/M4wdDmh+CcPUi6Dr8Jyw9t4qF9/QnF4Bohk2fTf7E24kyMHLImDDX5mvXoROyXPv5htvtdgcl0lWKj4nFUVFuqtihjBtKZsu1Gdt1qJgt123XBez5maQuiGPF7jmkXFGHeZqS7AfJLExp9r1m3S7NFDeUsc0WN5SxzRb3amK3uQsvIiIiIiLScioIREQkPDlXk5kUR3xaPvbL/pKni6qSOSTFDCKz4Ggwlk5EJGxoDoGIiISZ9iRkFODIuJL3WIhKmce+inlGLZSISNjSFQIRERERERNTQSAiIiIiYmIqCERERERETEwFgYiIiIiIiWlSsYiISAOh/L2MUMU2W9xQxjZb3FDGNlvc1gqrHyYTEQlX+mGy8BGOPyqkuOET22xxQxnbbHGvJnZYXSEIt+SGc9xQMluu1a6DF1dEREQa0xwCERERERETU0EgIiIiImJiKghERERERExMBYGIiIiIiImpIBARERERMTEVBCIiIiIiJqaCQERERETExFQQiIhImLmAPT+d+JhYz19aPnYXUG2nJD+bkb7HY25jSt5m7NUuwEVVyRySfM/1nkNJlStAy1NDpe0tclL7eOP2YWT2H3jfVkmgIjTHVbmHguy0ulykZrPqPRuVxgfGtsY/12nk5H+IrbLG4MB+qkrI6R1L/IA8bBeND2e+XKtdm6Fd+6ggEBGR8GR9lBX7ynAUZ5DAUYpnPcq0HTcyb3cpjopyDu9+mX4HX+DBWe9ywmUhKmUe+yr2sCK9R0AXw3XiQ15Mfxsm5bPjq3IcX21l3vW7+Hn6K3wSsKKjqcBHeW/eM6xlAut2l+KoKGXHizfy6ewsln9y2ri41HDi3flkvAUTC3dxuKKcw7uf4/oduWQs20mVgZFrVR9k1dM/Y60zGMEwZa7Vrk3Qrv2oIBARkbDnKttG/sYI0tLHMTA6AgBL9G08NfsJ4jbm8YphBxIXKNu0nk0JY3j04UFEWwBLNAOnPsPMhJ1stX1tUFzfd+7O+MljSY6OACKIHpTBrGe7U7zlgHEHMK5yNq/8mLgJjzApORrPV/bmunA7NiMPFn1nrR8aT+6WSgPj1Ge+XKtdm6Fd+1NBICIiYc+SkMGGio/JTencxLOnOVBxxqBhDtUcdxzD2i+GLv49qqUTPfvVGHgA46L6eDll1jh6donwezyCLjFxYOQBTPVJHPbr6BvTqd5BhKVLDH0pMfRg8aJtOaPGPs/aQ/E8lJ6C1bBI/syYa7Vrn7bbrutTQSAiIm3cAEYP7mlMh+c6w5H9zV99cO6v4JQhxy81nKooo9mRBc4yjpwyZtyz61QFB5oNbGTxBfAtBk/PY93uP5I7NomIy78hAEyYa7XrBtpiu66vXQhiioiIGM91lPdefpWy1NncHdfemBjVJ3HYndDPmI+/RGCOO44BcUGO6z2DC/QNcmSAdslPsiTZ8++LJ4IV1YS5VrsOqtC06wbLEJqwIiIiRqqidPWvyKkYxdKV99FV18NFRJqlXaSIiLQxVZTmz+ahBTBz8U9JiTbwAnzkDcQnhGLEbyTd4ruHIK6FyG6xQT9/G1omzLXatemoIBARkbbDVcnepTM9xcCG+UxOjDI2nqUTPfs1NZHZo9GkzIDxTLJs9pCt0aTMwLF0iaFvs4E7N5qUGf5MmGu16wbaYruury1/NxERMRFX5VbmjryLH2zsSm5xEIoBwHdGs9EkS9cZjuw/T1z8DUQaEtd7RrPRJEvvpMyEWLpFGtTFR95AfML5RpMsPZMyuxPfzZhvHDpmzLXatU/bbdf1qSAQEZHwV72HpY9No+DICBa98TxpCcEoBgDaEzdiHCPsr7Jw9UGqwXOVYtlvWGS/n+ljEwzraC1xw8hILWPR/PWUVruAGir35LNwwTEmZo0iwbDAsdz92FDKFixmTann5pOuyl0sn/8qZemZjEkwaAJ3CJkv12rX0PbbtT9NKhYRkTB3Afv6JSw75ASKmHl7ETMbvMKavpIdv0jBiDLB0nUYWb87x+r547llrue+hdbh08gtmMiQKAPPu1lu5K6nf8W5VXmM7JuDNzBTf5nHD4Y0P9zj6kXQdfhPWHpuFQvv6U8uANEMmz6b/Im3G5LjkDNhrtWuoc23az/fcLvd7lAvREvEx8TiqCg3VexQxg0ls+Va7Tp4cUMpVOu5bbqAPT+T1AVxrNg9h5QrOjg5TUn2g2QWpjT7Xm2XbT9uKGObLW4oY5st7tXE1pAhERERERETU0EgIiLhybmazKQ44tPysV/2J0RdVJXMISlmEJkFR4OxdCIiYUNzCEREJMy0JyGjAEfGlbzHQlTKPPZVzDNqoUREwpauEIiIiIiImJgKAhERERERE1NBICIiIiJiYioIRERERERMTAWBiIiIiIiJhdUPk4mIhCv9MFn4UH8jIuGsNf1NWN12NNx+9S2c44aS2XKtdt224/piS3jRdtm244YyttnihjK22eL6YreGhgyJiIiIiJiYCgIRERERERNTQSAiIiIiYmIqCERERERETEwFgYiIiIiIiakgEBERERExMRUEIiIiIiImpoJARERERMTEVBCIiEiYuYA9P534mFjPX1o+dhdAFfatv2VKb+/jvR9n8VY71QC4qCqZQ5LvPb3nUFLlCtDy1FBpe4uc1D7eZerDyOw/8L6tkkBFaI6rcg8F2Wl1uUjNZtV7NiqND4xtTTYjfXFj0sjJ/xBbZY3Bgf1UlZDTO5b4AXnYLhofzny5Vrs2Q659VBCIiEh4sj7Kin1lOIozSLDUcKJoDg/+5jQji7/EUVHGF8UjOfObCUzI20M1FqJS5rGvYg8r0nsEdDFcJz7kxfS3YVI+O74qx/HVVuZdv4ufp7/CJwErOpoKfJT35j3DWiawbncpjopSdrx4I5/OzmL5J6eNi0sNJ96dT8ZbMLFwF4cryjm8+zmu35FLxrKdVBkYuVb1QVY9/TPWOoMRDFPmWu3aBLn2o4JARETCX9VOXvm5jbTZPyUtIQqwEJlwL49O6E3p6++z17BO9QJlm9azKWEMjz48iGgLYIlm4NRnmJmwk622rw2KC66ybeRv7M74yWNJjo4AIogelMGsZ7tTvOWAcQcwrnI2r/yYuAmPMCk5Gs9Xvo2nZj9BXOF2bIYewHjPpD40ntwtlQbGqc98uVa7NkOu/akgEBGR8BeVQu5fPiY3pXOQA1dz3HEMa78Yuvj3qJZO9OxXY+ABjIvq4+WUWePo2SXC7/EIusTEgZEHMNUncdivo29Mp3oHEZYuMfSlxNADmIu25Ywa+zxrD8XzUHoKVsMi+TNjrtWufdpurutTQSAiIm1QDZV78lm4oIwRv/wRQ6IM6u5cZziyv/lhDM79FZwy5PilhlMVZTQ7YsZZxpFTxox7dp2q4ECzgU9zoOKMgeOev8Xg6Xms2/1HcscmEXH5NwSACXOtdt1AW8x1fSoIRESkTXHZ8xkTk8gdD81n9+2T+eGt0cZ1dtUncdiDNZC9XmCOO46FIK73DG4IIgO0S36SJVmjvUNJgsWEuVa7Dp6Q5bo+FQQiItKmWBIy2FBRjuOrT1nabSM/GDab4hNBvPuNiEiYUUEgIiJtk6UrKTOzmMgm8jeVG3O5P/IG4hOCM5K9QWC6xXcPQVwLkd1iiQtB5NAxYa7VroMnZLmuTwWBiIi0cU7KHCe9v0cQYJZO9OzX/ETmRhMFA8YzybLZw4hGkzIDx9Ilhr7NBu7caFJm+DNhrtWuG2iLuW6wGMaHEBERMZZn3sBQckqampzXg7ThfYkyJLLnjGajiX+uMxzZf564+BuINCSu94xmo0mW3kmZCbF0izSoi4+8gfiE840mWXomZXYnvpsx3zh0zJhrtWuftpvr+lQQiIhI2LMkjCZnemeKCz6itNrbq7pOULIoj+LbHyd94LcNityeuBHjGGF/lYWrD3quQrgq2bvsNyyy38/0sQmGdbSWuGFkpJaxaP5673f23VnpGBOzRpFgWOBY7n5sKGULFrOm1HNDRFflLpbPf5Wy9EzGNad6GgAAIABJREFUJLQ3KHDomC/XatfQ9nPtr10QYoiIiBisA8nTl5P/7loWfS+ObU6AJCbOzeKdWUNIMOqsImDpOoys351j9fzx3DLXc7cQ6/Bp5BZMNO52pwCWG7nr6V9xblUeI/vm4A3M1F/m8YMhRv4eQwRdh/+EpedWsfCe/uQCEM2w6bPJn3i7QVdiQsyEuVa7hjafaz/fcLvd7qBEukrxMbE4KspNFTuUcUPJbLlWu27bcUMdu226gD0/k9QFcazYPYeUK+owT1OS/SCZhSnNvteMbcVscUMZ22xxQxnbbHGvJraGDImIiIiImJgKAhERCU/O1WQmxRGflo/9svcUdVFVMoekmEFkFhwNxtKJiIQNzSEQEZEw056EjAIcGVfyHgtRKfPYVzHPqIUSEQlbukIgIiIiImJiKghERERERExMBYGIiIiIiImpIBARERERMbGw+h0CEZFwpd8hCB/qb0QknLWmvwmruwyF2488hHPcUDJbrtWu23ZcX2wJL9ou23bcUMY2W9xQxjZbXF/s1tCQIRERERERE1NBICIiIiJiYioIRERERERMTAWBiIiIiIiJqSAQERERETExFQQiIiIiIiamgkBERERExMRUEIiIiIiImJgKAhERCTMXsOenEx8T6/lLy8fuaviaGk4UZZHUew4lVS7ARVXJHJJ876l9PBBqqLS9RU5qH+8y9WFk9h9431ZJoCI0x1W5h4LstLpcpGaz6j0blcYHxrYmm5G+uDFp5OR/iK2yxuDAfqpKyOkdS/yAPGwXjQ9nvlyrXZsh1z4qCEREJDxZH2XFvjIcxRkkNOjNXCc+5Bc/L8JZ+4iFqJR57KvYw4r0HgFdDNeJD3kx/W2YlM+Or8pxfLWVedfv4ufpr/BJwIqOpgIf5b15z7CWCazbXYqjopQdL97Ip7OzWP7JaePiUsOJd+eT8RZMLNzF4YpyDu9+jut35JKxbCdVBkauVX2QVU//jLXOy780IEyYa7VrE+TajwoCERFpW6oPsuaF5RzpGdgD/6ZdoGzTejYljOHRhwcRbQEs0Qyc+gwzE3ay1fa1YZFdZdvI39id8ZPHkhwdAUQQPSiDWc92p3jLAeMOYFzlbF75MXETHmFScjSer3wbT81+grjC7dgMPYDxnkl9aDy5WyoNjFOf+XKtdm2GXPtTQSAiIm3IOWyvv8Ci//MYMx/qHoR41Rx3HMPaL4Yu/j2qpRM9+9UYeADjovp4OWXWOHp2ifB7PIIuMXFg5AFM9Ukc9uvoG9Op3kGEpUsMfSkx9ADmom05o8Y+z9pD8TyUnoLVsEj+zJhrtWuftpvr+lQQiIhIG+Gi2raGnNd7kJsziu7B6OFcZziyv/lhDM79FZwy5PilhlMVZTQ7YsZZxpFTxox7dp2q4ECzgU9zoOKMgeOev8Xg6Xms2/1HcscmEXH5NwSACXOtdt1AW8x1fSoIRESkbaj+jBXP7+TugjmkdQ3OoaLnrGKwBrLXC8xxx7EQxPWewQ1BZIB2yU+yJGu0dyhJsJgw12rXwROyXNfXLtQLICIictVcRyme9QybR/yGt5M7ABdCvUQiImFDVwhERCTM1XDi3TxyKsaR+/h3iQxm6MgbiE8Izkj2BoHpFh+MORINWYjsFktcCCKHjglzrXYdPCHLdX26QiAiIuHNVc7mlZtwHnIyvu/LDZ78lMyk9fSbW8g7GYmBPwtm6UTPfp2bfbrRRMGA8UyybPYwotGkzMCxdImhb7OBOzealBn+TJhrtesG2mKuGyyG8SFEREQMZElkcvFBHBXlfn+H2Dh3sPe3CvazwYhiAPCd0Ww08c91hiP7zxMXf4NBVyy8ZzQbTbL0TspMiKVbpEFdfOQNxCecbzTJ0jMpszvx3YJ6jSYIzJhrtWuftpvr+lQQiIiItFp74kaMY4T9VRauPkg1gKuSvct+wyL7/Uwfm2BYR2uJG0ZGahmL5q+ntNoF1FC5J5+FC44xMWtUox9rC1zgWO5+bChlCxazptRzQ0RX5S6Wz3+VsvRMxiS0Nyhw6Jgv12rX0PZz7U9DhkRERK6Cpeswsn53jtXzx3PLXM/dQqzDp5FbMJEhUQZ25ZYbuevpX3FuVR4j++bgDczUX+bxgyHND0G4ehF0Hf4Tlp5bxcJ7+pMLQDTDps8mf+LtRBkYOWRMmGu1a2jzufbzDbfb7Q5KpKsUHxOLo6LcVLFDGTeUzJZrteu2HTfUsdumC9jzM0ldEMeK3XNIuaIO8zQl2Q+SWZjS7HvN2FbMFjeUsc0WN5SxzRb3amJryJCIiIiIiImpIBARkfDkXE1mUhzxafnYL/tLni6qSuaQFDOIzIKjwVg6EZGwoTkEIiISZtqTkFGAI+NK3mMhKuX/b+/+g6Mq83yPf6bXyjKpTGp24JrwI0Nnu7vYgRCdFNxanVsQAXFz0YpEBYMR40xGZ5WRQlEcTTaGzGoF5EYd0GWH1Q5koWSYzCCO7iJopBC4hdvuQlwrdjdp4AJNFVhU7EpRKW9z/+hEE0JfS8nTZ08/71dVqmY60/M5/a2HnOd7zvP0adKRWJOpgwIA1+IOAQAAAGAxGgIAAADAYjQEAAAAgMVoCAAAAACLsakYAIDLOPk8Fqeybct1Mtu2XCezbcv9tlz1YDIAcCseTOYebnyoELnuybYt18ls23KvJttVdwjcVlw35zrJtlozrrM7dzAbAID/qthDAAAAAFiMhgAAAACwGA0BAAAAYDEaAgAAAMBiNAQAAACAxWgIAAAAAIvREAAAAAAWoyEAALjMRYWDNfJ7i1M/lUGFk5KS3WqrnPbV61/+1Kgt3KfezkaVDr42tVGdvclROp5+xUNb1VAxmD1NC+pf1ZuhuEYrIZ1k/LDa6yu/+qwV9WrbFVLcfLBCW+q14MsaV6oh+JZC8X7DwUP0dqpharH8P25V6AvzcfbVmnFtQ60H0RAAANwpd6k2HYkqsrNWAY+kxBlFwj9WwzufKBLrGfLTrvsCucovb9KR2GFtqvnhqB5G8vRbWl3zurQkqP3HehQ5tldN1x7UUzWvaN+oNR1XCj6hXU0rtU2Ltf1QtyKxbu1fPUkfrFqhDfvOmctVv06/0aLarVJ1x0F9GuvRp4ee1LX7m1W7/oB6DSZ/KfGx2h79lbb1ZSJMVtaacW1BrYegIQAAZIGkekPvaad8mlyQk8Hci4ru3qHdgYVaes9MFXokeQo1Y9lKPRY4oL2hz4wlJ6PvKvh2kRbdV6WywhxJOSqcWavHnyjSzj1d5iYwyR6989r78i2+V0vKCpX6yDfo4VUPytfxnkJGJzADV1LvWqTmPXGDOcPZV2vGtQ21HoqGAACQBfp1NhZVX6BYE/MyeWpL6FTkpHKne1UwNNYzVpOn9xucwCSVONWjaO7lDVCOCrw+yeQEJnFGkfD3VOIdO2wS4SnwqkSdRicwX4Q26Naqp7XtE7/uqilXrrGkoWysNeN6UPbWejgaAgBAFhg4qf63U9r5wA0D63Bv0AOtfzS7/jd5XsePpl/G0Hc0prNG5i8DDVDa4KiOnzXzuZNnY+pKG3xOXbHzBtc9f1c/Wd6q7Yd+p+aqUmXmXpCFtWZcXyYbaz0cDQEAwP2S53X86OcqmvjXqvnHgwN7B/5Vjxd/pIb7NyqUMHlVMVML2YcF61TkpAO5A1dwHUiWpGvKfqEXV9w+sJQkUyysNeM6cxyr9XA0BAAA9/NM0X07Q/pT09zUGlxJUr4Cc25S2fF/UPOOcMa+rQMA3IaGAACQvfLGyx+QopEzShj7/8/MSvbLgjXRX+RArkd5E4vlcyDZORbWmnGdOY7VejgaAgAAvi3PWE2ePi7tr0dsFBw1qU2WaacRIzZljh5PgVclaYPHjdiU6X4W1ppxfZlsrPVlh2E+AgAAs5LhoBZ6Z6uh87LNeYkzioTHqXJeifKNJKeuaI7Y+Dewp8HnH688I7kDVzRHbLLMwLct5Y2XP/D5iE2WqU2ZRfJPNPOJnWNjrRnXg7K31sPREAAAXM8TuF0Ny8dpZ/u/qPvLDcS96u54XW/PXqG/nZX+CtzVGSPf/Ds1P7xRazd/nFqWlIzrw/XPa134Ni2vChg70Xp8c1RbEdW6lh0Dn7lf8cNBrV1zUtUrbk09rM1IcLFuvn+2omte0Jbu1BciJuMHtaFlo6I1dVoYGGMo2Dn21ZpxLWV/rYe6JgMZAAAY9n2VLd+g4BvbtO6/+/RunySVqvqZFdq2dpYmGDyjeibM0YqXL2hzyyJd/0zq20Jy5z2i5vZqzco3GTxJcx99ThfaWrWgpEEDwVr2bKvuNtYASVKOJsx7SC9daNPaW65TsySpUHOWr1Kw+kZDd2IcZmGtGddS1td6iO9cunTpUkaSrpLfW6xIrMeqbCdznWRbrRnX2Z3rdHZ2uqhwsE4Va3zadKhR5d/ohHlOnfV3qK6jPO17bRwrtuU6mW1brpPZtuVeTTZLhgAAAACL0RAAANypb7PqSn3yVwYV/tqHDCTV29moUu9M1bWfyMTRAYBrsIcAAOAyYxSobVek9pu8x6P88iYdiTWZOigAcC3uEAAAAAAWoyEAAAAALEZDAAAAAFiMhgAAAACwGA0BAAAAYDFXPZgMANyKB5O5B+cbAG72bc43rvraUbc99c3NuU6yrdaM6+zOHcyGu/DvMrtzncy2LdfJbNtyB7O/DZYMAQAAABajIQAAAAAsRkMAAAAAWIyGAAAAALAYDQEAAABgMRoCAAAAwGI0BAAAAIDFaAgAAC5zUeFgjfze4tRPZVDhZOo3yfhhtddXDvxumhbUb1Uo3i8pqd7ORpUOvmdqozp7k6N0PP2Kh7aqoWLakNxX9WYortFKSGf45y2Wv6JebbtCipsPVmhLvRYM5nor1RB8a6DWGdLbqYapxfL/uFWhL8zH2VdrxrUNtR5EQwAAcKfcpdp0JKrIzloFPFLy9B+1rOJV/d97tygS61Gkq0MP6HUtenCrwkmP8subdCR2WJtqfjiqh5E8/ZZW17wuLQlq/7EeRY7tVdO1B/VUzSvaN2pNx5WCT2hX00pt02JtP9StSKxb+1dP0gerVmjDvnPmctWv02+0qHarVN1xUJ/GevTpoSd17f5m1a4/oF6DyV9KfKy2R3+lbX2ZCJOVtWZcW1DrIWgIAABZ4Jz2vdyq/QsWa+GU/NRLeVNU+cQKVYdf02ZjE4mLiu7eod2BhVp6z0wVeiR5CjVj2Uo9FjigvaHPDOVKyei7Cr5dpEX3VamsMEdSjgpn1urxJ4q0c0+XuQlMskfvvPa+fIvv1ZKyQqU+8g16eNWD8nW8p5DRCczAldS7Fql5T9xgznD21ZpxbUOth6IhAAC4X2+X9nZIlfNKlD/09fxyNf/n+2ouH2coOKFTkZPKne5VwdAzqmesJk/vNziBSSpxqkfRXJ8mF+QMeT1HBV6fZHICkzijSPh7KvGOHTaJ8BR4VaJOoxOYL0IbdGvV09r2iV931ZQr11jSUDbWmnE9KHtrPRwNAQDA9ZJnY+rqK5I//7Q6g4NrgG/QA63vKJwwubzhvI4fTX/3oe9oTGeNxPfrbCyqtCtm+qI6ftbMuudUrdP99py6YucNrnv+rn6yvFXbD/1OzVWlyvn6N4wCC2vNuL5MNtZ6OBoCAIDLDVxV1Eltf/o5dXof0q5YjyKxf9Vjf9Gh6sff0GlTJ9TEGUXCmVrIPixYpyInHcgdrLUzrin7hV5ccfvAUpJMsbDWjOvMcazWw9EQAACyxDnlLH5Gf1c+YeDklq8pVYtV8X6rXjG6GREA3I2GAACQJcaNWP+rvPHyBz43d7s/b7z8gcysZL8sWBP9RQ7kepQ3sVg+B5KdY2GtGdeZ41ith6MhAAC4nEf5ZTep0olzqmesJk9Pv2F5xEbBUZPaZJn2I4/YlDl6PAVelaQNvkJT5noW1ppxfZlsrPVlh2E+AgAAw/L+UjNmX+EbORJnFAkXa3ZpgaETXuqK5oiNf8nzOn70c/n845VnJHfgiuaITZYDmzIDxZqYZ+gUn+auy5cbuyea+cTOsbHWjOtB2Vvr4WgIAADu55mkuT9foqL23+jV0IXUa8m4Pgz+s96efa8WXf99Q8Fj5Jt/p+aHN2rt5o+VGMxd/7zWhW/T8qqAsROtxzdHtRVRrWvZoe5EUlK/4oeDWrvmpKpX3KqAseBi3Xz/bEXXvKAt3an2Kxk/qA0tGxWtqdPCwBhDwc6xr9aMayn7az3UNRnIAADAMI/yyh7W6+/8lTa1VMi/Jy6pUHOWr9a2tXM1weAZ1TNhjla8fEGbWxbp+mdS3xaSO+8RNbdXa1a+yeBJmvvoc7rQ1qoFJQ0aCNayZ1t19yxTz12QpBxNmPeQXrrQprW3XKdmSalar1Kw+sbhz4HIFhbWmnEtZX2th/jOpUuXLmUk6Sr5vcWKxHqsynYy10m21Zpxnd25Tmdnp4sKB+tUscanTYcaVf6NTpjn1Fl/h+o6ytO+18axYluuk9m25TqZbVvu1WSzZAgAAACwGA0BAMCd+jarrtQnf2VQ4a/9TtGkejsbVeqdqbr2E5k4OgBwDfYQAABcZowCte2K1H6T93iUX96kI7EmUwcFAK7FHQIAAADAYjQEAAAAgMVoCAAAAACL0RAAAAAAFqMhAAAAACzmqgeTAYBb8WAy9+B8A8DNvs35xlVfO+q2p765OddJttWacZ3duYPZcBf+XWZ3rpPZtuU6mW1b7mD2t8GSIQAAAMBiNAQAAACAxWgIAAAAAIvREAAAAAAWoyEAAAAALEZDAAAAAFiMhgAAAACwGA0BAAAAYDEaAgCAy1xUOFgjv7c49VP5T/pwb6NKB//7FX5K6/fq/3QO+d9MbVRnb3KUjqdf8dBWNVRMG8ibpgX1r+rNUFyjlZBOMn5Y7fWVX33Winq17Qopbj5YoS31WvBljSvVEHxLoXi/4eAhejvVMLVY/h+3KvSF+Tj7as24tqHWg2gIAADulLtUm45EFdn5M82Y26QjsR5Fhv18pO3Ly6QfPargkzdpUnmTjsQOa1PND0f1MJKn39LqmtelJUHtP9ajyLG9arr2oJ6qeUX7Rq3puFLwCe1qWqltWqzth7oViXVr/+pJ+mDVCm3Yd85crvp1+o0W1W6VqjsO6tNYjz499KSu3d+s2vUH1Gsw+UuJj9X26K+0rS8TYbKy1oxrC2o9BA0BACALJZUIbVHDi9Kyv79XZXmmTncXFd29Q7sDC7X0npkq9EjyFGrGspV6LHBAe0OfGcqVktF3FXy7SIvuq1JZYY6kHBXOrNXjTxRp554ucxOYZI/eee19+RbfqyVlhUp95Bv08KoH5et4TyGjE5iBK6l3LVLznrjBnOHsqzXj2oZaD0VDAADIPol/06an/0Ena36pn5Z932SQTkVOKne6VwVDz6iesZo8vd/gBCapxKkeRXN9mlyQM+T1HBV4fZLJCUzijCLh76nEO3bYJMJT4FWJOo1OYL4IbdCtVU9r2yd+3VVTrlxjSUPZWGvG9aDsrfVwNAQAgCzTr9N7turV4/PV/NCNyjcZlTyv40fTL2PoOxrTWSPzl36djUWVdsVMX1THz5pZ95w8G1NX2uBz6oqdN7ju+bv6yfJWbT/0OzVXlSrn698wCiysNeP6MtlY6+FoCAAA2SXZo3de2y1VVeqmCYanjIkzioQztZB9WLBORU46kDtwBdeBZEm6puwXenHF7QNLSTLFwlozrjPHsVoPR0MAAMgqyehB/fE//ko/rbre7N0BAMgSNAQAgCxyUdEP9ujodbdpwfUm9w4MyBsvfyAzK9kvC9ZEf5EDuR7lTSyWz4Fk51hYa8Z15jhW6+FoCAAA2SN5XAf+8NHIDXqmeMZq8vRxaX9t7jhSmyzTTiNGbMocPZ4Cr0rSBo8bsSnT/SysNeP6MtlY68sOw3wEAACZkVouNE6V80oytFwodUVzxMa/5HkdP/q5fP7xyjOSO3BFc8Qmy4FNmYFiTTT1Vat54+UPfD5ik2VqU2aR/BPNfGLn2FhrxvWg7K31cDQEAIAsMbgxMJOT0jHyzb9T88MbtXbzx0pIUjKuD9c/r3Xh27S8KmDsROvxzVFtRVTrWnaoO5GU1K/44aDWrjmp6hW3KmAsuFg33z9b0TUvaEt36gsRk/GD2tCyUdGaOi0MjDEU7Bz7as24lrK/1kNdk4EMAACylmfCHK14+YI2tyzS9c+kvi0kd94jam6v1qx8g6dyzyTNffQ5XWhr1YKSBg0Ea9mzrbp7VvolCFcvRxPmPaSXLrRp7S3XqVmSVKg5y1cpWG34a16dYmGtGddS1td6iO9cunTpUkaSrpLfW6xIrMeqbCdznWRbrRnX2Z3rdHZ2uqhwsE4Va3zadKhR5d/ohHlOnfV3qK6jPO17bRwrtuU6mW1brpPZtuVeTTZLhgAAAACL0RAAANypb7PqSn3yVwYV/toneSbV29moUu9M1bWfyMTRAYBrsIcAAOAyYxSobVek9pu8x6P88iYdiTWZOigAcC3uEAAAAAAWoyEAAAAALEZDAAAAAFiMhgAAAACwGJuKAQC4jJPPY3Eq27ZcJ7Nty3Uy27bcb8tVDyYDALfiwWTu4caHCpHrnmzbcp3Mti33arJddYfAbcV1cy5gkk3/ngazAQD4r4o9BAAAAIDFaAgAAAAAi9EQAAAAABajIQAAAAAsRkMAAAAAWIyGAAAAALAYDQEAAABgMRoCAIDLXFQ4WCO/tzj1UxlUOClJ/YqHtqqhYtrA7yrVsOWw4klJSqq3s1Glg++Z2qjO3uQoHc/ludO0oP5VvRmKa7QS0knGD6u9vvKrWlTUq21XaOAzGw1WaEu9FgzmeivVEHxLoXi/4eAhejvVMLVY/h+3KvRF5mKlc+qsny2/93/qhVAig7lfjeEZrSFl9CPbUmvHxrVzf0MG0RAAANwpd6k2HYkqsrNWAU9SidBG/azmsGb800eKxHoUOfYbzTiwUj978bAS8ii/vElHYoe1qeaHo3oYydNvaXXN69KSoPYf61Hk2F41XXtQT9W8on2j1nRcKfiEdjWt1DYt1vZD3YrEurV/9SR9sGqFNuw7Zy5X/Tr9Rotqt0rVHQf1aaxHnx56Utfub1bt+gPqNZj8pcTHanv0V9rWl4mwoXrVHWzQI+0nMpybVKJ7q1Y+tFkZ/8jW1Nq5ce3Y35AhaAgAAFngM33YsUMnqyp104Sc1EueSbppcblO/vZNfWjspHpR0d07tDuwUEvvmalCjyRPoWYsW6nHAge0N/SZoVwpGX1XwbeLtOi+KpUV5kjKUeHMWj3+RJF27ukyN4FJ9uid196Xb/G9WlJWqNRHvkEPr3pQvo73FDI6gRm4knrXIjXviRvMGSl1N+ZeLXjmXzI7KU/GFdryd1p8S4PezWiwZbV2bFw79zdkKBoCAEB264vq+FlTt/wTOhU5qdzpXhUMPaN6xmry9H6DE/OkEqd6FM31aXJBzpDXc1Tg9UkmJzCJM4qEv6cS79hhkwhPgVcl6jQ6gfkitEG3Vj2tbZ/4dVdNuXKNJY0I1ksVi/RMe0RT7l6sORkLTij04k+1qOGf1f2j21U9rzBTwfbV2rFx7dTfkOFoCAAAWeAHmlF1p4o6duq90wOT/2Rcofe6VLR8ue4IjDETmzyv40fTL8/pOxrTWSPz8n6djUXTXz012AQlz8bUlTb4nLpi5w2ue/6ufrK8VdsP/U7NVaXK+fo3jJo/+x+PaF3HXu369SJN/fNMBpdp2Qu/1/4/NWvhtL/IYLBdtXZsXDv2N2S4a8xHAABgmkd5ZT/XC0/UqeLGKXps8OXrGvX2H2Yqz1Rs4owi4T5puqmAtME6FTkpyZfh3IE7E5JKMpwsSdeU/UIvlqX+8xenMxqsX/7GieA8lT3ya6WSE8rsR7ap1g6Oa8f+hgzHHQIAQBa4oFDrPbpj/9/oT13R1Kbi2H/oTwv/t6ofaFd3IlPf1QEA7kNDAABwv95/1x9+e06VNX+jKXmDp7Z8TalarIoDv1X7h4bW/+aNlz+QsdXVQ4M10V/kQK5HeROLM35fAjDLwXHt2N+Q4WgIAAAul1Rv6D3tvNL637zx8gfOmduY5xmrydPHpf31iI2Coya1eTjtNGLEZuPR4ynwqiRt8LgRmzIBN3BsXDv2N+SywzAfAQCASR7ll92kyiudzBNnFAmPU+W8EuUbyU5dqR+x8S95XsePfi6ff7yh/QsDVzRHbB4e2GwcKNbEPEOn+Lzx8gc+H7HJMrUps0j+icZ2bADmODaunfobMhwNAQDA/fLLVPPEdera86Y6w4P3AnrV3fG6dk6+Uwtn/MBQ8Bj55t+p+eGNWrv5YyUkKRnXh+uf17rwbVpeFTB2ovX45qi2Iqp1LTsG9kj0K344qLVrTqp6xa0KGAsu1s33z1Z0zQva0p2qdTJ+UBtaNipaU6eFpr7RCTDJsXHt3N+QofiWIQBAFsjXlNpfa01nhzY/coPqPumTVKg5y1fr97+bq4Cpq+WSPBPmaMXLF7S5ZZGufya1bil33iNqbq/WrHyDp3LPJM199DldaGvVgpIGDQRr2bOtuntW+iUIVy9HE+Y9pJcutGntLdepWVKq1qsUrL7R0J0YwDTnxrVjf0OG+M6lS5cuZSTpKvm9xYrEeqzKdjIXMMmmf09OZ2eniwoH61SxxqdNhxpV/o1OmOfUWX+H6jrK077XxrFiW66T2bblOpltW+7VZLNkCAAAALAYDQEAwJ36Nquu1Cd/ZVDhr33MQFK9nY0q9c5UXfuJTBwdALgGewgAAC4zRoHadkVqv8l7PMovb9KRWJOpgwIA1+IOAQAAAGAxGgIAAADAYjQEAAAAgMVoCAAAAACL0RAAAAAAFnPVg8kAwK14MJl7cL4B4Gbf5nzjqq8dddtT38h1T7ZtuU5m25Y7mA134d9lduc6mW1brpPZtuUOZn8bLBkCAAAALEZDAAAAAFiMhgAAAADR5HIjAAAFpElEQVSwGA0BAAAAYDEaAgAAAMBiNAQAAACAxWgIAAAAAIvREAAAXOaiwsEa+b3FqZ/KoMJJSepVuDOohoppqden/lwv7A0rIUlKqrezUaWD75naqM7e5CgdT7/ioa1f5XqnaUH9q3ozFNdoJaSTjB9We33lV7WoqFfbrpDi5oMV2lKvBYO53ko1BN9SKN5vOJhaU2ujwdbVehANAQDAnXKXatORqCI7axXw9Ov0Hxt1R0uPZrx0UJFYjyJdTbp+35Na3HpYCXmUX96kI7HD2lTzw1E9jOTpt7S65nVpSVD7j/Uocmyvmq49qKdqXtG+UWs6rhR8QruaVmqbFmv7oW5FYt3av3qSPli1Qhv2nTOXq36dfqNFtVul6o6D+jTWo08PPalr9zerdv0B9RpMptbU2hwLaz0EDQEAwP16D+iVp3bLt3iJbgvkp17zTNCsmtuU8+KL+n34oqHgi4ru3qHdgYVaes9MFXokeQo1Y9lKPRY4oL2hzwzlSsnouwq+XaRF91WprDBHUo4KZ9bq8SeKtHNPl7kJTLJH77z2vnyL79WSskKlPvINenjVg/J1vKeQsQkMtabW1NoUGgIAgOslz8bU1TdOJd6xw05sngKvSnI/0h8/OG7o1ntCpyInlTvdq4JhwWM1eXq/wQlMUolTPYrm+jS5IGfI6zkq8PokkxOYxBlFwt+7cq3VaXACQ60HUWsDrKv1cDQEAIAs16do5MzAXoJRljyv40fTL2PoOxrTWSPzl36djUXVlzY4quNnzax7TjVf6X57Tl2x82aaL2p9GWo9muyr9XA0BAAA10vdCRj5+v//JD8KEmcUCZsMSBusU5GTDuQOXMF1IJlaZxC1zhzHaj0cDQEAwP3yb9TfPlumnS0b1TnwjSDJ+EFt+F+71B+4QqcAAPgSDQEAIAvkaMLtTfr9ynxtnTNFfu803ba+Rz9paNSi3D+Xzz9eeSZi88bL70jDkaeJ/iIHcj3Km1gsnwPJ1DqDqHXmOFbr4a5x+gAAABgd+QrM/aX+8T9/+dVLvZ36wxU2Co4az1hNnj4u7a9HbBQcNalNlmmnESM2ZY6edMuzUkZu7B69YGo9HLUeTfbV+rLDMB8BAIBhyW61Vd6shs7hm/OSZ2PqUrnmlv3AUHDqiuaIjX/J8zp+9HNzdyYGr2iO2GQ5sCkzUKyJeYZO8Xnj5Q98PmKTZWq/RpH8E818Ymr9FWptgHW1Ho6GAADgfp7JunFhgbYN20NwWFvb9mj8sz/TrHxTp7sx8s2/U/PDG7V288epbzJKxvXh+ue1LnybllcFjJ1oPb45qq2Ial3LDnUnkpL6FT8c1No1J1W94lYFjAUX6+b7Zyu65gVt6U59IWIyflAbWjYqWlOnhYExhoKptUStqbUZLBkCAGSBMQosfV7b/2y9Gv56iuok6Uf36Jm/f17rBx4yZIpnwhytePmCNrcs0vXPpL4tJHfeI2purzbYiEjyTNLcR5/ThbZWLShp0ECwlj3bqrtnpV+CcPVyNGHeQ3rpQpvW3nKdmiVJhZqzfJWC1Tcq32AytZaotSkW1nqI71y6dOlSRpKukt9brEisx6ps23KdzLYt18ls23Kdzs5OFxUO1qlijU+bDjWq/BudMM+ps/4O1XWUp32vjWPFtlwns23LdTLbttyryWbJEAAAAGAxGgIAgDv1bVZdqU/+yqDCX/skz6R6OxtV6p2puvYTmTg6AHAN9hAAAFxmjAK17YrUfpP3eJRf3qQjsSZTBwUArsUdAgAAAMBiNAQAAACAxWgIAAAAAIvREAAAAAAWoyEAAAAALOaqB5MBgFvxYDL34HwDwM2+zfnGNQ0BAAAAgNHHkiEAAADAYjQEAAAAgMVoCAAAAACL0RAAAAAAFqMhAAAAACxGQwAAAABYjIYAAAAAsBgNAQAAAGCx/wcOZJjlavTcRAAAAABJRU5ErkJggg==\"/></p>\n<p style=\"text-align: left;\">In an application, the black-and-white image can be inverted (all white pixels are changed to black, and all black pixels are changed to white).</p>\n<p style=\"text-align: left;\">Method <code>invert(N,A)</code> accepts a positive integer <code>N</code> and an <code>N × N</code> two-dimensional array <code>A</code> that holds the data for a simple black-and-white image; it returns the inverted <code>N × N</code> two-dimensional array <code>A</code>.</p>\n</div><div class=\"specification\">\n<p>In the application, it is also possible to rotate an image clockwise by 90 degrees (90°).<br/>For example, when the simple black-and-white image is rotated, the corresponding 10 × 10 two-dimensional array MAT is updated.</p>\n<p>This would mean the first row of the original MAT is the last column in the rotated MAT, the second row is the second-to-last last column, … and the last row is the first column.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">Consider the following algorithm fragment:</p>\n<pre style=\"text-align: left;\">K=input()<br/>loop for M=0 to K mod 4 - 1<br/>    A=rotate(N,A)<br/>end loop</pre>\n<p style=\"text-align: left;\">where:</p>\n<ul>\n<li style=\"text-align: left;\"><code>N</code> is an integer and <code>A</code> is the <code>N × N</code> two-dimensional array that holds data about an image</li>\n<li style=\"text-align: left;\"><code>K(K&gt;=0)</code> is an integer showing how many times the image should be rotated</li>\n<li style=\"text-align: left;\">method <code>rotate(N,A)</code> accepts an integer <code>N</code> and an <code>N × N</code> two-dimensional array <code>A</code> representing an image. It returns an <code>N × N</code> two-dimensional array representing the image rotated clockwise by 90°.</li>\n</ul>\n</div><div class=\"specification\">\n<p>The algorithm for method <code>rotate(N,A)</code> uses an additional <code>N × N</code> two-dimensional array, named <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABUAAAATCAYAAAB/TkaLAAAA+UlEQVQ4jWP8////fwYqAyZqGzhqKAMLCu/FBoY0i0KGfeiquFwYciaXMaQ4qzLwEGPqf3Tw++z/foPc/xue/4YK/P3/+db6/zUeCf8X3PqOoRwbIML7TAw8qj4M2ekiDEevviHK+0QY+o/hy+0tDFPnMzB4m4kRZSgLduHNDMUWmxmK4XwJBqeiPgZzCTaiDCUiTP/////3+f+zi6r/B2Ss///0L1XClIGBgUmCwSg2gyHh91GG06/+EFZOnH9+Mbw4vYVh61kuBn4uIrSguPv5+v+p8gr/lbFhj7r/G259hCr8/v/W/Oj/yvLRWJMZ4///o6XUUDAUANB9AFw72krCAAAAAElFTkSuQmCC\"/> The <code>N × N</code> dimensional array <code>B</code> is initialized and updated using the values from <code>A</code> to represent the image rotated clockwise by 90°.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm in pseudocode for the method <code>invert(N,A)</code>.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the number of degrees by which the image will be rotated if the input value of <code>K</code> is 3.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why it is more efficient that the loop in the given algorithm fragment executes <code>(K mod 4)</code> times instead of <code>K</code> times. You may give an appropriate example in your answer.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the algorithm in pseudocode for the method <code>rotate(N,A)</code> described above.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>To avoid inefficient use of memory, a new algorithm for the method <code>rotate(N,A)</code> is constructed.</p>\n<p>The <code>N × N</code> two-dimensional array <code>A</code> should be rotated clockwise by 90°, <strong>without</strong> the use of any additional arrays.</p>\n<p>One way of rotating the two-dimensional array <code>A</code> clockwise by 90° is to transpose <code>A</code>, and then reverse each row of <code>A</code>.</p>\n<p>The transpose of <code>A</code> is formed by turning all the rows of <code>A</code> into columns. For example, the value in the first row and third column (<code>A[1][3]</code>) is swapped with the value in the third row and first column (<code>A[3][1]</code>).<br/><br/>Construct the new algorithm in pseudocode for the method <code>rotate(N,A)</code> described above.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]  </strong></em><br/><em>Award <strong>[1]</strong> for nested loops/loop through all array elements;</em><br/><em>Award <strong>[1]</strong> for checking the array element for the number of colour;</em><br/><em>Award <strong>[1]</strong> for updating correctly each of the array elements;</em></p>\n<p>Example 1:</p>\n<pre>loop for I=0 to N-1<br/>    loop for J=0 to N-1<br/>        if A[I][J]==1<br/>            then A[I][J]=0<br/>            else A[I][J]=1<br/>        end if<br/>    end loop<br/>end loop</pre>\n<p>Example 2:</p>\n<pre>R=0<br/>C=0<br/>I=0<br/>loop while I&lt; N*N<br/>    if A[R][C]==0<br/>        then A[R][C]=1<br/>        else A[R][C]=0<br/>    end if<br/>    C=C+1<br/>    if C&gt;9 then<br/>        C=0<br/>        R=R+1<br/>    end if<br/>    I=I+1<br/>end loop</pre>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]  </strong></em><br/>270 (degrees);</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]  </strong></em><br/>A rotation by 360 degrees returns the image/matrix to its original value, so no action need be taken. A rotation by 360+N degrees is equivalent to a rotation by N degrees(this logic repeats, so for 90 degree rotations, there are only 3 transformations needed: by 90, by 2*90 and by 3*90);<br/>making more is unnecessary/inefficient;</p>\n<p>Unnecessary calls to the method <code>rotate()</code> which would make the algorithm less efficient are avoided;<br/>for example, repeating 10 times and repeating 2 (=10 mod 4) times, both return the array holding the image rotated by 180 degrees;</p>\n<p><em><strong>NOTE</strong>: Accept any appropriate example, with any value of K which is greater than or equal to 4</em>;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]  </strong></em><br/><em>Award <strong>[1]</strong> for completely correct nested loops;</em><br/><em>Award <strong>[1]</strong> for correctly determining row indexes (in both matrixes, A and B);</em><br/><em>Award <strong>[1]</strong> for correctly determining column indexes (in both matrixes, A and B);</em></p>\n<p><em><strong>NOTE</strong>: The method heading and return statement may not appear in the answers. Only an algorithm for the method <code>rotate()</code> is requested</em>.</p>\n<p>Example:</p>\n<pre>rotate(N,A)<br/>    // initialize two dimensional array B<br/>    // for example, int[][] B = new int[N][N];<br/><br/>      <strong>loop for I = 0 to N-1</strong><br/><strong>            loop for J = 0 to N-1</strong><br/><strong>                 B[I][J] = A[N-J–1][I]</strong><br/>                    // instead of these array subscripts<br/>                    // the following can be written<br/>                    // <strong>B[J][N-1-I] = A[I][J]</strong><br/>             <strong>end loop</strong><br/><strong>       end loop</strong><br/><br/>       return B<br/>end rotate</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]  </strong></em></p>\n<p>Example:<br/>(Transposes the two-dimensional array and then reverses each row.)</p>\n<p><em>Award <strong>[3]</strong> for transposing.</em><br/><em>Award <strong>[1]</strong> for the correct outer loop;</em><br/><em>Award <strong>[1]</strong> for the correct inner loop;</em><br/><em>Award <strong>[1]</strong> for the correct swap;</em></p>\n<p><em>Award <strong>[3]</strong> for reversing each row of the transposed matrix.</em><br/><em>Award <strong>[1]</strong> for the correct outer loop;</em><br/><em>Award <strong>[1]</strong> for the correct inner loop;</em><br/><em>Award <strong>[1]</strong> for the correct swap;</em><br/><em>Award <strong>[1]</strong> for using nothing but a temporary variable to achieve this / no additional / extra space used except one temporary variable;</em></p>\n<p><em><strong>NOTE</strong>: The method heading and return statement may not appear. Only the algorithm for the method <code>rotate()</code> is requested</em>.</p>\n<pre>rotate (N,A)<br/>                       //transposes A<br/>     loop for I=0 to N-1<br/>          loop for J=0 to I-1 <br/>             TEMP = A[I][J]<br/>             A[I][J] = A[J][I] <br/>             A[J][I] = TEMP<br/>          end loop<br/>     end loop<br/>                     // reverses each row of transposed matrix A<br/>     loop for I=0 to N-1<br/>          loop for J=0 to N div 2-1<br/>             TEMP = A[I][J]<br/>             A[I][J] = A[J][I]<br/>             A[J][I] = TEMP<br/>          end loop<br/>     end loop<br/>     return A<br/>end rotate</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was answered well.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was well answered with many gaining full marks.</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was well answered with many gaining full marks.</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Answers to this question vary from poor to excellent. Some candidates constructed excellent algorithms and they gained full marks on this question.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Answers to this question vary from poor to excellent. Some candidates constructed excellent algorithms and they gained full marks on this question.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.1.HL.TZ0.13",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"specification\">\n<p>The emergency management information system (EMIS) has a multitier architecture.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> functions of the Data Tier of the EMIS.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics of a TCP/IP Socket.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for any valid function up to <strong>[2 max]</strong></em>.<br/>Stores/retrieves/searches/edits the data;<br/>Encapsulates/holds the code for the database (accept DML / SQL or any other language);<br/>Abstracts/Simplifies access to the databases;<br/>Personalises the SQL for different databases (e.g. contains optimised queries for Oracle, MySQL etc.);<br/>Manages the transactions (anything relating to ACID);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for any characteristic up to <strong>[2 max]</strong></em>.<br/>Binding anything relevant (e.g. nodes, port, IP);<br/>Associating a socket with a socket address;<br/>Associated with the IP address and a port number for the local node;<br/>IP address;<br/>Port number;<br/>Sending/receiving data (packets);<br/>Node-to-node communication;<br/>Works at OSI levels 3, 4, and 5 (Accept any from L3 - Network, L4 - Transport, L5 - Session);</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "19M.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"specification\">\n<p>Tania is considering purchasing an off‑the‑shelf emergency response system rather than a bespoke system as an implementation option in the proposed EMIS.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Other than cost, explain <strong>one</strong> advantage of purchasing an off‑the‑shelf emergency response system rather than a bespoke system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Other than cost, explain <strong>one</strong> disadvantage of purchasing an off‑the‑shelf emergency response system rather than a bespoke system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>two</strong> advantages of using URL rewriting as a strategy for making the HTTP connection stateful.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for any advantage up to <strong>[2 max]</strong></em>.<br/>Tried and tested;<br/>So, less risk;</p>\n<p>Regularly updated;<br/>Therefore, contains fewer errors;</p>\n<p>More tech support;<br/>e.g. Online chat systems.</p>\n<p>Usually available for various platforms / operating systems;<br/>Therefore, fewer compatibility issues, or can be run on different HW and SW;</p>\n<p>Shorter timeframe for implementation;<br/>Which is important in the context of saving lives/obtain benefits more quickly;</p>\n<p>Books/Training material more accessible;<br/>Easier to train staff on using the system;</p>\n<p>Reviews and feedback exist already for the product;<br/>Therefore, the quality of the final product can be known in advance;</p>\n<p>Off-the shelf is likely to implement common standards;<br/>Whereas, you would need to program compatibility in a bespoke system/to work with a multitude of devices;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for any disadvantage up to <strong>[2 max]</strong></em>.<br/>Modifications are not possible without help of developers;<br/>Source code is not usually available to change;</p>\n<p>Compatibility/interfaces with existing systems;<br/>It may not be possible to connect / share data with systems already in use;</p>\n<p>Standard operating procedures may need to change;<br/>To fit the software capabilities/functions;</p>\n<p>Software may contain unused features;<br/>That is potentially confusing for users;</p>\n<p>Software is designed for general requirements;<br/>It may not include features needed by Bangbai;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong></em> for explaining what URL-rewriting is in the context of stateful connection and allow <strong>[1]</strong> for an example or a comparison with session cookies</p>\n<p>URL Rewriting changes the URL to include parameters<br/>e.g. www.bangbai.com?userId=Asdf34e3</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Works without cookies/cookies disabled/as an alternative to cookies;<br/>URL re-writing will still work as it embeds the session ID inside the request;</p>\n<p>URL Rewriting does not result in anything being saved (e.g. cookies) on the client side;<br/>Therefore, the privacy of the client cannot be compromised (e.g. By searching contents of the cookie folder later);</p>\n<p>May work with legacy devices/devices without access to cookies;<br/>That are not able to use cookies/do not have access to an alternative to URL re-writing;</p>\n<p>Makes the links more descriptive/readable;<br/>Which will help the developer resolve issues (accept example, such as server identified in URL);<br/>(<em>Note to examiners: descriptive cannot be awarded a second mark if not related to the scenario e.g. search engines is not correct</em>)</p>\n<p>Allows the details of the identification of the user to be recorded in log files;<br/>Which will help for future analysis/reviews/data mining;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "19M.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Some citizens have used the citizen reporting function of the app through a VPN.</p>\n<p>Explain why the use of a VPN would reduce the effectiveness of the citizen reporting app.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each point that explains why the use of VPN reduces the effectiveness of the app up to <strong>[6 max]</strong></em>.</p>\n<p>A VPN hides the true IP address;<br/>Instead, it provides the IP address of the VPN’s server;<br/>A VPN adds an additional layer of complexity has been added;<br/>Which may increase the likelihood of the connection dropping;<br/>A VPN encrypts/tunnels the data which is transmitted;<br/>Which has a potential overhead (more computational power/increases the size of data packets) and this may reduce emergency responses times;<br/>A VPNs may log traffic that passes through;<br/>This may present a potential security concern if sensitive information is reported;<br/>A VPN may employ a filtering policy/blocks ports;<br/>So certain parts of the app may not work (e.g. VOIP protocols may not work);<br/>If GPS/location service is deactivated, then the system is reliant upon IP Address;<br/>Location information can be obtained from the GPS/GPS is sent in data packets;<br/>However, it may not be as easily verifiable by comparing with a known IP location;<br/>Not having the true IP address would mean that the EMIS server would not have any indication of the location of the source of the report;<br/>VPN usage may give users a feeling of anonymity which may encourage misuse;<br/>Which may divert resources to the hoaxes instead of authentic reports;</p>\n</div>",
    "Examiners report": "",
    "question_id": "19M.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Rahul has suggested that the use of the citizen reporting function of the app might be much higher than initially requested and this could affect the emergency assistance function if they share the same server.</p>\n<p>As a result of this, Rahul has decided to put citizen report on a separate cluster of servers and use a load balancing algorithm to manage the workload of the server.</p>\n<p>Evaluate the appropriateness of the following load balancing algorithms for use in the Bangbai EMIS:</p>\n<ul>\n<li>client side random</li>\n<li>source IP Hash.</li>\n</ul>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>The answer could include the following</em>:</p>\n<p><strong>Definitions</strong>:<br/>The source IP hash combines the source and destination IP address to generate a hash key, which is then assigned to a specific server. The benefit of this approach is that a client who experiences a dropped connection can be returned to their session with the same server.<br/>The client-side random load balancing algorithm delivers a list of server IPs to the client, which then selects a server IP at random.</p>\n<p><strong>Security</strong> <br/>As Client Side Random allows control of the choice of server to be made by the client it would leave the system much more vulnerable to DOS/DDOS attacks.</p>\n<p><strong>Persistence</strong> <br/>If disconnected, Client Side Random may allocate a new server to the client as it will choose randomly from the list. However, if the IP is the same, then Client-side Hash will pair the client with the previous server. This may mean that the work being done before is not lost (e.g. Client continues from where she left of seamlessly).</p>\n<p><strong>Distinguishing between users</strong> <br/>Client-Side Hash is affected when a single IP address is used by many users (e.g. proxy servers sharing a connection, VPNs etc.). A single user might also disconnect and re-connect with a different IP address due to DHCP so would be seen as a “different” person.</p>\n<p><strong>Cost</strong> Client Side Random is a cheaper solution as it doesn’t require any expensive hardware on the server side (i.e. the hardware load balancers etc.).</p>\n<p><strong>Single Point of Failure (SPOF)</strong><br/>Client Side Random eliminates the SPOF as all requests do not depend on one device (i.e. the hardware load balancer provided by the system). Similarly, it avoids <strong>bottlenecks</strong> if the load balancer is overloaded.</p>\n<p><strong>Control</strong><br/>When using client side random, Bangbai Government is essentially losing control over the load balancing process. If for some reason they suddenly want to re-direct all queries away from certain servers (e.g. It’s been compromised, or the data centre has a fire etc.) they can’t, as they cannot easily/quickly change the algorithm on each of the client devices.</p>\n<p><strong>Intelligence/Adaptiveness/Flexibility</strong><br/>Client Side Random may not take into consideration privileged information about the servers, which it does not have access to. Therefore, it cannot decide which of the servers is *currently* best suited to handle a specific type Such information may include:</p>\n<ul>\n<li>Server Resources are different (Primary Memory, Secondary Memory, Processor)</li>\n<li>Deployment of the services to different servers (Server A and B deal with video reports, Server C with simple text/photo reports etc.).</li>\n<li>Current load of each server (Server B has more connections than server A)</li>\n<li>Current Health of each server (Server C just died)</li>\n</ul>\n<p><strong>Additional Research</strong><br/>Caching can affect a lot of load balancing algorithms as they use DNS to allocate the next server. This can skew the allocation of servers, but because Client-side Random uses a randomly generated server from a simple list, it avoids this problem.<br/>The HTTP/2 specification, which is now supported by every major browser, has built in support for Client-Side Load Balancing. The citizen reporting app can benefit from this as it uses HTTP for the REST API.</p>\n<p><strong>Overall comparison/evaluation</strong><br/>The main issue with the source IP hash load balancing algorithm is that each change can redirect EVERYBODY to a different server, which again lags the time of response for a citizen call in Bangbai.<br/>That is why some good load-balancers have implemented a consistent hashing method to ensure that if a server fails, for example, only the client connected to this server are redirected and not all.<br/>The counterpart of consistent hashing is that it doesn’t provide a perfect hash, and so, in a farm of 3 servers, some may receive more clients than others and this can take a toll on the time of response to citizen calls.<br/>When a failed server comes back, its users (determined by the hashing done on the Source IP) will be redirected to it again.<br/>There is no overhead in terms of CPU or memory when using such an algorithm.</p>\n<p><strong>Conclusion</strong><br/>A final measured conclusion that links together the various points and recommends one or both of the algorithms as appropriate for the needs of Bangbai.</p>\n</div>",
    "Examiners report": "",
    "question_id": "19M.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics of a digital signature.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> stages in the proof-of-work process.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Award <em><strong>[2 Max]</strong></em><br/>Anonymity (signature key identifies the user but not their real name).<br/>Asymmetric (cryptography);<br/>Certificate authority stamp;<br/>Collision resistant;<br/>Data / transaction / message;<br/>Deterministic;<br/>Hash / bitstring;<br/>Non-invertibility;<br/>Non-repudiation;<br/>Private key / Secret key<br/>Public key / key generation;<br/>Randomness (for Private key generation);<br/>Secure / encryption;<br/>Signature function / signing algorithm;<br/>Timestamp (sometimes);<br/>Unique.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Award <em><strong>[2 Max]</strong></em><br/>Candidate block is formed from transactions in the pool<br/>Nonce is generated (based on difficulty level);<br/>Miners try to find the nonce / Hash generated and compared to target / mathematical calculations / solving the problem;<br/>Puzzle solved / Hash smaller than target;<br/>Broadcast to distributed ledgers;<br/>Other miners verify/validate;<br/>Block added;<br/>Reward is issued to successful miner;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21M.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a Merkle tree stores the hash addresses for the blockchain technology.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the residents of Santa Monica may be concerned by the lack of a central authority to manage MONS transactions.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Award <em><strong>[4 Max]</strong></em><br/>A transaction is hashed and added (as a leaf node);<br/>A pair of leaf nodes are combined (hashed) to form the hash of a parent;<br/>When the number of nodes on the same level is odd the rightmost node is copied to the parent (For example, 10, 5, 3, 2, 1);<br/>This is repeated (recursively) until the Merkle root node is created;<br/>The root node hash is created to represent all of the hashes of the nodes;</p>\n<p><em>Award marks for a diagram that shows the steps</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Award <em><strong>[4 Max]</strong></em><br/>Award <strong>[2 max]</strong> for a Governance technology concern with example and a further [<strong>2 max] </strong>for expansion and consequence.</p>\n<p><strong>Technology example answer 1 [4 marks]</strong><br/>Concerns that there is no way to correct errors;<br/>A transaction sent to the wrong person;<br/>Relies on the receiver’s honesty to return the funds;<br/>But difficult to identify the receiver from their signature;</p>\n<p><strong>Technology example answer 2 [4 marks]</strong><br/>Residences may be concerned that errors or mistakes won’t be corrected;<br/>Their wallet password may be compromised and transactions made from their account;<br/>They would need to put in a request to Pablo (Santa Monica mayor) to trace the transactions;<br/>Pablo (Mayor) is unlikely to act since it isn’t a major breech on the blockchain;</p>\n<p><strong>Technology example answer 3 [4 marks]</strong><br/>The MONS currency may be subject to a 51% attack;<br/>With no central authority to roll back the blockchain;<br/>Double spend problem will occur;<br/>Currency would lose credibility/residences may stop using it.</p>\n<p><strong>Technology example answer 4 [4 marks]</strong><br/>Residences are trusting that the software is designed well and secure;<br/>Since it is open-source they may be concerned that hackers will find a flaw;<br/>With no central authority to manage the blockchain, which is distributed;<br/>There could be concerns that necessary updates/patches won't get rolled out.</p>\n<p>Award <strong>[2 max]</strong> for a Governance non-technology example</p>\n<p><strong>Non-technology example answer 1 [2 marks]</strong><br/>Human nature to trust in authority;<br/>When there is no Governance there may be irrational concern;<br/>They may worry about the currency's value going down;<br/>Because there is no way to influence the exchange rate;</p>\n<p><strong>Non-technology example answer 2 [2 marks]</strong><br/>The MONS currency could be abused by the criminal element;<br/>It may be used for money laundering / fraud / illegal payments;<br/>The anonymity of digital signatures makes this possible;<br/>No central authority to prevent this;</p>\n<p><em>Do not award marks for saying how to overcome concerns</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21M.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Two features of hashing are <em>determinism</em> and <em>non-invertibility</em>.</p>\n<p>Explain how <em>determinism</em> <strong>and</strong> <em>non-invertibility</em> are used to facilitate the use of the blockchain.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Award <em><strong>[6 Max]</strong></em></p>\n<p><strong>Determinism</strong>:<br/>The hash algorithm must always generate the same output for the same input;<br/>when replicated on different nodes;<br/>Otherwise, the consensus cannot be reached;</p>\n<p>Without determinism different miners would get different results;<br/>And there would be no consensus;<br/>So, the distributed ledger would be different for every node;</p>\n<p>Without determinism one block could not link to the next / prevHash;<br/>Therefore, transactions could be edited / blockchain would not be immutable;<br/>The blockchain would be insecure;</p>\n<p>Merkle tree and the block are validated many times by miners;<br/>Without determinism the Merkle proof won’t work;<br/>So, transactions can’t be validated;</p>\n<p>Without determinism, when a sender signs a transaction, other nodes on the blockchain wouldn’t be able to be certain it was them;<br/>Which may result in an increase in illegal payments and corruption;</p>\n<p><strong>Non-invertibility</strong>:<br/>A public key is hashed to provide a unique encrypted public address that can be published;<br/>Whereas private keys are not publishable and can be hashed (needed to access the wallet);<br/>Non-invertibility guarantees that publishable information is protected, and separated from private key;</p>\n<p>Prevents the private key from being calculated if you have the public key;<br/>Non-invertibility/secure private key prevents false transactions being made;<br/>So that “digital signatures” can authenticate the person who made the transactions;</p>\n<p>If the nonce used an invertible hash it could be calculated;<br/>So, it would be solved quickly / difficulty level could not be set;<br/>And fake branches could be created / double spend could occur;</p>\n<p><em>Mark as 3 and 3</em>.<br/>Award <strong>[1 max]</strong> for definition + <strong>[2 max]</strong> for explanation, for both sections (determinism and non-invertibility)</p>\n</div>",
    "Examiners report": "",
    "question_id": "21M.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Pablo has claimed that the use of blockchain technology for the MONS cryptocurrency will mean the cryptocurrency is both secure <strong>and</strong> scalable.</p>\n<p>To what extent do you agree with Pablo?</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Answers may include:</p>\n<p><strong>Security features</strong></p>\n<ul>\n<li>All transactions are recorded into files called blocks.</li>\n<li>Each block contains a hash of the previous block as well as some transactions.</li>\n<li>Every transaction is visible to everyone, which makes it difficult to change existing data which may be replicated on thousands of computers (decentralization).</li>\n<li>Any change to any historic transaction would be noticeable because the hashes of all subsequent blocks would not agree.</li>\n<li>Transactions are confirmed many times (consensus control).</li>\n<li>The more users of MONS there are, the more likely that there will be additional miners, which will increase the security of the network.</li>\n<li>A proof of work is required when creating a new block, which makes the effort required to falsify many blocks unfeasible (intractable).</li>\n<li>Each user has his own private key which is unknown to anyone else, as well as a public key (cryptography).</li>\n<li>With no central authority there is no focus point for hackers to attack.</li>\n<li>As MONS uses a private blockchain then only verified and approved computers could mine;</li>\n<li>The larger and more distributed the network is, the safer it is considered to be.</li>\n</ul>\n<p><strong>Security concerns</strong></p>\n<ul>\n<li>Ledgers are technically not immutable (but to do so would require unfeasible computing power and taking over &gt;51% of the network within the space of 10 minutes (ie a 51% attack).</li>\n<li>The 51% attack is more likely to be successful on a small private blockchain rather than a large public one.</li>\n<li>Attacks on cryptocurrencies have been documented (reference opportunities).</li>\n<li>These attacks related to access to wallets (obtaining private keys).</li>\n<li>Currency transfer websites are a target and have been hacked with cryptocurrencies stolen.</li>\n<li>With no central control it is difficult to rollback transactions.</li>\n<li>DDoS attacks on cryptocurrency services may slow transactions slightly/may affect MONS value.</li>\n<li>Future concerns have been expressed about the scalability of the blockchain, lack of standards, and how it can be used if laws on data privacy become tighter.</li>\n</ul>\n<p><strong>Scalability</strong></p>\n<ul>\n<li>Scalability is defined as the capacity for a system or network to grow in size to manage increased demand.</li>\n<li>Thus, scalability refers to the ability of the MONS currency to continue to function when the number of transactions increases.</li>\n<li>Number of miners are likely to increase in proportion to the number of transactions because there are more rewards.</li>\n<li>A peer-to-peer network makes scalability possible because it is easy to add new nodes.</li>\n<li>A peer-to-peer network is unlikely to bottleneck with increased transactions.</li>\n<li>An increase in processing speeds for miners causes the nonce difficulty level to adjust and allows the blockchain to function (regardless of the number of miners).</li>\n<li>Due to the limitations of proof of work, blocks should take a minimum time (e.g. 10 minutes) to create and should hold in the region of 1MB of data, which is about 1000 to 3000 transactions.</li>\n<li>Other consensus methods can be used to increase scalability at a slight cost of decentralization, e.g. using proof of stake, proof of ownership, instead of PoW;</li>\n<li>However, if the network becomes heavily congested, a MONS currency transaction might take longer to be processed.</li>\n<li>If a network is running slowly, the mining fees have to increase or there is a risk that miners will stop mining.</li>\n<li>These increased fees will need to be paid by the users making a cryptocurrency transfer. Small cryptocurrency transfers may incur high charges making the currency undesirable.</li>\n<li>Blockchain ledgers are more scalable than centralized ledgers because they are distributed and there is no bottleneck.</li>\n</ul>\n<p><strong>Strategies to improve scalability include</strong><br/>Increasing the block size to include more transactions.</p>\n<ul>\n<li>The initial reason behind block size limits is to prevent Denial-of-Service (DOS) attacks by hackers seeking to create huge (or infinite) blocks that would harm and paralyze the blockchain.</li>\n<li>Increasing the block size to cope with increased demand may place the MONS currency at a risk of attack.</li>\n</ul>\n<p>Modifying the underlying protocol (although the issue with this is that there needs to be some central governance to make these decisions).</p>\n<ul>\n<li>The code is open source and a consensus (proposal and voting on forums and discussions boards) would be need to change the protocol.</li>\n<li>Since its creation there have been very few changes to the bitcoin source code so is unlikely that MONS blockchain code would be open to frequent changes.</li>\n<li>In Santa Monica, the MONS project would need some form of governance so that decisions to change the protocol and improve scalability could be made.</li>\n</ul>\n<p>Sharding and other “Layer 2” solutions can be implemented to reduce congestion on blockchains, in which slower transaction types are put on a different chain which deals only with that type.<br/>For example, Lightning networks, which is a layer 2 payment protocol designed to be layered on top of a blockchain-based cryptocurrency.</p>\n<p>Security could be optimized according to the \"Blockchain Trilemma\".</p>\n<p><strong>Scalability concerns</strong></p>\n<ul>\n<li>The lack centralized servers means that there is no control over the amount of hardware that can be added to the system (i.e. if centralized, scalability could be improved by the central authority by adding more processing power).</li>\n<li>Making decisions / governance of a distributed ledger is more difficult and has to rely on voting and an agreement before proceeding.</li>\n<li>The increased network traffic of many nodes on the blockchain can have a greater overall requirement for network bandwidth than (for example) centralized miners.</li>\n<li>Scalability in distributed ledger systems such as blockchain depends on the participation of the whole network, whereas centralized ledger systems (for example VISA) which depend only on the service provider and can scale quickly, but with hard limits imposed by the infrastructure.</li>\n</ul>\n<p><strong>Conclusion</strong><br/>A reasonable conclusion that includes both security and scalability.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "21M.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: computer science in medicine, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define <em>bioinformatics</em> and give an example of the data used in medical research.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> feature of fuzzy logic which makes it suitable for medical diagnosis.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Bioinformatics is the analysis of biological data by computers;<br/>For example: DNA / patient statistics / results from blood samples, etc. </p>\n<p><em><strong>Note</strong>: Accept any example related to medical research</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for a feature of fuzzy logic and <strong>[1]</strong> for outlining its relevance to medical diagnosis, up to <strong>[2 max]</strong></em>.<br/>Fuzzy logic can put a measure on uncertainty (give an approximate/probabilistic value);<br/>Can express relationships in a probabilistic manner;<br/>Patterns can be found which are not easy to classify with traditional logic;<br/>Work with multi-variable connections;<br/>Can help to give probability of certain diagnoses; </p>\n<p><em><strong>Note</strong>: One symptom may be important only if there are some related ones;</em><br/><em>vagueness and uncertainty of measurements e.g. borderline blood results etc.;</em></p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "17N.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: computer science in medicine, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare ultrasound and a CT scan in the creation of a medical image.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> compatibility issues that might be faced with the introduction of Electronic health records (EHRs) in a large country.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each comparison up to <strong>[4 max]</strong>. Note that the italicised descriptions below are from then case study and will not gain credit unless they are expanded on</em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each issue and <strong>[1]</strong> for each associated outline, for two issues up to <strong>[4 max]</strong></em>.</p>\n<p>Hardware incompatibility;<br/>Different machines making measurements with dedicated processors;</p>\n<p>Software incompatibility;<br/>e.g. different OS where not all function with the electronic records software;</p>\n<p>Different format of records;<br/>Not all centres taking the same measurements;</p>\n<p>Different format of results;<br/>For example temperature “&gt; 40” or temperature “= 40.5”;<br/>Analogue values may be rounded differently;</p>\n<p><em><strong>Note</strong>: Accept other reasonable issues which could occur</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "17N.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: computer science in medicine, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>With reference to the technology involved, explain how augmented reality imaging could be used to assist a surgeon in one country to carry out a complicated operation under the supervision of an expert surgeon in another country.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2]</strong> for a clear description of augmented reality, <strong>[2]</strong> for a clear explanation of how an image could be sent to the expert surgeon, and <strong>[2]</strong> for the role in the operation (<strong>up to</strong> <strong>[6 max]</strong>)</em>.</p>\n<p><em><strong>AR</strong></em>:<br/>Augmented reality projects digital/additional information;<br/>onto a real-time situation/onto the real world;</p>\n<p>Augmented reality allows an actual situation to be complemented in such a way;<br/>that essential information is added to a video image;</p>\n<p>An expert surgeon in another place sees the operation as if they were there/as a video being filmed;</p>\n<p>Tags attached to the affected area to be operated can be overlaid on the image;<br/>Can overlay information on the view they have;</p>\n<p><em><strong>Transmission</strong></em>;<br/>Annotated video data is continually streamed between the two;<br/>Using a camera at the operation end;<br/>In real-time over a (very) secure and fast network;<br/>Viewed by expert surgeon on a screen;</p>\n<p><em><strong>Example of use</strong></em>;<br/>The expert can show on the image what actions should be made (and where);<br/>Allow example;<br/>Using the information being sent back on a headset tablet/smart phone/transparent screen;</p>\n</div>",
    "Examiners report": "",
    "question_id": "17N.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: computer science in medicine, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>A new health centre is planned in a remote mountain area to serve a community which is scattered over a large area. The nearest large hospital with complete medical services is difficult to reach.</p>\n<p>The services to be offered in the new health centre should include:</p>\n<ul>\n<li>health carers at the health centre for visits made by appointment</li>\n<li>expert doctors from other locations, who are available 24 hours a day via a VPN connection</li>\n<li>a comprehensive care system for chronically sick patients at home</li>\n<li>some visiting health workers</li>\n<li>some provision for emergency operations in the health centre</li>\n<li>diagnostic equipment to identify cases where a person needs to be treated at the large hospital.</li>\n</ul>\n<p>The planners are also considering whether to create an internet site with authorized access for the community.</p>\n<p>With reference to the technologies involved, discuss ways in which the required services could be met and their effect on the people in the community.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><strong>Discussion should focus on</strong>:</p>\n<p>“<em>health carers at the health centre for visits made by appointment</em>”<br/>People will need a physical presence at the centre for reassurance which means carers need to be employed and in place – enough to cope with the estimated need especially as visits to the hospital are difficult.</p>\n<p>“<em>expert doctors from other locations, who are available 24 hours a day via a VPN </em><em>connection</em>”<br/>Essential for crises which occur outside of the hours the centre is open.<br/>Will need a secure and reliable connection with access from mobile device, and access to any data and phone calls coming into the centre.</p>\n<p>“<em>a comprehensive care system for chronically sick patients at home</em>”<br/>Continual measurements can be sent to the health centre; this could be done with wearables. Someone with a heart problem could have the heart rate continually measured on a bracelet with an imbedded RFID to communicate wirelessly via NFC to a receiving device which sends the data automatically to the health centre.<br/>The centre would need to have software to interpret the data (or this could be done at the patient side – therefore less to send) and any fluctuations identified.<br/>Danger signals would mean the doctor needs to know. Health carer would check on minor changes as well as regular check-ups – need for health carers depends on proportion of chronically sick and elderly.<br/>Effect on population – good system to stay at home may be met with some aversion to remote monitoring.</p>\n<p>“<em>some provision for emergency operations in the health centre</em>”<br/>Could be dealt with by telesurgery when essential but will need appropriate cameras/connections and software, as well as a doctor who is confident to be involved.</p>\n<p>“<em>diagnostic equipment to identify cases where a person needs to be treated at the large hospital</em>”<br/>An ultrasound scanner should probably be purchased. For example, regular scans are needed for pregnant women. However, these need expert technical operators and interpreters. One suggestion would be for the scans to take place via telemedicine and the results sent electronically to be analysed at the hospital.</p>\n<p>An internet site for communication and advice would have the advantages of knowledge so that a decision whether or not to make an appointment can be made. Accounts would need to be set up and security ensured via firewalls, encryption etc. as personal medical information is sensitive. It would be helpful to give information as to when an appointment should be made and simple remedies for simple problems. Some people may be suspicious of using this medium for medical uses and be concerned about privacy.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "17N.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> smartphone resources that might limit the user experience of the Bangbai app.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why an application programming interface (API) would be useful when developing a smartphone app which uses the CAD system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each smartphone resource identified up to <strong>[2 max]</strong></em>.</p>\n<p>Screen size;<br/>Memory;<br/>Processor speed;<br/>Available primary or secondary storage;<br/>Battery life;<br/>GPS Receiver;</p>\n<p><em>Do not accept any resources that are obviously on the server side</em><br/><em>Do not accept software as a resource</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying why an API may be useful and <strong>[1]</strong> for a brief elaboration up to <strong>[2 max]</strong></em>.</p>\n<p>An API ensures abstraction;<br/>as the user only how the service is requested, how the service is implemented;</p>\n<p>An API acts as a “contract” or definition of the services of the CAD system;<br/>which makes it clearer to client application developers and back-end developers;</p>\n<p>The API would ensure arguments (parameters) are passed to the system;<br/>in the correct order and type);</p>\n<p>An API would make sure that the app is expecting a specific data type;<br/>which will be returned from a CAD request/service;</p>\n<p>An API provides a consistent method/protocol/interface for building a resource;<br/>and thus, reduces the amount of programming required/provides structure.</p>\n<p>An API would allow the smartphone app and the CAD system to be “compatible”;<br/>can be easily updated if the situation changes;</p>\n<p>An API ensures authentication/authorisation/security checks are carried out;<br/>before allowing operations to be done;</p>\n<p>Using an API would allow server to be more secure;<br/>as unused ports and binary/executables/programs/scripts could be hidden from users. (i.e. Only the API handler would be accessible, not the program modules);</p>\n<p>Using an API would allow changes to be coordinated between all users;<br/>and backward compatibility when the app is upgraded;</p>\n<p>An API standardises the operations available across different clients/devices;<br/>and the level of service would not be as device dependent;</p>\n<p>An API would allow the server to track usage of individuals (through keys);<br/>To identify when certain users were overusing services;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "19N.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Some people have asked if the informing function of the Bangbai app could allow the anonymous reporting of incidents and issues. They believe that potentially useful information (for example, information about criminals) might not be reported because people are afraid of their identities being discovered.</p>\n<p>To what extent might it be technically possible for the Bangbai app to guarantee the anonymity of its users if this feature was approved?</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>The answer could include the following</em>:</p>\n<ul>\n<li>It is very difficult to guarantee anonymity as so many technologies and levels of logging are involved.</li>\n<li>Encryption can be used to make the contents of the communication inaccessible (or at least much more difficult to access).</li>\n<li>Information such as IP address, can be used to trace somebody but would need the cooperation of an ISP etc.</li>\n<li>IP Addresses are logged at various different levels: Client OS, ISP, routers on the internet (many of them), host webserver (in a weblog file). The server could be set up to not log this information.</li>\n<li>Logging of IP addresses by the ISP, local router at client’s point of origin and routers all along the internet cannot necessarily be controlled or secured to prevent them logging accesses.</li>\n<li>MAC addresses that identify the specific device used are logged by switches.</li>\n<li>VPNs or proxy servers could be used to make the users’ origin difficult to obtain.</li>\n<li>IP addresses can be shared between many different computers in one area/organization, using NAT (network address translation) so might not uniquely identify a person.</li>\n<li>The client device itself could be compromised with key loggers, spyware etc. and therefore even if all transmission risks were removed, someone could still get access to the information and source.</li>\n<li>Various products/services/protocols exist which try to make anonymity easy (such as Tor browser) but this is still an area that is developing</li>\n<li>If the servers are vulnerable to physical attack, then people could forcefully enter the hosting facility and install monitoring software or steal data. Sufficient security, biometric access locks etc. could help to prevent this.</li>\n<li>If the data is backed-up, then this historic data should be anonymised or destroyed.</li>\n<li>How ethically correct is it for the providers of the emergency services to receive what could be hoax calls and have no way of knowing themselves who has made them. Could this be counterproductive and therefore allow people to drain emergency resources and potentially send police to unnecessary operations? The equivalent to this online service would be the “confidential telephone” services available by the police in several countries, but these are not anonymous either (though they don’t necessarily pass through an open communications platform like the Internet).</li>\n</ul>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "19N.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics of a peer-2-peer (P2P) network.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> sources of entropy. </p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Many clients connect <span style=\"text-decoration:underline;\">directly</span> to many other clients;<br/>Clients also act as servers;<br/>No centralized administration for the network/decentralized network;<br/>Network is more resilient to failure;<br/>Is more scalable than a client server network;<br/>There are no \"bottlenecks\" as with Client Server model; </p>\n<p><em><strong>Note</strong>: Do not accept “widely used for illegal file sharing” (or similar)</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Answers may include</em>:</p>\n<p>atmospheric noise;<br/>radioactivity;<br/>thermodynamics;<br/>Brownian motion of particles;<br/>mouse movements;<br/>algorithmic sources;</p>\n<p><em><strong>Note</strong>: Do not accept “Entropy can be obtained from physical sources” as this is clearly stated in the case study. A physical source must be stated (e.g. radioactivity) to get that mark</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "20N.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how a smartphone uses data from GPS satellites to obtain its location.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Rahul has stated the importance of a multi-tier architecture using standard protocols. Explain why this approach is important.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <em><br/>Award<strong> [1]</strong> for identifying part of the process that a smartphone uses to obtain its location up to <strong>[4 max]</strong></em>.</p>\n<p>Trilateration;<br/>Uses signal strength and latency based on very accurate atomic clock;<br/>Complex formula taking into consideration the curvature of the earth to arrive at latitude, longitude and altitude;<br/>The smartphone uses the distance between satellites to create overlapping “spheres” that intersect in a circle (which is the phones location);<br/>When fewer satellites are available the accuracy diminishes and may be specified to within a radius only;<br/>Assisted/augmented GPS can help accuracy by adding location information received from or about WIFI networks and the triangulation of cell towers;<br/>The smartphone depends on data received from GPS satellites, but that can be obstructed or distorted by obstruction (e.g. tall buildings), weather or water;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>The answer could include the following</em>:</p>\n<p><strong>A multi-tier approach for developers provides</strong>:<br/>Abstraction of layers;<br/>Less complexity;<br/>Faster development time;<br/>Easier testing;<br/>Work can be divided better amongst teams;<br/>Performance – system can be divided and allocated to many processors/nodes;<br/>Easier addition of clients, databases or logic which does not “break” other layers;</p>\n<p><strong>The use of standards and protocols provides</strong>:<br/>Future proofing;<br/>Interoperability;<br/>Communication with legacy devices;<br/>Compatibility with best of breed 3rd party products (e.g. firewalls, load balancers, Quality of Service and network monitoring software etc.).<br/>Quick patching of discovered errors, exploits etc.;<br/>Standards and protocols may already be implemented and have available code references or libraries which would reduce development time etc.;<br/>Security updates and upgrades from the community; </p>\n<p><em><strong>[1–2 marks]</strong></em><br/>A limited or superficial response that indicates a basic understanding of one or both approaches. Uses little appropriate subject specific terminology.</p>\n<p><em><strong>[3–4 marks]</strong></em><br/>A competent explanation of the importance of one or both approaches. There is appropriate use of subject specific terminology throughout the response.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "19N.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the steps that need to be carried out by the blockchain system to find a user’s current MONS balance.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Dolores states, “one of the great things about the blockchain is that we can ensure that the solution time remains at 10 minutes, and we can do this even as the number of MONS miners increases” (lines 63–65).<br/><br/>Explain why it is important to ensure the solution time remains at 10 minutes.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Balances are not stored in the blockchain/calculated in real time;<br/>A digital signature/hash address is used to identify the user;<br/>An algorithm must go through the entire blockchain examining all transactions that match the signature/address;<br/>Relevant transactions debits and credits are added to calculate the balance;<br/>Third party services are available to monitor the blockchain and provide a balance for the specified addresses;<br/>It is likely that the wallet (app) will regularly compute the balance by verifying all the MONS it contains, but to do a complete confirmation could take a long time (e.g. 10–60 mins on bitcoin);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Proof of work delays the creation of new blocks and therefore avoid spamming and/or instant re-writing of the blockchain;<br/>A certain amount of time is required to make sure that there are not too many blocks mined;<br/>If mining takes much longer than 10 minutes, it may discourage miners from mining<br/>If mining takes much longer than 10 minutes it may make cryptocurrency transactions too slow;<br/>Miners are required by the network so therefore a balance must be found which takes miners with standard hardware the correct time to solve;<br/>Complexity of the PoW (and therefore time required) can be altered by changing the nonce value used to create a valid hash;<br/>Difficulty of PoW can be adjusted so that miners take that time to create blocks;<br/>if the number of miners rises too much so the time drops, the reward can be decreased to try to reduce the attractiveness of mining;<br/>Many cryptocurrencies (e.g. Bitcoin) modify the difficulty of their PoW to attract miners while maintaining the minimum time for a candidate block hash to be generated;</p>\n<p><em><strong>Note</strong>: Do not accept “adding more (powerful) GPUs”, as this will simply increase the chance of a particular miner solving the proof of work first, not the difficulty of the task for all miners</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "20N.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a new computer aided dispatch system for Bangbai, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>It is expected that at any given time during a normal day, there will be between 50 and 100 members of the public interacting with each individual server in the cluster. If the network card in one of the servers breaks while being used, it is very likely that the users connected to it would still be able to complete their current operations without any inconvenience.</p>\n<p>Explain why this hardware problem would not seriously affect the users who are currently connected to that particular server.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>The answer could include the following</em>:</p>\n<p><strong>Redundancy</strong>:</p>\n<ul>\n<li>There is another network card in the server which is waiting to take the role of the first.</li>\n<li>The user session information is replicated onto another server so that the other server can take over the operation of that client, should the first one be completely unavailable (e.g. If there is no redundant network card).</li>\n</ul>\n<p><strong>Failover</strong>:</p>\n<ul>\n<li>At the point failure, the OS realises that the network card is not working and automatically switches to the redundant network card to continue conversation with the client.</li>\n<li>At the point of failure, the load balancer/application server/partner server realises that the server is no longer reachable and from that point onwards all requests by the owner of that session are directed to the server which contains a replicated copy of the original session. The session is then replicated to a third machine.</li>\n</ul>\n<p><strong>Replication of session information</strong>:</p>\n<ul>\n<li>The information contained in the session of a user can either be stored only in one node of the cluster (sticky sessions), or it can be stored in shared memory or secondary storage accessible by the whole cluster. There are performance overheads, because if the session information is constantly changing, or it has to be accessed with each request then it would usually be much faster to keep it in primary memory in one server (sticky sessions) to which the user is always directed by the load balancer / application server.</li>\n</ul>\n<p><em><strong>[1–2 marks]</strong></em><br/>A limited response that indicates very little understanding of the topic or the reason is not clear. Uses little or no appropriate subject specific terminology. No reference is made to the scenario in the stimulus material. The response is theoretical and descriptive.</p>\n<p><em><strong>[3–4 marks]</strong></em><br/>A superficial explanation of why this hardware failure would not seriously affect the users who are currently connected to that particular server. There is some use of appropriate subject specific terminology in the response.</p>\n<p><em><strong>[5–6 marks]</strong></em><br/>A thorough explanation of why this hardware failure would not seriously affect the users who are currently connected to that particular server. Explicit and relevant references are made to the scenario in the stimulus material. There is appropriate use of subject specific terminology throughout the response.</p>\n</div>",
    "Examiners report": "",
    "question_id": "19N.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Critics have complained about the potential environmental effects caused by the computing resources required by a blockchain network.</p>\n<p>Analyse the potential effects that the use of MONS could have on the environment.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><strong>Negative impacts on the environment</strong>:<br/>Miners which work on the network require energy to process proof of works;<br/>Farms of miners will need energy for cooling systems such as AC, which have negative environmental results (e.g. greenhouse gases);<br/>The use of computers to maintain the network may result in e-waste, which would be detrimental to the environment;<br/>The mining of raw materials for electronic products (e.g. silicon, aluminum, copper, lead, and gold) can damage natural habitats for animals/pollutes water;</p>\n<p><strong>Positive/Mitigating factors</strong>:<br/>Renewable energy may be used (solar energy is used by Bitcoin Miners now)<br/>Low power specific computational devices (ASICS) are now widely used which reduces power consumption;<br/>As miners aim to make a profit, they will always use the cheapest source of energy and therefore prefer solar etc.;<br/>A moving towards a proof of stake rather than a proof of work would reduce the amount of processing required, therefore reducing cost and environmental damage;<br/>Moving away from physical currency would reduce cotton/paper/plastic production;<br/>The new computing machinery would replace the older physical technology (banks, counting machines, printing, etc.);</p>\n</div>",
    "Examiners report": "",
    "question_id": "20N.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: a local economy driven by blockchain, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Pablo states: “In a traditional banking system, users trust the banks to keep everyone’s money safe; but with MONS, the whole blockchain, right from the very first transaction, would be visible to all MONS users, so it is important to be able to explain to citizens how their money is guaranteed to be safe” (lines 109–112).</p>\n<p>With reference to the key technologies, to what extent do you believe the MONS project will ensure the safety of the residents’ money?</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[12 max]</strong></em>.<br/><em>Answers may include</em>:</p>\n<p><strong>Security features</strong><br/>All transactions are recorded into files called blocks.<br/>Each block contains a hash of the previous block as well as some transactions.<br/>Every transaction is visible to everyone, which makes it difficult to change existing data which may be replicated on thousands of computers (decentralisation).<br/>Any change to any historic transaction would be noticeable because the hashes of all subsequent blocks would not agree.<br/>Transactions are confirmed many times (consensus control).<br/>The more users of MONS there are, the more likely that there will be additional miners, which will increase the security of the network.<br/>A proof of work is required when creating a new block, which makes the effort required to falsify many blocks unfeasible.<br/>Each user has his own private key which is unknown to anyone else, as well as a public key (cryptography).<br/>With no central authority there is no focus point for hackers to attack.<br/>As MONS uses a private blockchain then only verified and approved computers could mine.<br/>The larger and more distributed the network is, the safer it is considered to be.</p>\n<p><strong>Security concerns</strong><br/>Ledgers are technically not immutable (but to do so would require unfeasible computing power and taking over &gt;51% of the network within the space of 10 minutes (i.e. a 51% attack).<br/>The 51% attack is more likely to be successful on a small private blockchain rather than a large public one.<br/>Attacks on cryptocurrencies have been documented (reference opportunities).<br/>These attacks related to access to wallets (obtaining private keys).<br/>Currency transfer websites are a target and have been hacked with cryptocurrencies stolen.<br/>With no central control it is difficult to rollback transactions.<br/>DDoS attacks on cryptocurrency services may slow transactions slightly/may affect MONS value.<br/>Future concerns have been expressed about the scalability of the blockchain, lack of standards, and how it can be used if laws on data privacy become tighter.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "20N.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics of a genetic algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by the term “elitism”.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>(Search) heuristic / Finds a near optimal solution;<br/>Based on the theory of evolutionary biology / natural selection;<br/>Uses convergence;<br/>Uses a stochastic approach;<br/>Uses a mating pool;<br/>Uses elitism;<br/>Uses a stopping condition;<br/>Uses selection;<br/>Uses crossover;<br/>Uses mutation;<br/>Uses a fitness function;<br/>Uses offspring/generations;<br/>Uses stochastic universal sampling;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Taking only the fitness members of a population to the mating pool;<br/>Based on a calculated fitness value;<br/>Unfit population members are discarded / fit members are carried forward;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Calculate the offspring from parents P1 and P2 using the cycle crossover (CX) method.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Show all your working.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The partially matched crossover (PMX) operator is a genetic operator that can be used with a genetic algorithm written to solve the travelling salesman problem.</p>\n<p>PMX combines two chromosomes (parents) to produce a new chromosome (offspring).</p>\n<p>Outline how the parental characteristics (cities) are preserved when two offspring are generated through PMX crossover.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p><strong>Solution 1</strong></p>\n<p><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p><em>Award <strong>[1]</strong> for B D in correct place</em><br/><em>Award <strong>[1]</strong> for G I F J in correct place</em><br/><em>Award <strong>[1]</strong> for C E in correct place</em><br/><em>Award <strong>[1]</strong> for A H in correct place</em></p>\n<p><strong>Solution 2</strong></p>\n<p><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p><em>Award <strong>[1]</strong> for D G in correct place</em><br/><em>Award <strong>[1]</strong> for I F J B in correct place</em><br/><em>Award <strong>[1]</strong> for A C in correct place</em><br/><em>Award <strong>[1]</strong> for H E in correct place </em></p>\n<p><em>Note to examiners: allow follow through errors, so that if a candidates’ logic is correct from the point of an error, they are awarded marks that point onwards</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p><strong>Offspring 1</strong><br/>PMX algorithm <span style=\"text-decoration:underline;\">randomly</span> selected sub-sequence of P1 is copied into the first offspring;<br/>P2 contributes the remaining cities to the first offspring;<br/>To ensure that P2 cities are not duplicated, they are mapped against P1;</p>\n<p><strong>Offspring 2</strong><br/>The remaining cities in P1 (i.e. not copied to offspring 1) are copied into the second offspring;<br/>P2 contributes the remaining cities to the second offspring;<br/>To ensure that P2 cities are not duplicated, they are mapped against P1;</p>\n<p><em>Award <strong>[1]</strong> for this alternative offspring 2 solution if offspring 1 is correctly described</em></p>\n<p><strong>Alternative Offspring 2</strong><br/>The process for offspring 1 is repeated but with a different P1 random subsequence;</p>\n<p><em>Mark as <strong>[2 max]</strong> for Offspring <strong>1</strong> and <strong>[2 max] for</strong> Offspring 2</em></p>\n<p><em>Accept a correct example that explains the process but not replicated from the case study</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Compare and contrast the effectiveness of heuristic and non-heuristic algorithms for optimizing solutions.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>A heuristic is an algorithm that does not guarantee an optimal solution;<br/>Instead, a stopping criterion is determined before running;<br/>Heuristic sacrifices accuracy (optimization) for speed;<br/>Heuristics use randomness to escape local extrema / for exploration;<br/>Unlike heuristics, brute-force does not build off previous success/is entirely explorative and novel;<br/>Given enough processing power/time, a brute force approach will always produce an optimal solution;<br/>A brute-force approach is not suitable / heuristic only suitable approach for non-polynomial (computationally intractable) problems;<br/>The number of different permutations if the number of cities is high will make the problem difficult to solve computationally;<br/>In some situations, an optimal solution may be necessary ethically (e.g. where people’s;<br/>lives are at risk) / In other situations, a non-optimal solution can be used; </p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>The following flowchart is intended to represent an algorithm in which numbers that are input cannot be negative.</p>\n<p>The flowchart contains a logic error that will affect the algorithm’s functionality.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArIAAAM6CAYAAAB97aCBAAAgAElEQVR4nOzdv3Ma+Zr3/c88NekRk2w0qJ3etzBKx9gofIRtlN2WaoROZHHXGhwJV42Qg8eaYC0cGEWGqTroRAbvCke70oxwOFDCE65YnNPm5IbzB/AEcveAhH4jmhbvV5VqRNN0X9JI+NKHq7980+l0OgIAXNnmZlqStLKScLgSABgP39DIArgIw/A6XQJczDSbTpcA4Ab51ukCALgPzQgugz+CAAza/+N0AQAAAMBl0MgCAADAlWhkAQAA4Eo0sgAAAHAlGlkAAAC4Eo0sAAAAXIlGFgAAAK5EIwsAAABXopEFAACAK9HIAgAAwJVoZAEAAOBKNLIAAABwJRpZAAAAuBKNLAAAAFyJRhYAAACuRCMLAAAAV6KRBQAom83IMLwyDK+SyVWnywGAc6GRBeCoYPCu3UBJUqVS7ttQdTda3R9Hmy7TNBWJLNr3h8MPtbu7o0hk8ULn7N7n6Ef3saxzhsMP7ftjsSeKRBZVqx0cO36/j0IhP/hv7AXs7u5IkkyzKdNsamZmRtlsxtGaAOBcOgBwAZOT3w/0eI1Go3PvXuDYtsXFH098zNH9jx7r4OC/e7avrv7Uc7zznnNx8cdOufx7z7Zy+fdj+927F+jZ7+XLf+tMTn5vbyuXf++srv7Ut/7V1Z86+fzbE7/WYej3vT7pe3wVg/7ZAYBvnW6kAWBQMpk3isefyu+f7tmeSr26tnOapilJCgZn7G1ra89VqVTk8XgGfr5KpXwsEe5mGIYqlf1zH6/VaqnVavU5zi3VagfHvpcAMEpoZAHcGJVKWWtrz6/l2JHIouLxuILBmZ6m1TAMSVIyudrTMO/u/notdQSDMzLN5sCOd9jIfjnxPgAYZTSyABxnmqY9r2rpbhYv4rwp6HnP2Z1+xuPxvscqFP5dmcwb+3h+/7SWlpYUiSzZxz3p67nOtBgAbjou9gLgOMMw7AuNTLN5oZfGj+pOEY9esNV9UdV5z1kovJNpNk9trA3DUCr1yj7W0tKS8vn8tVzEddaFY8Hg3Qsd77TG/zpGIwBgkEhkAdwYweCMKpWywuE5SbKb00qlrEwmYyekl1EovLM/z2YzisXi9udfvnzpGWmwznMdL80PerTA4/HI4/lOrVbLblxbrZZMs8F8LICRRyIL4MaIx58qk8nYy15ZrAuyBiWff9tzzGw2c2y5qp2dHfn9/oGe97rE43FtbLy0bxcKeS0t/dXBigDgfGhkATimUikrGLzbM69qbbP+a+leR9ba/+g6soZhKJv9Rclksufl9oODA2Wzv1zonNbt7jVpu8/fLRaLq1wu97y8Pzc3d2wcoV/9g26yLyMcntOtW7fsmhqNhp04A8Ao+6bT6XScLgKAexw2X4N7aRvjg58dAINGIgsAAABXopEFAACAK9HIAgAAwJVoZAEAAOBKNLIAAABwJRpZAAAAuBKNLAAAAFyJRhYAAACuRCMLAAAAV6KRBQAAgCvRyAIAAMCVaGQBAADgSjSyAAAAcCUaWQAAALgSjSwAAABciUYWAAAArvSt0wUAcB/D8DpdAgAA+qbT6XScLgIAbgLD8Mo0m06XAQBjg9ECAAAAuBKNLAAAAFyJRhYAAACuRCMLAAAAV6KRBQAAgCvRyAIAAMCVaGQBAADgSjSyAAAAcCUaWQAAALgSjSwAAABciUYWAAAArkQjCwAAAFeikQUAAIAr0cgCAADAlWhkAQAA4Eo0sgAAAHAlGlkAAAC4Eo0sAAAAXOlbpwsAADeLRpfVbrfs2wsLj+zPvd5JpdObTpQFAGOBRhYArsDn82lzM23f/vjxo/3569cLTpQEAGPjm06n03G6CABws0Dgjv7xj2bPtjt37mh7+71DFQHAeGBGFgCuqN/4wMrKMwcqAYDxQiMLAFcUCAR0584d+/ajR/MKBAIOVgQA44HRAgAYgGbzs+7eDegvf/mLqtU/NDEx4XRJAHDjcbEXAAyA1zupx4+X5fF4aGIBYEhIZAFciGF4nS4BLmaazbN3AoBzIpEFcGE0I7gM/ggCMGhc7AUAAABXopEFAACAK9HIAgAAwJVoZAEAAOBKNLIAAABwJRpZAAAAuBKNLAAAAFyJRhYAAACuRCMLAAAAV6KRBQAAgCvRyAIAAMCVaGQBAADgSjSyAAAAcCUaWQAAALgSjSwAAABciUYWAAAArkQjCwBjplIpy+/3yTC8CocfqtVqOV0SAFzKt04XAADdCoW8ksnVnm3B4IwKhXd9908mV1Uo5CVJhmEoHn+qSGTp6+PuyjRNe1/TbCqbzWhj46W9bW3tuRqNhn2Mfkyz2fW5qVjsiWq1A0lSODynVqultbU1+f3TX+vwnnisSGRJqdSrE++/bqZpamdnR7Va3b69sfHS0ZoA4LJIZAGMlEhkSabZVCr1yv78pCY2HH4o0zRVq9Xtx2Qyb5TNZiRJ2ewv8vunZZpNuxmNxeL2McPhOcVicaVSr7S29lxra8/tfbsf01vfj1pbW7PvNwxDlUq5J9U0DMO+PxJZUqHwzq7Pafn8Wy0tLdm3DcNQq9WyG3MAcBMaWQCulM1mZBiGCoV38ng8kqzk9t+Vz79Vq9WSYdySaTbsx/j9PsViTyQdJpGGYVzonFa6GwzO2NvW1p7L75+2axg0w/Ce+tGdOJ9HpVKRYdzq2TY9Pa1KpTLIsgFgKBgtAOBK5XJZa2trx7YbhqFgcEaVSlnh8JwkqdVqyTQbMoxbqlTK9rbvvvuu77GtRNdKb7uPLR2OM3Snq7u7vw7mi+qjXyp8Fa3WF7VarWtrvAFgmEhkAbiSaTbk8fRvRG/dumW/1G+lsjs7O5qbm5PfP61a7UCNRuNYIrux8VKG4e2ZoT2qUPj3r8f12hdLHZ2vrVT2+z7W6flYALhpaGQBuEY2m7HTUsO4pVbrS9/9Go2GnTj6/X6ZpqlKpaJgMKiZmRlVKpW+owXWjOza2vMTazAMQ6nUK3sGdmlpSfl8/tSLxa5i0KMFJzX/AOBGjBYAcKWZmRllMhlls7/0bD9sWst2M3rr1i2ZpqlW64u9qkAymVSr9eXYrKglFovbn2ezGUUiS/J4PMpmM/ry5UtPo2utkHBdS1gNerQgGAyqVjvoaeJPGtMAgFFHIgvAlQ7nV01FIot2KlmplBWJ/Kilpb/aiazH41Em88aelz1cxaBx7jnRRqOh3d0d+3Z3KmzZ2dmR3+8f1Jd2rZaW/qp8Pm833pVKWR6Px27yAcBNSGQBjJSj68gefcm+Ow3d3f1VyeSqgsG7ko6vI2tta7Vamp7+s1ELh+dUq9Xs291r0fabj+1+bCwWV7lctvezztm9koEkRSKL9oVl1rGtJcWcdFhvXH6/T9JhY3/S8mYAMOq+6XQ6HaeLAOAeh3OZg325G+OBnx0Ag8ZoAQAAAFyJRhYAAACuRCMLAAAAV6KRBQAAgCvRyAIAAMCVaGQBAADgSjSyAAAAcCUaWQAAALgSjSwAAABciUYWAAAArkQjCwAAAFeikQUAAIAr0cgCAADAlWhkAQAA4Eo0sgAAAHAlGlkAAAC40rdOFwDAfQzD63QJAADom06n03G6CAC4CQzDK9NsOl0GAIwNRgsAAADgSjSyAAAAcCUaWQAAALgSjSwAAABciUYWAAAArkQjCwAAAFeikQUAAIAr0cgCAADAlWhkAQAA4Eo0sgAAAHAlGlkAAAC4Eo0sAAAAXIlGFgAAAK5EIwsAAABXopEFAACAK9HIAgAAwJVoZAEAAOBKNLIAAABwpW+dLgAA3CwaXVa73bJvLyw8sj/3eieVTm86URYAjAUaWQC4gkAgoJ9/Xrdvf/z40f78b3+LOlESAIyNbzqdTsfpIgDAzQKBO/rHP5o92+7cuaPt7fcOVQQA44EZWQC4on7jAy9e/OxAJQAwXmhkAeCKAoGAZmdD9u1Hj+bl8/kcrAgAxgOjBQAwAM3mZ929G9Bf/vIXVat/aGJiwumSAODG42IvABgAr3dSKysJTUxM0MQCwJCQyAK4EMPwOl0CXMw0m2fvBADnRCIL4MJoRnAZ/BEEYNC42AsAAACuRCMLAAAAV6KRBQAAgCvRyAIAAMCVaGQBAADgSjSyAAAAcCUaWQAAALgSjSwAAABciUYWAAAArkQjCwAAAFeikQUAAIAr0cgCAADAlWhkAQAA4Eo0sgAAAHAlGlkAAAC4Eo0sAAAAXIlGFgCgbDYjw/DKMLxKJledLgcAzoVGFoDjCoW8wuGHdiO1sfFS2WxG2WymZ79sNiO/32fv131/pVK2txuGV4VCvu+2ZHLVvm2apn3c7v0qlfKxbUc/TNOUaZqn7mPV133O7o9Y7Mnwvsmn2N3dkSSZZlOm2dTMzMyx7z0AjKQOAFzA5OT3Az3ezs5/dV6+/Leebfn8287t21OdTOaNve3ly3/rPHz4oHNw8N+dTqfTaTQanYcPH3SePPlXe59Go9FZXPzx2DlWV3/qlMu/27czmTed27enju17716g02g07H3y+bd9j9u9X6fT/3uSybzpqf/o7S9fvnQWF3/srK7+1O/bMlT9vmf37gUGfp5B/+wAAIksAEdlMhmtrT3v2RaJLCkSWbJv12oH2t3dUaHwTn7/tCTJMAzt7v4q0zRVqZQvfN54/KlMs3Gpxw6Cx+NRKvXqwuc/mjIf/QgG717oeK1WS61W69h2w7ilWu3gQscCgGH71ukCAIwv66X9frqb20qloqWlv8rj8Rzbb2lpSeVyWcHgzIXPH48/VTK5qkpl/8KPPcnh2EFTsVj81P1qtQMlk0ktLf31QscPBmdkms2rlNjjsJH9cuJ9ADDKSGQBOKpfc3pUo9E4cT/DMC7dcEUiSzKMWyoU8sfui8XiPalwt0plX4ZhHKnjMBE9zcbGy5752KWlpTMbXgDAyWhkATjKNBtn7nPr1q0Tm1XTNM/VDJ8kHo9rY+PlldNH60Kp06ytPZdpNlWr1WUYt3RwcPGX7gc9WnDa9+4q31cAGAYaWQCOsVLNs2Yxg8Gg8vm3fZvNfD6vmZnDsQKPx9N3n1qtdmJTFgzOKBye65vKXkZ3M3vSlf8ej0eFwju1Wq0Lrw5gjRac9HHRMQmPxyOP57ue71ur1ZJpNux5ZAAYVczIAnBUNvuLksmkUqmU3TjVagfa2NiQx+NRNvuL/P5phcNzikQWtba29rWZMxWLPZFhGPZ87GFT5lE2m7Ffsi8U8mq1vpzalMXjTxUM3h1oAmmapvL5t6eODqytPVc4/EDh8NyxUYVhslLpVOqVpMPv2UVnd+FeZ43EACcZ5Lz+pTm9bAIAd7mOJZQODv67s7j4Y2dy8vvO5OT3x5besljLZln79dvHWtbK2ufhwwc9S2Wtrv5k3zc5+X3PclvdtzudTqdc/r1n38nJ73uWpWo0Gsfu77fv0XMeXQrs6DYnWHVMTn5/bUuCsfzWaOL/Cy5jVH5uvul0Oh2nm2kA7mFdlQ9cFD87o4n/L7iMUfm5YUYWAAAArkQjCwAAAFeikQUAAIAr0cgCAADAlWhkAQAA4Eo0sgAAAHAlGlkAAAC4Eo0sAAAAXIlGFgAAAK5EIwsAAABXopEFAACAK9HIAgAAwJVoZAEAAOBKNLIAAABwJRpZAAAAuBKNLAAAAFzpW6cLAOA+huF1ugQALtdqtbSx8VKFQl6S5PdPK5VKKRJZVK1Wt/erVMra2NhQrXYgSQqH55RKvZLH45EkRSKLqlTKkiTDMFSp7B/bFo8/VTK5KklKpV4pEllSpVJWJLJon2dt7blisfipz2+RyJJSqVcKBu/KNM2++wSDMyoU3sk0TQWDd4/dbxjG12PMnPt7hVN0AAADMTn5vdMlABfm1M/t4uKPnUajYd/+8uVLZ3Hxx556dnb+q3P79lQnn39rb3vy5F879+4FOl++fLG3ra7+1CmXf+85fqPR6Cwu/mjfLpd/79y+PdW5fXvq2GO7j3/vXqDvcfP5t53V1Z966j96znL5955zdjrHv7/5/NvO7dtTPV+7G43K8x2jBQAAYKgKhbxmZmZkGIa9zePxKJv9xU5aJSmZXFWh8E6RyJK9LZv9ReHwnJ3kXoTfP33pxw5KJLKkcHjOTozP/7hFGYb3xA8nvyYnMVoAAACG6uDgQDMzx19a93g89lhBpVJWMDgjv3/62H5LS39VLPZEsVj8wueOx58qGLyrcHiup5G+ikhkUfF4XMHgzJkjA9lsRpVKWWtrzy90jkLh3VVKvLFIZAEAwNB1J6/9mKZ54j6GYajV+nKp8xqGobW158pk3vS9v1LZ77vdmo/t3XaYkp6VrnYnpwcHB9rd/e3Mrx/nQyMLAACG7qSLpSyHzWrrxMd6PN9d+tyRyJJ2d3cu/PL+UYcXdTXPTGFNsynTbCqVeqVa7UCm2bjwuRgt6I/RAgAAMFTT09Mql8s9s69HBYMzisWeqFY7ODZekM+/1dzcnH3b4/Eca3prtYMTU0+Px/M1lc0MZLyg+2X/bDZz4shDJLIkv9+vWOzJhVNZRgv6I5EFAABDFYksyePx2EtiWbLZjPx+n73U1uFSWYvKZjP2PodN4E5PEzwzM6ONjZd2yttqtZTJZPrO4XbXYJoN7e7uDPJLUz7/9tS02brgbGPj5UDPO65oZAEAwNClUq9069Yt+f0+++XxcrmsQuGdncCGw3PKZn/Rzs6OvY+kY2lmMDijePypwuEHMgyv/H6f5ubm7Ga3UMjba8t2vwyfSr3qO75gvYxvPe7oS/fB4F17HdqjL/FbTaxpmna9huHtWbM2Hn96bB1bXM43nU6n43QRAHATHP4j1nS6DOBC+LnFZYzKzw2JLABcs2bzsxYWHjldBgBcu62tnKLR5aGdj4u9AOCatNttpdOv9fe/bzldCgBcq2q1qkRiRf/4R1N37twZ2nlpZAHgGmxt5ZROv9Y///lPp0sBgGvTbH7W+vq6PnwoOXJ+GlkAGKBqtaqff36hT58+HbvPuvADAG6Czc20NjfTx7Z//Phx4M93J83j0sgCwJCMwoURwFH8gYVBu3Pnjra33w/seKf9jHKxFwAMUCAQ0N7eB71+ndZf/vIXp8sBgGuzspLQ/n5Vs7Mhx2qgkQWAazA/v6Bq9Q89fjy8q3cBYNi83knlclv6j/8oampqaujnp5EFgGsyMTGh9fWftb9fHepVvAAwbNarUS9erGti4vxvvXtVvCECAAzIqCwQDlwEP7e4jGH+3Jx2LhJZAACAGyabzdhvpXuT0cgCwBVEo8taWHhkv3OX9fnCwiMlEisOVwc4I5vNyO/3yTC88vt9KhTyikQWVasd9OyXTK7KMLwyDK+CwbsqFPI9x7DuO0zkzL7bgsG79uMtkchiz379tnV/WI8tFPIn7mMYXrsptM559CObzVz5e9f9dVxGrXagcPjh12PNXLmeUUcjCwBXEAgE9PHjR338+FGS7M8/fvyoUMi5K3kBp2xsvJRhGKrV6jLNpmq1ug4ODlSrHajVatn7hcMPZZqmvV8q9UqZzBu7GYzF4kqlXmlt7blMsynDMBSLxe3Prf9WKvsKBmfUarXsxxYK71Sp7Nv7WSqVfftc1nErlX37/khkSYXCOwWDMzLNZs9Hd1NonbNQeGffv7v7q3Z2dnqacSf4/dPa3f1VsVjc0TqGhUYWAK5geTna90rdO3fuKBS670BFgHNM05RpmgqH53q2p1KvZBi35PEcXgR0mKwaKhTe2dsOG8N/Vz7/tqfhPa+1tefKZN5c6rGD4PdPa2lpSQcHB2fvjIGhkQWAK3rx4udzbQNuukqlLMMw+t63u/ur/P5pSVK5XFY8fjwxNAxDweDMpeY6DcNQODynjY2XF37sSSqVsiKRRUmyk9qTFAp5ZTJvtLS0NLDz42y8sxcAXFEgENDsbMh+r/HHj5fl8/kcrgpwxnfffXfmPqbZkMfTf79bt25dOlVdW3uuYPCu4vGnx+4rFN71fYw1ntDtsCE/nK09rXm1mlzpMJHNZn+xm/Xz6m6W/6zpz3eyOquBHncksgAwAOvr65Kkv/zlL0oknjlcDeCcRqNx5j6GcUut1pcTH2+NG1yUx+NRPP5UyeTqpR5vsWZkT2p+LdaM7O7ur5KkWq126XNZH9Zcb7/ZXBxHIwsAA+D1TioWiyuReKaJiQmnywEcYY0FnJWozszMKJM5foW/aZqqVMp28+bxePTlS2/D22q1Tj3+4QVhjYEsO2Vd0CUdrgawu7vTdz+/f1qFwjtlMm+OrcyA60UjCwADUK1W9Z//+Z8qFrdVr9edLgdwhGEYisefKhJZ7Gk2C4W8wuGHPSsSmKapSGRRpmlKsl5i/1FLS389cgFYvqcp3dh4eexisqPi8acDnZWVDtPWcvnk5tjj8Wht7blisScDPS9Oxzt7AcAVtNttpdOvVSxuK5F4pna7rc3NtFZWElpZSThdHnCm63iHpkIhr3w+b6eT1hX9kUjvhVDJ5Kq9XNWfTXDvPrXagZLJpH2scHhO2ewv9v3B4F27Ge6edz1skhs986/ZbOZYgxuJLCmVemXXfdpYgrVv9zkl9Xz/YrEn2t3dufT39PBNDPbP3vEE/WZupf6zwFcxKu/sRSMLAJdUr9f17NmKJiYmlE5vyuud7NkuSa9fb3LhF0Yab1GLyxiVRpbRAgC4hM3NtBYWHml+fkHb2+/tJlaSfD6f9vY+KBS6rwcPQtrcTDtYKQDcXDSyAHAB9Xpd9+/PqlTa0/b2ey0vR0/cd2Ulod9+K6lU2tPCwiM1m5+HWCkA3Hw0sgBwTpubaT14EFIodF97ex/ONTJgpbOBwF2FQrPa2soNoVIAGA+8IQIAnKF75vW330qXmnldWUnozp2AEokVlUp7PTO1AIDLIZEFgFNcJoU9SSAQUKn0QVNTPtJZABgAElkA6KPZ/KxEYkXtdvvSKWw/ExMTWl//WaHQfdJZALgiElkAOGJrK6dQaFaBwN0rp7AnOZrOFovbAz8HANx0JLIA8JWVwn7+3FQu93cFAoFrPd/xdLakdHqTt7gFgHMikQUA/ZnCTk35VCp9uPYmtpuVzk5MTCgQ+EGl0t7Qzg0AbkYiC2CsDTuFPYn17mClUkiJxIoCgSLpLACcgUQWwNhyMoU9SSh0X9XqH5JEOgsAZyCRBTB22u22EokV1et1R1PYk0xMTCiX21KptEc6CwCnIJEFMFZKpT0FAj/I6/WOTAp7kqPpbLVadbgiABgtJLIAxoKVwlar+0qnNxUK3Xe6pHPpTmej0cean19QIvGMdBYARCILYAxYKawkVat/uKaJ7Wals58+1RUKzZLOAoBIZAHcYG5NYU8yMTGh7e332trKkc4CgKRvOp1Ox+kiAGDQ/rxQ6u6NvFCqe9mwdHpzpGd9MdoMw+t0CXAp02wO5TyG4T3xXDSyAG6UdrutdPq1isXtG5HCnmVrK6d0+jXpLIAb67RGlhlZADdGtVpVKDSrZrPp2lnYi1pejqpU+mDPztbrdadLAoChoZEF4Hrtdlvr6y8UjT5WNBpVLrc1Vsmk1zup7e33ikajWlh4pM3NtNMlAcBQMFoAwNWq1aoSiRVNTnqVTm/K6510uiRHNZufFY0uS5Jev96Uz+dzuCIAuBpGCwDcOEdT2O3t92PfxEqH6eze3geFQvf14EGIdBbAjUYiC8B16vW6otFlUtgz1Ot1PXu2Iol0FoB7kcgCuDE2N9NaWHhECnsOPp+PdBbAjUYiC8AVutPFXG6LBvaC+P4BcCsSWQCutrmZ1oMHIYVC97W394Em7BK609lQaFZbWzmnSwKAKyORBTCymPG8HswYA3ATElkArnM0haWJHRyfz6dS6YOmpnykswBcjUQWwEghhR0u1uEFMOpIZAG4wtZWTgsLj0hhhygQCJDOAnAtElkAjms2PyuRWNHnz03lcls0sA6x0lmfz6d0enOs3uYXwOgikQUwsra2cgqFZjU1dTi3SRPrHCud9Xq9CgR+UKm053RJAHAqElkAjuhOYdPpTQUCAadLQpdSaU+JxIoCgbukswAcRSILYKQcTWFpYkdPKHRf1eofkkQ6C2BkkcgCGJp2u61o9DEprMuQzgJwEoksAMeVSnsKBH4ghXUh0lkAo4pEFsC1arfbSiRWVK3uK5f7Ow2sy1npbCh0X+vrP5POArh2JLIAHGGlsJJUrf5BE3sDWOlsu91WKDSrarXqdEkAxhiJLICB605h0+lNhUL3nS4J16BY3Nb6+gvNzy8okXhGOgvgWpDIAhiaoyksTezNNT+/oFLpgz59qpPOAnAEiSyAgWi321pff6FSaY8UdgxtbeWUTr8mnQUwcCSyAK5VtVpVKDSrdrtNCjumlpejpLMAho5GFsClWSlsNPpYiURCudwWSdwY83ontb39XtFoVNHoY21upp0uCcANRyML4FKsFPbTp7pKpQ+an19wuiSMCCudrVb3df/+rOr1utMlAbihaGQBXEh3ChuNRrW9/V5e76TTZWHEWOlsKHRfDx6ESGcBXAsaWQDndjSFXV6OOl0SRtzKSkK//VZSqbRHOgtg4GhkAZzL5maaFBaX4vP5tLf3gXQWwMCx/BaAU9XrdT17tqKJiQml05s0sLgS6+dJkl6/3pTP53O4IgCjjuW3AFzK5mZaDx6EFArdJ4XFQHSnswsLj7S1lXO6JAAuRiIL4BhSMwwDaT+A8yCRBXBu3Sns3t4HmlhcG5/Pp+3t95qa8ikUmiWdBXBhJLIAJJHCwlnValWJxIomJ72kswB6kMgCONXWVo4UFo4KBAIqlT6QzgK4EBJZYIw1m5+VSKyo3W6TwmJkkM4C6EYiC+CYra2cQqFZTU35SGExUo6ms6XSntMlARhRNLLABbRaLQWDd+X3+2SaZt99CoW8DMOrZHJ1yNWdT7P5WQsLj5TL5ZTL/V3r6z87XRJwzMTEhNbXf1Yu93clEiuKRpfVbredLgvAiKGRBS7A4/EolXqlVqulWOzJsftbrZY2NviGUm0AACAASURBVF7KMAylUq8cqPB03SlsqfRBgUDA6ZKAUwUCAVWrf3z9/AfSWQA9aGSBCwoGZxSJLKlWO1A2m+m5L5lcVavVGrkmtl8KOzEx4XRZwLlMTEwol9tSOr1JOgugB40scAmp1Cv5/dPa2HipWu1A0uFIwe7ujmKxuILBGUmSaZqKxZ7IMLwyDK+CwbsqFPI9x8pmMwoG79r7hMMPtbu7M7BaS6U9UljcCKHQfdJZAL06AC6lXP69Mzn5fefhwwedL1++dO7dC3QePnxg39+9rdFodDqdTieTedOZnPy+k8+/7XQ6nc7Ozn/13O50Op0nT/61Mzn5vf2Yy2q1Wp3l5cedO3d+6Ozv71/pWMCo2dv7rTM19b86y8uPO61Wy+lyAFyjycnvT7yPRBa4pGBwRrFYXLXagcLhBzJNU6lUyr5/Y+Olvc0wDElSLBZXODynTOaNJOng4MA+liWb/UWm2bQfcxml0p4CgR++fk4Ki5unO50NhWZVrVYdrgiAE2hkgStYW3suv39apmnan1sqlbIMw+jZJknT04f7VyplzcwcNrC7uzt243sV7XZb0eiyEokVpdObyuW2RmoWNhJZtEcouj+OjlsMUqvVUjK52jO6UasdyO/vXW6sUikrHH5o7xeLPVGr1epbezB4t+82a8WK7q/p8Ofgz6/Vmqvu932wPqwVL7pHTo5+RCKLkg7HV/rdHwzeVaVSvvD3a3d359KPHTZrdnZ9fV3R6GOtr79gdhYYN0NMhoEb6eXLf+tMTn7fOTj4757tt29PdSYnvz/xY2fnvzqdzuF4werqTz33vXz5bxeuw00vtTYajc7i4o9DOdfi4o89YxpfvnzpLC7+2PNS1c7Of3Vu3546NuJx716g8+XLF3vb6upPnXL5957jH/1ayuXfO7dvT3Vu35469tju49+7F+h73Hz+bWd19aee+o+es1z+/dj37+hLb/n8287t21OXGlEpl3/v3LsX6Cwu/tjzNYyyz5/Nzvz8/2GUBriBGC0AHODxeL6mtc2+H+HwnCQpHJ5TKvVKptnU7u6vCofnlM1mtLHx8lznGfUU9jIOxzUe9qSPRxPU8ygU8pqZmekZ0/B4PMpmf5HH47G3JZOrKhTeKRJZsrdls78oHJ67VFrs909f+rGDEoksKRyeu1SyGgzOqFLZl9/vl9/vO7Y6xyjyeie1vf1e0WiUdBYYIzSywDUJBmdkmo2el6elw9lZv993bLt02ABZTdZ5xgyq1apCodmvn/+hUOj+YIp3WDKZ1NzcnN30x+Pxvt+vsxwcHPSdNfZ4PKrV6pIOX/oPBmeOjYBI0tLSX7Wzc7kVJOLxpwMZF+kWiSza9RYK707dN5vNfB2XmLv0+dbWnqtS2dfOzo49kjHqlpejKpU+6NOnOrOzwBigkQWuSTz+VJIUiz2xm5lCIa9sNqN4/Kk8Ho/d1HYvt5XNZtRqtez52X7a7bbW1198TZ7Wb0QKe1R3Ynr4R0H/99m+yHH6MU3zxH0Mw1Cr9eVS5zUMQ2trz+0L+46qVPb7bo9Elo6tQ2zN4p6VrnbPyB4cHGh397czv/6zGIah3d1f5ff7FQ4/dDRlPq9+6SyAm4lGFrgmhw3AYSNhXbSzsfFSa2vPFYvFJR0mXvH4056LkfL5t0qlXvW8zN3NSmE/faqrVPpwY1LYbmtra1/fIe3PC7Qu+/L2WYnoYbPaP+09bHK/u9R5pcOmdHd358oXThUK72SazZ7VLfqxEuxU6pVqtQOZZuNK5z08pvk1ja1pd/fXE38uR1F3Onv//qzq9brTJQEYMBpZ4IrW1p7LNJt9X5o2DMNeTss0m6rV6nYTazlcwqtu71Op7PdtFrpT2Gg0qu3t9/J6J6/t63JSMDjT8z2Zm5vTzs7FG8Lp6WmVy6c/5nAetNz3ZfN8/q3m5v58ad7j8Rxremu1gxNTT4/H8zWVHcyMaaHwzm5mT2vsI5ElZbO/HFt54aI2Nl4qGLyrubm5r6ns8Z/xUWels/PzC3rwIKTNzbTTJQEYIBpZwAWOprDLy1GnS7o21jJY3UlqMBi81LEikSV5PB57OStLNpuR3++zm9fDBHyxpzmMxZ5od3en54+KmZmZnrnXVqulTCZz6hhIJLIk02wM9N3apMMm+7S02brg7LwXDXY7nMO9q1qt1vePLzdaXo7qt99KKpX2SGeBm2SIqycAuKBWq9V58eL/60xN/a9OLvc3p8u5MmvZq6Mf3ctSWUtLde97+/ZUJ5N5c+nzZjJvepZDW1z88dhyaeXy752HDx/Y+zx58q99l56ylrWy9uuuK59/2/frst4FrvvrPOn7cXSJrtOWcGs0Gp1Go9GzrXtZLuvd5S6y1Fk+/7Zz717g2JJfN0k6/bozOfl9J51+7XQpAM7htOW3vul0Oh2nm+lxZhhep0uAC/ztb7kbOQsLOGVrK6eff17X48fLWl//2elyAJzCMLwnXvD77ZBrQR+XvRob42FzM63/+3+jWllJaGUl4XQ5gOutr79QsbitFy/Wb/SYDjAOmJEFRtzKSoLZPmAA6vXD1QvGYdYcGBc0soAL+Hw+7e0dLrX14EFIW1s5p0sCXGVzM60HD0Kan1+40St+AOOGRhZwESudLRa3tbDwSM3mZ6dLAkaalcKWSnv67bcSKSxww9DIAi5jpbNTUz6FQrOks8AJrBQ2FLqvvb0P8vl8TpcEYMBYtcBhp12JB5ylWq0qkVjR5KRX6fQmL5cCOkxhnz1bkSS9fr1JAwu43Gm9Eoks4GKBQEClEuksYNnaypHCAmOERNZhJLIYFNJZjLNm87MSiRW1221SWOCGIZEFxsDRdLZU2nO6JGAotrZyCoVmNTXlI4UFxgyNLHCDTExMaH39Z+Vyf9f6+rqi0WW1222ny3KFVqulSGRRhuGVYXi1sfGy73612oHC4YcyDK/8fp8KhXzf/TY2XtrHikQW1Wq1rrP8sdRsftbCwiPlcrmvP/O8QxcwbmhkgRvISmcPP/+BdPYcrMbVNJsyzaZu3brVt0mNxZ5obm5OptlUrVbXwcGBTNPs2adQyKtSqahS2ZdpNrW2ttb3WN1NMQ3vxXSnsKXSBwUCAadLAuAAGlnghpqYmFAut6V0elOJxArp7BkqlbIKhXf27UhkSY1Go2efWu1Afv+0YrG4vS2VeqV8/m3PfuVyWalUSoZhSJL8/mlJOtakRiKLPU1xq9U6MQnGoX4p7MTEhNNlAXAIjSxww4VC91Wt/iGJdPaidnd3zkxITdNUpVI581iNRkOVSrlnm8fjsZtij8ejQuHdsX3wp2JxmxQWQA8aWWAMkM6eLRicUSSyKOmwOf3z8z9TWb9/WrXagbLZjKTDFDcWe6JW60vPsWZmZpRMJu2Rg2RyVZVK+dgIQqWy33PbNBvyeL4b7Bd2A7TbbUWjy0qn06SwAHrQyAJjpDudDYVmVa1WHa5odKytPZd0uMxLMHhXc3Nz8vunjyWy2ewv2tnZkWF4lUyuKpVK9RkZWFIwGFQweFeG4ZUkLS399cRzm6apZHJVyWRSqVRqwF+Zu5VKewoEfvj6OSksgF6sI+sw1pGFU0qlPSUSK5qfX1Ai8YyEqw+/36dKZV8ej+fEfWq1AyWTSe3u/nrqsayLxMLhuWP3JZOrmpubUzA4c+Wab4p2u61EYkXV6r7S6U2FQvedLgmAQ1hHFsAxodB9lUof9OlTnXS2j93dHfn906c2sZK0s7OjubnjzWk30zRVqx2c2KimUq9oYrt0p7DV6h80sQBORCMLjDGvd1Lb2+8VjUYVjT7W+voLZmd1uHxWJpNRKvXq1P02Nl6qVqv1rGJwVK12oFjsiVKpV32b4kqlbK9JO+5Lb1mzsInEitLpTeVyW7xSAOBUNLIAtLwcHft0NhZ7Yq/nenBwoELhnb18lqXVatlzr8HgXX333Xc9S3ZZstmMfazDhjhF4nqGarVKCgvgwpiRdRgzshg1W1s5pdOvmZ3FULTbbaXTr1UsbjMLC6AvZmQBnFt3Oruw8Ej1et3pknBDVatVhUKz+vSpTgoL4FJoZAEcY83Ozs8vaGHhkTY3006XhBuk3W5rff2FotHHikaj2t5+T/IP4FJoZAGcaHn5sMkolfZ0//4s6SyurDuFLZU+aHk56nRJAFyMRhbAqXw+n/b2PigUuq8HD0Kks7iUfims1zvpdFkAXI5GFsC5rKwk9NtvJdJZXFi9fjhvTQoLYNBoZAGcG+ksLmpzM62FhUean18ghQUwcDSyAC6sO51dWHikZvOz0yVhxNTrdd2/P6tSaU/b2+9JYQFcCxpZAJdipbOBwF2FQrPa2so5XRJGxOZmWg8ehBQK3dfe3gf5fD6nSwJwQ33rdAEA3G1lJaE7dwJKJFZUKu0pnd7k5eMxVa/X9ezZiiTpt99KNLAArh2JLIArCwQCKpU+aGrKRzo7pkhhATiBt6h1GG9Ri5umWq0qkVjR5KSXdHYMNJuflUisqN1u6/XrTRpYAAPHW9QCGJqj6WyxuO10SbgmW1s5hUKzCgTuksICcASJrMNIZHGTWemsz+dTOr3J25DeEFYK+/lzU+n0pgKBgNMlAbjBSGQBOMJKZycmJhQI/KBSac/pknBFVgo7NeVTqfSBJhaAo0hkHUYii3FRKu0pkVhRIHCXdNaFSGEBOIVEFoDjQqH7qlb/kCTSWZchhQUwqlhHFsDQTExMKJfb6kpni6SzI6zdbiuRWFG9Xlcu93caWAAjh0QWwNAdTWer1arDFeGoUmlPgcAP8nq9pLAARhaJLABHdKez0ehjzc8vKJF4RjrrMCuFrVb3lU5vKhS673RJAHAiElkAjrLS2U+f6gqFZklnHWSlsJJUrf5BEwtg5JHIAnDcxMSEtrffa2srRzrrAFJYAG5FIgtgZCwvR1UqfSCdHSJSWABuRiILYKR4vZOks0PQbreVTr9WsbhNCgvAtUhkAYyko+lsvV53uqQbo1qtKhSaVbPZJIUF4Go0sgBGlpXORqNRLSw80uZm2umSXK3dbmt9/YWi0ceKRqPK5bZIugG4Go0sgJFnpbOl0p7u3yedvQwrhf30qa5S6YOWl6NOlwQAV0YjC8AVvN5J7e19UCh0Xw8ehEhnz+loCru9/V5e76TTZQHAQNDIAnCVlZWEfvutRDp7DvV6nRQWwI1GIwvAdXw+H+nsGTY301pYeEQKC+BG+6bT6XScLmKcGYZXptl0ugzAter1up49W5Ek5XJbY9+w8f0AcNOc1iuRyAJwte50NhSa1dZWzumSHLO5mdaDByGFQve1t/eBJhbAjUci6zASWWBw6vW6otFlTU56lU5vjk0j153Cvn69KZ/P53BFADA4JLIAxoLP51Op9EFTU76xSWePprA0sQDGCYmsw0hkgetRrVaVSKzc2HSWFBbAuCCRBTB2AoHAjU1nt7ZyWlh4RAoLYOyRyDqMRBa4flY66/P5lE5vuvZtWZvNz0okVtRut0lhAYwNElkAY81KZ71erwKBH1Qq7Tld0oVtbeUUCs1qasqn7e33NLEAIBJZx5HIAsNVKu0pkVhRIHDXFemslcJ+/txUOr2pQCDgdEkAMFQksgDwVSh0X9XqH5I08ulsdwpbKn2giQWAI751ugAAGLaJiQnlcltd6WxxpNLZdrutaPSxPn9uKpf7Ow0sAJyARBbA2BrFdLZU2lMg8AMpLACcA4ksgLF2NJ0NhUpaX/956Olsu91WIrGianWfFBYAzolEFgD0ZzrbbrcVCs2qWq0O7dxWCitJ1eofNLEAcE4ksgDwlZXOFovbikYfa35+QYnEs2tLZ7tT2HR6U6HQ/Ws5DwDcVCSyAHDE/PyCSqUP+vSpfm3p7NEUliYWAC6ORhYA+vB6J7W9/V7RaFTR6GOtr79Qu92+8nGtFDaRWFE6valcbmtkVksAALehkQWAUywvRweWzlarVYVCs2q326SwADAANLIAcIaj6ezmZvpCj2+321pff6Fo9LESiQQpLAAMCI0sAJyTlc5Wq/u6f39W9Xr9zMdYKeynT3WVSh80P78whEoBYDzQyALABVjpbCh0Xw8ehE5MZ7tT2Gg0qu3t9/J6J4dcLQDcbDSyAHAJKysJ/fZbSaXS3rF09mgKu7wcdbBSALi5WEcWAC7J5/Npb++DNjfTevAgpJWVhFqtlorFbSUSz2hgAeCa0cgCwBWtrCQ0OxvSgwch3blzR6XSB8YIAGAIaGQBYAB8Pp8kaXv7vcOVAMD4YEYWAAAArkQjCwAAAFeikQWAa9RqtWQYXvn9PpmmeeL9sdgTB6oDAHejkQWAIWi1WjSrADBgNLIAMCS12oGy2YzTZQDAjUEjCwBDEA7Pye+fVibzpu+IwVHZbEZ+v0+G4bVHD87zOAAYJzSyADAkqVTqXCMGyeSqNjZeam3tuUyzqUplX6ZpKhL5Ua1Wa0jVAsDoo5EFgCHx+6e1tvb81BGDWu1AhUJesVhckciSJMkwDKVSKZmmqUzmzTBLBoCRRiMLAEMUi8Xl909rY+OlarWDY/dXKhVJ0szMTM92v39ahmFod3dnKHUCgBvQyALAkGWzv8jj8SiZTB6778uXL5Ikj8dz7D6P5ztGCwCgC40sAAyZYRiKx5+qVjs4Nirw3XffSVLfhrXV+iLDuDWUGgHADWhkAcAB1ojB0VnZYDAoSSqXyz3ba7UDmaYpv98/tBoBYNTRyAKAQ6wRg25+/7QikSVlsxkVCnlJkmmaSiaTMgxDa2vPnSgVAEYSjSwAOMQaMTgqlXqltbXn2th4KcPwKhi8K8MwVCj8e9/ZWQAYV990Op2O00WMM8PwyjSbTpcBYAD4fQaAwTvtuZVEFgAAAK5EIwsAAABXopEFAACAK33rdAEA4GbR6LLa7T/XfF1YeGR/7vVOKp3edKIsABgLNLIAcAWhUEjPniXs2x8/frQ//4//eOZESQAwNmhkh2x9/YU+far3bLMSnIkJj3K5LSfKAnBJ8/ML2trK6dOnTz3b79y5o0Ag4FBVADAemJEdslDovj5+/Gh/SLI/9/l8DlcH4DJevPj52DZGCgDg+tHIDlkgENDsbOjY9u+/92p5OepARQCuKhAI6NGjefv248fL8nonHawIAMYDb4jggGbzs+7e7X3J8W9/yykUuu9QRQCuqtn8rFBoVpJUrf6hiYkJhysCgJvhtDdEYEbWAV7vpFZWEtrcTEs6nKWjicVN1263Va/Xz97RxUKh+/qXf/mXG/91DoPP5+OPAQBnIpF1SLvdViDwg/75z3/qt99KzMfixltff6FSqaTJSa/TpWDEffz4kedFADYS2RE0MTGh9fWfVa1WebLGWCiVSkqnN7mSH6cqFrfVbrd5XgRwLiPbyBaL2z1rM95k798XnS7B9V6/Tmt+fsHpMnCCUmlPkmhicaZSqcTvMoBzG9lGtlQq6cWLda7kx6msC2yYMR5tpVJJCws0Jzhds/lZHz6UWLoMwLmN5PJb1pMZf5XjLLnc4WoPXBQyutrttt6/L2p+fv7snTHWisWiHj2a5/cZwLmNZCNbKpU0OxviyQxn4mXI0Vcq7Wl2NsS6qjjT9va2QqHj62wDwElGspHN5XKkNzgTc5fusLWVoznBmarVqiQxJgTgQkaukeXJDOdVLBYVjTJDPcrq9bo+f/5Mao4zFYuksQAubuQaWZ7McB7tdlsfPpT4WRlxh7/P/FGK07XbbZVKe/xhCuDCRmrVAuvJrFT64HQpGHHF4jZzly5QLG5re/u902VgxJVKe/L5fPw+A7iwkUpkS6U9TU5O8mSGM5Hcjz7r95mF7XGWYnGb8RMAlzJSjWyxuM26sTgTc5fuUCwW+X+EMzWbn1Wv1xlBAXApI9PI8mSG8yK9GX2sBY3zYi1oAFcxMo1ssVjkyQznQiM7+lgLGufFWtAArmJkGtntbZoTnK1Y3Gbu0gVYCxrnwVrQAK5qJBpZnsxwXqQ3o69er0tiLWicrVQqseQWgCsZkUa2pIUFmhOcjrlLd+CdvHAe7XZb798X+VkBcCWOryNrPZnt71edLgUjrlQq6dGjeeYuRxhrQeO8WAsawCA4nsiWSns8meFcDq9uJr0ZZawFjfNiLWgAg+B4I8vLkDiPavUwsWfucrS5cS3oSqUsw/Ae+wgG79r7BIN3j22LRBZ79pekQiHf91iRyKJarZYkKZvN9Nxnmua5th39ME1zuN+oAWItaACD4mgjy5MZzov0ZvS5dS3oYHBGptmUaTYViSypUHgn02yqUtm396lU9hUMzqjVaimbzUiSCoV3qlT2ZRiGTLMpSfbju49pmk35/X4lk6uSpFgsrlTqldbWnss0mzIMQ7FY3P7c+q8kpVKv7Fq6j2nd71YsoQdgUBxtZA+bk9H/Ry+ZXO1JaQqFvH3fedOVk1If68M6JonOcdbcJVc3j7ZxWAt6be25Mpk39u/iRR5Xqx1c+HE3FY0sgEFxvJEd9Zchw+GHMk1TtVpdptlUKvVKmcwbO5W5SLoSiSz1JCrdKZCFROe4UmlPPp+PucsRNw5rQRuGoXB4ThsbLy/8WJrYQ6wFDWCQHGtkrYtCRvnJ7DDRNFQovJPH45F0+DJkofDvyuffDvUfpnFOdEhvRt84rQW9tvZcu7s7F3pFI5vNKByes59HziMWi/f8kdvNGmlwI9aCBjBIjjWyxWJx5J/MyuWy4vH4se2GYSgYnFGlUh5qPePYxLp17nLcjNNa0B6PR/H4U/sVkn6OjhI1Gg2lUq+GWOVoYi1oAIPmyDqy1pNZOr3pxOnPzTQb8ni+63vfrVu3LtRYBoMzCgZn+t53nn/gLpvonKT7QpZRdrjk1s2eu3S7cVwLOhaLK59/e+Ifs4ev3Lw78fEej0eNRqNnW6vVuvF/rLIWNIBBcySRLZVKmp0NjfyTmWHcUqv1pe99jUbjQk3lRZHoHOJlyNE3rmtBx+NPLzUrK1mNbr6nEd7YeKlweG5Q5Y0k1oIGMGiONLK5XE7z8/NOnPpCZmZmlMlkjm23ViGwElaPx6MvX3ob3qumK0cv9jraxF7HOUfNOM1dupnb14Lu/qOxUMjb68MeXUe2UikrElm0t0ciS/L7p3uOZT3+6DGP8ng8KhTeaWNjw96v1Wod+z23jmOd/2hdbsJa0ACuw9BHC+r1uiR3PJnFYnGFww8ViSwqlXolwzBUqZSVTK5qaemvPReAJZOrmpn5c3zgutMVJ845bKVSiSW3RtxNWAva+qPxNCeN4hwdH4hElk68QOsov39au7u/Xrk2t2AtaADX4ZtOp9MZ5gkTiRVNTExoff3nYZ72SpLJVTtVMQxD8fjTY/9Y1WoHSiaTqtUOJEnh8Jyy2V+OHct6B6Bu3VcgFwr5YxeRpFKv+v7jeJ5zWilSbw3GyM/Itttt3b49pf396ti9ZO0m6+sv1G63R37eHc5qt9sKBH5QqfSB32cAF3a49n3/P+qH2sjyZIbz2trKqVqtKpfbcroUnMLn+9/a3n4/0svowXnF4ra2tnLa2/vgdCkAXOi0RnaoM7LW2rE0sTgLL0OOvlJpTxMTHppYnMkNb34DwJ2G2sjyZIbzuAlzl+OgWCwyw4wzsRY0gOs0tEaWJzOcF+/kNfpY2B7nxVrQAK7T0BrZYrHIkxnOhUZ29LllLWg4j7WgAVynoTWy29s0JzhbsbityclJ5i5HnFvWgoazWAsawHUbSiPLkxnOi/Rm9NXrdbXbLcaEcKZSqaSFBX6fAVyfITWyPJnhbMxdusPWVo7/RzhTu93W+/dFknsA1+ra39nLejLb369e96ngcsxdjr52u61SaU+lEuuB4nTF4rZmZ0MstwjgWl17Ilsq7enOnTs8meFMzF2OPresBZ1MrsowvF8X0TYlSdlsxt5mGF5VKuWe29Y76h3d1v0461iWSGTR3q/7nN0fkciiWq2W/Zhg8G7f/ax9pcN3+Ot3fzj88FgNo4q1oAEMReeahUL/b2d7+z+u+zRwuf39/c6dOz84XQbOMD//f1zz+5zJvOncvj3VWVz8sWf7vXuBTqPR6Ll9VD7/tpPJvLFvr67+1Ll3L9BZXf3J3tZoNDr37gV6Hp/JvOl5XKfT6Swu/tjzOGtbufx7z7Zy+feeWo/e7nQ6nZcv/61vvaPmf/7nfzpTU//L6TIA3BCTk9+feN+1JrKjurB9d3JSqZQl/ZnCBIN37f2OJjb99ul3TL/fp0Ih37Pfec55UqJjfRwViz3pSWqs9Mhy2rGs9GlUkN6Mvmbzsz5+/Oiqi7zi8acyzYb9O3fVYxUKeTtdzWTeaGnpr2c+LpV6NZDzS9La2nNJGvlU9vD32T0/JwDc61ob2VF9MkulXskwDFUq+woGZyRJptm0t1mCwRmZZlOm2VQksqRC4Z1Ms9mzjySFww81PT1t71ur1b8e07zQOVOpV1pbe661tef2sayPo5LJ1Z5zLi0taXd3p2cfwzD61p9KvRrAd3FwrLlL3iVqtBWLRT16NO+6GeZ4/OlA/nAzDOPr79FhM1uplBUOz535uGRy9dT9IpFFVSplBYMzKhTenbhfq9VSLPZEfv+0DMO41NcwLLyLI4BhufZG9qY/mRUKefn9fkUiSz3bI5Glvg3ooBz9RzQSWTpWg1u4Ze5y3Ll1LehIZEmGcUuFQv7Kx5qbm1Mm8+bUNHZj42XPqx+m2dDc3PFG1pqvPS2t7X5VyO/3aXp6WtnsL1f+Oq4Ta0EDGKZra2RLpT1NTHhu/JPZwcGBZmZmruXY2WzGHhU42hQHgzNKJld7Ut+1teeKxeLXUst1Goc/eNzO7WtBx+NxbWy87Lno6jKCwRn5/dMqFPIn/uF49BWVVOqVksnksYbVeoXEeoXmpPNZx4nF4trZ2bny13DdWAsawDBdWyNbLBZH/qXio1cPX3buzOPxDPScVqKzsfHyxOOkUq/k9/sVDj+w52y752MlHRuBsEQiSyMzXtBsfla9xbARkQAAIABJREFUXh/JERT8ye1rQQeDMwqH5/qmsh7Pd8eaw0ajceKxCoV3qtXq5/69DwZntLS0pJ2dnb73Fwrv7Gb26O9wt7W155qbmxu5+fZurAUNYNiupZF1y5NZpbLfk5xcdu6s+x/BoxdsHf1H5zzntBId68KOk6ytPVetVpdpNpXN/qJyuTzS/8j1k8vlFArdd93c5Ti5KQvbx+NP+6ayc3NzisWe2LdrtQMVCvlzzb+eh2mayufzunXr1pn75vNvT/2DOhaLyzTNgYxJXAfWggYwbNfSyI7Tk9n09LQODg7s26nUq54LtK6SfMZicXtUIJvN2P8AH87HPuzZ1++f/nru0b6a+Shehhx9blwLOplc1cbGS/vVDdM0ZRiG1taeH2tkY7G4/H6//cdnLPZE2ewv9h+Zkcji11GCxZ6VSKzbh8f2Hjtn96okwWDQ/l0OBu+qUinbM7L9XqGxzmfNyHYntalU6tgKJaOCtaABDNu1vLNXLpfT+vr6dRx65EQiSwqHHx6bmRt0Q9loNLS7u2Ofo1Y7sP/Btezu7lzbvO51cPvc5bjY2sq5boY5lXrV94/I7j8Ou1mrhfRz0koC/baf5w/Xk0Z+up128abfP32tF5JeVr1+uFoLY0IAhmngjWy9Xle73RrpJzNrTdZg8K49n2ZtO7yKeP/rslhl+512JNkv5x1dpmt391fFYk96XtYPBme0u/vrhc6Zybyxz9FvPnZ6etr+PByek8fj6Vlftt/FXlaq011/KvVqJFY4cPvc5ThoNj+P5FrQGD1bWznWggYwdN90Op3OIA+YSKxoYmJC6+s/D/KwuGHa7bZu357S/n7VVS9Zj5v19Rdqt9tKpzedLgUjrN1uKxD4QaXSB36fAQzc4ehV/1eiBprIWgvbl0ofBnlY3ECl0p5mZ0P8ozfiisVtbW+/d7oMjDjWggbglIFe7MWTGc6LlyFH37isBY2rYy1oAE4ZaCPLkxnOo16vM3fpAm5YCxrOYy1oAE4aWCPbbH7Wx48feTLDmYrFbX5ORly73XbFWtBwXrFYZC1oAI4ZWCNbLBb16NE8T2Y4E8n96CsWt8dmLWhczfb2Nn/wAHDMwBpZnsxwHtYcNXOXo42F7XEerAUNwGkDaWSr1aoknsxwtmKxyB88I84Na0FjNLAWNACnDaSRLRa3eTLDmZrNz8xdusDWVo7/RzhTu93W+/dFknsAjrryOrLWk9n+fnUQ9eAGK5VKzF26AGtB4zxYCxrAKLhyIlsq7enOnTs8meFMzF2Ovv+fvfv3SiTN+8b/3nM2bei8gU7uYB4Q07a6MVzLX9k2zIgbtbVnWye5Kc8ZtYOVDladQIga5pyBiUbmGTBbnaYMRx+rOxyZ8hsLbHQnUvcfUN8Ai+GnogJVwPt1jmcUoepqh4IP7/rUdeVyWc4FTV3hXNBEZAePLmR5GpK6oWkaALDv0uY4owR1g3NBE5FdPKqQLZdLfDGjrjC9sT/OBU3d4lzQRGQXj+qRTaVSfDGjO+m6zr7LIcC5oKlbuVwW2eyh1cMgInpcIsvTkNQNc+5Y9l3aG+eCpm5wLmgispMHJ7KKkofD4eSLGd2JH3jsT1VV/Oc/ZcTj+4jHrR7N8Pr06ROmpqasHkZflUplSBKPZyKyhwcXsrlcji9mdKdyuQRN05BKsQXFztxuF37+OWf1MIbel18GEYmsWz2MvmOAQUR28aBCVtd1nJwoKJdLtSUK6WFGPcHRdR2iOMu+S5tzudj60Stc4ZCIaHAenMgyvemNcUhw3G6X1UMgIiKiEfSgQtbhcDB16CH+LYmIiIju79ELIhARERERWYGFLBERERENJRayRERERDSUWMgSERER0VBiIUtEREREQ4mFLBERERENJRayRERERDSUWMgSERER0VBiIUtEREREQ4mFLBERERENJRayRERERDSUWMgSERER0VBiIUtEREREQ4mFLBERERENJRayRERERDSU/mz1AIiIhpkkrUDXK7WfQ6HXte8dDidSqbQVwyIiGgssZImIHsHn8yEej9V+/vTpU+37/f1Yu4cQEVGPsLWAiOgRIhEZz565Wm6fmppCMBiyYEREROODhSwR0SNFo9GW2yKRdQtGQkQ0XljIEhE9kijOYmpqqvbz69dBCIJg4YiIiMYDC1kioh7Y3n4PAHjy5AlkWbZ4NERE44EXexER9YDP58ObNytwOp1wudxWD4eIaCwwkSUi6gFN0/D//t8ZTk9PUS6XrB4OEdFYYCFLRPRI6XQKodBrzM8vYHJyEqI4g3Q6ZfWwiIhGHlsLiIgeqFwuQZYj0HUd2ewhfD4fgOrFX7IcgaLkkUr9AIfDYfFIiYhGExNZIqIHSKdTEMUZeL2+hiIWAARBgKKcwOv1QRBeQFHyFo6UiGh0/ckwDMPqQYwzj8eFYrFs9TCIqEtmClsqlRGLxe+cZktVVUjSGwjCS8RicaazRET3dFutxESWiKhL9Smsopx0NVesIAhQ1c833zOdJSLqJfbIEhHdoT6FTaV+uPdiBw6HA6lUGoqShyxHIAg5prNERD3ARJaI6BaKkr93CtuJKM4ynSUi6iEmskREbei6DlmOQNO0B6WwnTSns6KoIBp9z3SWiOgBmMgSETVRlDwE4cXN949LYTsx01ld1yGKM1BVtef7ICIadUxkiYhumCmsqp4jFotDFGf7uj8znc3lspCkNwgGQ5DldaazRERdYiJLRITGFFZVP/e9iK0XDIagKCe4vNSYzhIR3QMTWSIaa4NOYTtxudzIZg+RTqeYzhIRdYmJLBGNLVVVIYozN98PNoXtZGVFYjpLRNQlFrJENHZ0XUc0ug1JeoNoNIpUKm2r5NNMZyVJuhnjttVDIiKyJRayRDRWzBT28lKDopzYIoXtpD6dnZ2dgaZpVg+JiMhWWMgS0VioT2ElSUI2ewiXy231sO5kprPBYAhzcyLi8ZjVQyIisg0WskQ08ppT2JUVyeoh3dvKioSPHxUoSp7pLBHRDRayRDTShjGF7cTn8yGfr7ZDMJ0lImIhS0QjStOqfaXDnMJ2EonITGeJiMBClohGUDwew9yciGAwNPQpbCfN6Ww6nbJ6SEREA8dClohGhpnCKkoeHz8qI5XCdmKms7lcFqHQa5TLJauHREQ0MCxkiWgkmCmsKM4inz+Bz+ezekgDY6azXq8PojjDdJaIxsafDMMwrB7EOPN4XCgWy1YPg2hoaZqG9fUIAGB/Pz5WBWw7qqpCliNwu12IxeIj2VZBROPltlqJiSwRDa1xTmE7EQQBisJ0lojGAxNZizGRJbq/crkEWY5A13WmsLdgOktEo4CJLBGNjHQ6BVGcgSC8ZAp7h+Z0VlHyVg+JiKinmMhajIksUXfMFLZUKiMWi0MQBKuHNFTMdNbn8yEWi8PhcFg9JCKirjCRJaKhZqawXq8PinLCIvYBzHS2+v0LprNENBL+bPUAiIg6qU9hU6kfWMA+ksPhQCqVhqLkIcsRCEKO6SwRDTUmskRkS7lclilsn4jiLFT1MwCms0Q03JjIEpGt6LoOWY5A0zSmsH3EdJaIRgETWSKyDUXJQxBewOFwMIUdkOZ0VlVVi0dERNQ9JrJEZDkzhVXVc8RicYjirNVDGiv16awkvUEwGIIsrzOdJSLbYyJLRJYyU1gAUNXPLGItZKazl5caRHGG6SwR2R4TWSKyBFNYe3I4HMhmD5FOp5jOEpHtMZElooFTVZUprM2trEhQlBOms0RkayxkiWhgdF1HNLoNSXqDWCyOVCrNpM/GXC43stlDSJIESXqDaHQbuq5bPSwiohoWskQ0EKqqQhRncHmpMYUdMvXpbCj0GpqmWT0kIiIALGSJqM/qU1hJkpDNHjKFHUJmOhsMhhAKvUY8HrN6SERE+JNhGIbVgxhnHo8LxWLZ6mEQ9YWqqpDlCNxuF2KxOFwut9VDoh7QNA3r6xEAwP5+HD6fz+IR0WN4PC6rh0BDalD1y221EmctIKKe03Udsdg+crksZHkdKyuS1UOiHvL5fMjnTxCPxzA3JyISkRGJyFYPix6BgQrdl10+ALG1gIh6StO0Wi+sopywiB1hkYiMjx8VKEoes7Mz7J0looFjIUtEPROPxxAKva71wrKVYPSZ6awozmJuTmTvLBENFFsLBiwa3cblZWNqEQq9BgA4HE6kUmkrhkX0KPU9k9nsIXsmx1AkImNmRsT6+h+LXPCDDBH1GxPZARPFWXz69Kn2BaD2Pd/8aRiZfZKiOIt8/oTP4zFmprOC8BKiOIN0OmX1kIhoxLGQHTBBEDAzI7bc/uyZi72ENFQ0TcPs7AwUJY+PHxVe7EM1kYiMVOoHpFIphEKvUS6XrB4SEY0oFrIWiEajLbfJssy5NWloMIWluwiCAEU5gdfrYzpLRH3DQtYCLpe7Ib2amppCMBiycERE3SmXS0xhqWsOhwPR6Hums0TUNyxkLbKyIuHJkycAgO3t9xaPhuhu6XQKojjDFJbujeksEfULZy2wiJlUqKrKgoBsrVwuQZYjKJXKnJGAHsx8zRPFWchyBKqqIhaLs6WKiB7FtoWsouTx97+Px8VPh4c5q4cw9Pb3Y2zP6IN0OoVYbB/BYAip1A8sOujRzHQ2Gt2GILxALBaHKM5aPSwiGlJ/MgzDsHoQ7UjSCgRB4JX8dKtyuQRRnIGqfmaR1UP1KWwsFocgCFYPiUaQouQhyxEIwkumsxa6bR17ok4G+by5bV+27JEtl0s4OVGYsNGdUqkURHGWb4A9ZPbCer0+KMoJi1jqG1Gchap+BgAIwgsoSt7iERHRsLFla4GiKJiZEVmc0J0URUEsFrd6GCNB13XIcgSapiGV+oEFLA2Ew+FAKpWuS2dzTGeJqGu2TGRTqRSCwaDVwyCbM9MbFlyPpyh5CMILuFwuprBkCaazRPQQtktkNU0DADb/050URYEksYf6McwUVlXPedENWa45nQ0GVcjyOtNZIurIdolstT+vdQlXonq6ruPwMMfnyiOYKSwAqOpnFrFkG2Y6Wy6Xby7mVK0eEhHZlK0KWV3XoSh5pmx0p1wui5kZES6X2+qhDB1d1yFJK5DlCGKxOFKpNBMvsh0znZUkCZL0BtHoNnRdt3pYHSWTCXg8LmxubnS8z8LCPPx+H4rF4gBHRjTabFXIKkoebrebxQndKZfLMo19ADumsB6Pq+1XP97sM5mD2vYzmQMAwNnZacN+k8kEwuGl2s+BwEsAaLmt/nHmtkxmURMIvGzYZ/3XwsI8CoWL2mM2Nzc6/i08HhcAoFgstv2dOZ77Oj4+evBjB2VlRYKinODyUrN1Oru6uoZAYBqZzEHL8wEAdnd3UChcYG3ta3g8HgtGaC/1x0UymQDQeIyZx3/z8dl8XNZLJhPw+321++zu7mBzc6P2/K4/xszbmrfX6XhtfpxpdfVtwzG9ublR+/c0/5u6+TfQAxg2Egz+1chmf7Z6GGRzv//+u+H1fmH1MIZKpVIxtrf/aXi9Xxj5/Eerh9PWwcGPRiLxoe/7OT391ZiY8BoTE17j+vq6dvvGxjfGwcGPDT+fnv7a8NirqytjaemrhjG/eiUYr14JDfebn58zJia8xtXVVW2f9Y8zDMNIJD60PC6R+ND2b+B2P7v154ODHxv2dx+np78ar14JxtLSVw1/DztKpb43vN4vjO3tfxqVSsXq4bS4uroyJia8xqtXQsPf8uLiN8PtftbyHLCL5ufToDQfc+ZtzcddN7/b2fmXsbHxTcNtFxe/GRMT3obHvHoltBwnzcdhu+PVMAxjaemrhm1tbHzTcLyax2H9bUtLX9X2V/8ad3V11bLfYTPI581t+7JNIlsul6Bpmi0SIrK3XC7LOYbvQVVViOIMyuWybVLYh8hkDhAIvKylGaurbxEOLz1oW37/JBYWFtsmZ/e1vPw3ANV00xyn3++H0+m89XGrq2sA0JPkORxexsLC4oOS1UBgGmdn5/D7/fD7fQ1pkt00p7PmxcF24fF4sLX1DsVisaHFYHNzE06nE8nkd7Xbdnd3Ws4E1CsWiw1p311tC+OsWCzi+PgIe3vfNtzu90+iUNAQCEz3Zb9nZ6dYWFis/RwOLyMcXu7Lvqrb75zutjszNC5sU8jmcjlObE9dYSHbHV3XEY1uQ5LeQJKkoe6FrVQq2N3dwd7etygWyygWy5icnHzUNtfWvsbu7k5PCsm1ta9xcFB9Ezk4OMDa2td3PiaZTMDpfNrxNHMymagVN3etnpNMJlreVO9ra+sdzs7OcXR01NL2YCculxvZ7CEkSUIo9BrxeMzqITUIh5cRCEzj+PgIx8dHSCYTKBQusLX1rvbhJhxeQiZzgGTyOxSLZSST3yGR+NBQqK6uvkWxWKw934+Pf8Hx8RGL2TYe+9y/a9vmB+ZM5qeGojgQmMbm5kbDa8jW1rvah9Rey2R+qj0f2n31s4i2M9sUstksixO6Wy6Xhdvths/ns3ootmamsJeXGhTlZGSWeq5POVdX15DJ/PTgbZnpWSLx4dHjWlhYRKFwcdMb62lbnDb3+u3u7mB5ufWNx0zqdnd37hj/H9u6uLjA8fHHO1Pgu3g8Hhwf/wK/34+FhXlbJzxmOqsoeczO2iudTSa/g9PpxO7uDhKJD1hYWKwVGZnMAc7OTrG29nWt+FpYWMTa2tfIZA5QKFygUqmgULiA3++vbdNMF5tTx2HX3Bv+0Ofc06dPu75v/ZmdTv345vF621mfvb1vb46VuVrPa3Oynsn81Pb1wOPx4OzsvOsxU2e2KGQ5sT11S1G4dPFtmlPYbPZwJC6edDqdN2/887U3n3B46dEXKYXDyzg+Pnr0dszxdSpOgWp6U5+emOln8xtf9dR0GVtb727dp7mdvb1vUShcoFi8etS/obrN4k0aW8Dx8S+2T3hcLjfy+ROI4izm5kTbpLNOp7PWYuB0OhuKz4uLatLdnCAGAgEAwNnZGZxOZ0uqO6rqz7I8JlW8vr6ufd98wVbzRVVnZ+cN+2xXaJrH610flre23qFQ0GrJ+unpad9Sc7YWtGeTQlZBKMTihG5XLpdwcsJCtpNRTWFNq6trDW8+fr8fm5sbqFQqD96mWXAkEq2FgtPpbNl2oXDRMfU0x9dtP57H48He3rc4OPix4/bMU5TJZKLjvzMcXkYy+R1WV98+6m+xu7uDQOAlFhcXb1LZx7VuDFIkIuPjR8VW6axZqPr9kw3PGfP/UXMquLAwD+CPgiyT+Qnh8DISiUQtpQ+Hl2q92PSHap/3We3ncHi59joRCEw/KsWuzkRRLWYLhYva37/azjDfcF+/f/KmMO/P9GpsLWjP8kLWnNieS9LSXRRFwevXwaHt8+yneDw2cilsPXNKrHrPnz/vybarb3pXLQXC9PR0Qw9tpVJBIpHA9HTvLhzZ3NzoqmC8umodXz3z4rW72hHaOTs7RSDwEoVCAYWC1rf+vn7z+Xy2TGebmUWtmeI1f9Un8Vtb73B8/Evt9mLx6tEf3kaRx+NBIBBom4T24kyFqVAo4PT0tO7nC6yuvm24z/HxUU9fI6gLA5s7oYNs9mdjZeWN1cOgITA19cK2U0dZ5ffffzdE8S9GMPhXo1QqWj2cB3G7n7X9qp8iJ5H4YGxsfGPMz8/Vfj8/P9dxKp5ODg5+bNiHOfXP6emvDT/X339iwlu7f/20OonEh7bjbb69eZ/1X/VTXm1sfNPxfuZ2rq6uWh5vur6+rk2jdZ+/x6tXwr3/jnZnHhei+BfLjovr62vD7X5mvH37j4bbzefD0dG/G24/Ovp329vrmc+ti4vfejpWK6bfWlr6quW4qr+t+dhs/mo3dVXzsTc/P9dwTNf/znzONx/Dtx2vbvez2hRfp6e/Gm/f/qPluG03fV7zuOq3M8zsMv2W5YWsKP6Fc8fSnc7Pz42pqRdWD8NWYrF9w+1+ZsRi+1YPhciWYrF9w+v9wkilvh/4vjsVsoZRnWf41SuhVrReXPzW8CHk+vramJjwNnwoubq6Mubn54z5+bmej9WqeWRpuNmlkLW0tUDTNJRKJfY80p24ktcfNE3D7OwMFCWPjx8VRCKy1UMisqVIREY2e4hUKoVQ6DXK5ZLVQwIAHB//goWFxdo8sQsL8w29mE6ns/Z9/QVLHo/nwTN1lMslWy/xS/Tg5+jAyuk2trf/aUQi/23lELpSf+rg1Suh4VRFu9OL7W7rdHqk+TRKp9Ma9acgu91nu9MZnU7d2lmlUjG83i+G9tR5LzGFJbq/+pXtrEhn7cBMp9udAWUiSw/R6+dNLLZvTE29uPdz1NJC1uv9wvj999+tHMKd5ufnGopIcznH5mXp2vXF1PfwnJ7+2tATU/+75mX62i2Pt7Pzr4ZTVN3sM5H4UNtu89Ka7Zbps6ts9mcjGPyr1cOwVH3Pn92PGSK7MluUhrmn/KHMD8Fu9zNDFP9inJ+f137HQpYeoh+FrPkcDQb/2vVz1LLWAkXJ235ie3Ny80zmp9qVptXTP/8XBwc/DvTK0epcdRdjebXquK/klU5XT4uK4izy+RNbHzNEdiYIAhTlBF6vD6I4g3Q6ZfWQLHF5eYkvvwxCklZs025BVO/Tp0/48ssgZDlyZ7vBnwc0pha5XM72xcnp6Sm2trZabq9O9THd12Xx2hnHIrZcLkHTNKRSs1YPxRLR6DZ++CGN779PQRTH829A1EsOhwPR6Hv4fD6sr8t4/z5q9ZAsc3KiQFW5uhQ9nMfj6uv2Dw9ztUWzOrGkkDUnto/F4lbsvmvF4hWczvbL3j1//vxehWUgMN1xovRuJmtOJhNYWFi81xKUt80HOSxL46VS1QJuXOeOleX1m/9GIMvlkVvkgMgK6XQKsdg+IhF5bC6WjMdjLXPrTk1NIRJZx5dfch53ephisdyzbXV6jm5vv8fcXOeLvS0pZBVFwcyMaPvixON5jkrlGkDr8nVXV1d9nfTYXOfZFA4vj9wa291QFPt/4OknMz0SxVnIcgSqqiIWi9v+2CGyo3K5BFmOoFQqI5s9HNs2nSdPniAafW/7s6I0vp49c0GW5a6eo5b0yKZSqaFYyWt6errt0pXFYvFmNZxqIet0OhvWeQaqbQCPaQVoXpe9uYjtxz7txjydIAiCxSOxntnb53K5IAgv7jzVQkSN0ukURHEGXq8PijK+veaRiAxV/cwilmzpyZMniERkKMpJ18/RgReymqZB1ytD0e9XXTu9iHB4qbZM5dnZKcLhr7C8/LemC8AOcHb2x9J1u7s7fe2ftWKfg6YoCkIhvtiazHQ2FotDliOQpBXOC0l0h3K5hFDoNVKpFFKpHxCNvh/LMxpTUwLOz1VEIvJY/vvJ/qamqoHNfZ+jAy9k0+nUUH0SPD7+5ebirpfweFzY3NzA2trXDf2n5uTVu7u7tcmrK5VK21YA8/fFYrHhe1Mmc4BweKnWWuDxuJDJHLRsp9t9mtsJBF42fG93uq7j8DA3FMn9oIniLFT1MwAwnSW6RXMKO85ndwRBgMvltnoYRB099Dn6J8MwjD6Mpy1d12/eeE94QNGt0ukUVFVFKpW2eii2pih5yHIEgvCSvbNEN3RdhyS9QalURiwWH+sCthvVQKV3F+3QeBjk8+a2fQ00kTXnjmURS3fhkrTdGdd0tv6MRji8BMCc97n1LEY4vFS7vf6r+UxHdTq9+YbtJpMJJJOJrvdZf592X+a26vdpnu0xf19/xqR+++2+6s/mVCqV2pKn5rKnhcJFb//wQ0BR8hCEF0xhR9Tm5kbL87/5OKk/o2meSQXQclv94+qPJeCP143NzY2GfdZ/hcNLDdel1B/L7e4LVM+6tvv9wsJ8yxioSz1dluEOweBf2y49RlTv999/N7zeL6wextDJ5z8aXu8Xxvb2P41KpWL1cPru4ODHhtXyzNvarXhnGK2r29U7Ovq3MT8/V1vBzzAM4/r62pifn2tZxa+bfbZbhSaR+NBwv6urq4YV9q6vr42lpa8aHnuf1fmWlr5qGNvGxjcNK/2NukqlYqysvDG83i+MfP6j1cMZKsO2slci8cGYmPC2HM/Nx0S753/z8WoeJ/XHjnlsNq+U2XycNx9z5m2np7823Na8Wmen1TuH7Xgd5PPGFit7lcslfPr0aSgu8iJrVdNYPk/uy0xny+UyRHEGqqpaPaShsbu7g2Tyu4Z5mp1OJ46Pf7l1PubHMGc+8Xg8tf01j+G+1ta+rn2/t/ctnM6nY5HymCksAKjqZ75+jIG1ta9RLF41XPD8mG1lMge1dDWR+IDl5b/d+bi9vW97sn+gunongLE4XnttYIVsLpfD69dB9vDRnXK5LCf+fyCHw4FUKg1JkiBJbxCNbnNmgzsUi0U4nU9rBWWvmfNBr66uNRTFgcA0jo+PGtoNnE4nCgXtQfvJZH5q+DdUp+O7flRhbHfVXtgVyHIEsVgcqVSa7zFjZG3t61rbwGN4PB6Ew8u1YrbbVTs3NzduvZ954XZ1lqGfOt7PbAvy+yf79jo0ygZWyGaz2aGarYCskctl4Xa7x3aOx15ZWZGgKCe4vNRGOp1t7jd76JvafYq9bvdp/r4Tj8eDZPI7nJ7+0c+3uvq2oa91dXUN4fBy28efnZ13fNPb3d1BOLyEtbWvR7aQZQpL4fAyPJ7nbWf2ua/FxUUkEh9uTWN3d3eaetSvsLjYWsia/bW3pbX1fbx+vw+Tk5NIJr979L9jHA2kkOXE9tQtRVH4gadHXC43stnDkU5nw+HlWxcO6Vb9BRvtLtiqP93X7T7N39/GTGrM+05OTjbMW/0QmcwBnj59iuPjXzoWwcNM1/WbJZuZwhKwtraG3d2dRy8GFAhMw++fvJkCs/1xs7X1ruXY39zcbClYzWO607L05v7M7ayuruHo6GikFjQapAEVspzYnu5WLpdwcsJCttfGJZ19KI/Hg0rluvYm4vF4am8wW1vvsLX17lGn++qL2fo2gnB4CcfHRw33XV1dw8IZRtDJAAAgAElEQVTCIorFqwfvLxxe7ltfr9VUVYUozkDXdaawBKBaEC4sLHaYb/1pS3F4ddX52MpkfkKhoHV9FiMQmMby8jKOjo7a/j6T+alWzDbPWFJva+sdFhcXe9ImMY76XshyYnvqlqIomJkRma70QXM6G4/HrB6SrWxtvWuZSge4/U3vvorFIg4Ofmy4bXd3pyHNKRaLKBQK8PsnH7SPSqUCv99352nNYaPrOqLRbUjSG8iyzBSWGqytfd02lV1cXMTq6tvaz4XCBTKZg56tgFk9pg/w/PnzO+97cPDjrWdazJVEe9EmMXb6PWVCNvuzEQz+td+7oREwNfWC0+YMQKlUNILBvxqi+Bfj999/t3o4D5JIfDDc7meG2/2sNo1N/W31U+KYU1o1f5nTWplOT381Xr0Sar+fmPA2TLfTzT6vrq7a7sv8qp9eZ2npKyOR+GDMz8/Vfj8/P2dcXPzWMq7btlPv+vramJjwGm73s5YpgIbV+fm5MTX1wggG/2qUSkWrhzOShmn6rY2NbxqOBXO6LfNYrJ9+yzCq01rVHzf1x0X9a0PzsVm/j+Z91n/t7Pyr9rj61492X1dXV8bBwY8Nt9W/xlxc/NZym53ZZfqtvq/sNTs7g5UViaeL6VaqqkKWI1DVT1YPZWzE4zHE4zFEIjIiEdnq4RA10HUdsdg+crksZHmdM5n0EVf2ooewy8pef+7njjVNQ6lUsn0RWywWG1bT+eP29n+0ZDKBROJD7TTG1ta7Wk/a5uZGw6kBc8oNc1UPoNpXc1c/TH1vDQCsrr6t9dP5/ZPw+/14/vx5bb/mNB/teDwenJ2dd9yXHXAlr8GLRGTMzIhYX49AUfLY349ztgiyBfODrdvt4pLmRHS7fkbB29v/NCKR/+7nLnrKXM3jNjs7/2o4/Xd1dWXMz88Zb9/+o3afiQlvwwpB9SYmvLXv263uYRitK4NsbHzTsrpQ82nPpaWvaqdU6lcu6ebfZLVKpWJ4vV/wtKGFYrF9w+1+ZsRi+1YPhcZcLLZveL1fGKnU91YPZWwMU2sB2YddWgv6erHXqE1sXyhc4Pj4CJnMT7WLMTweD46Pf0GxWKwloh7P89pVx9VJjqspV3Van7ubwps1T84cDi+P1LQ6ipKH2+1m6mKhSETGx48KFCWP2dkZaNrDJuUneihN0zA7OwNVPYeinIzUewcR9U/fCllFycPhcI7UqcqzszMsL/+t7dQcy8vLOD01C1lP7erEQuHiZrWeCxSLVx2n8Tk7O621HzS3FQQC09jc3Gi44rG+nWHYjdoHnmHl8/mQz59AFGcRCr1GOp2yekg0JuLxGObmRIjiLLLZQ36oJaKu9a2QzeVykKTRKk6urq46zi9XnYuyfh7KIo6Pj+D3T2J5+W84Ozu7SWQbC1lzdY/6Htpme3vfwu/3Y2FhDh6PC4HAy5Y56ZqXp6wfl537Y8vlEjRN43yQNhKJyMhmD5HLZREKvUa5XLJ6SDSizBRWUfL4+FHhRYdEdG99KWR1XR+Zie3rk9Lnz593XHmjul67s3a/6+trXFxcYHJyEoFAABcXF6hUKnj69GnD48zVPW5bhxmoJrCFgoZisVxb1nIUJk/O5XIQxVnOCWkzPp8P2ewhvF4fRHGG6Sz1XH0Km8+fjNTZOyIanL4UsrlcdiQntg8EAjg4+LFtMXtwcIDp6Wo7gJnInp2dYWFhEX7/JM7OTnFxcdGxtcBcqhL4oxcXMPtj5xvu6/dPYm/v20ctY2kX2Wx2JD7wjCKHw4Fo9D1SqR+QSqWYzlJPMIUlol7qSyGbSqVGciUvv38SCwuLDVNdFYtFLCzMw+Px1PpaPZ7ndRd+VQvXQGAax8dHXS11WSgUav221Z8vGlYnAYDj46Na4TysFCUPABAEweKR0G0EQYCinDCdpUdLp6sfiJjCElGv9HweWU3ToOuVoep5bJ5H1uNxNfy+/sKrra13SCYTWF1923Ye2erjq/2ygUCgdtv0tFnIVmctyGQOaq0BzfsD0DArwcLCIpxOZ8P92l3slUwmsLu7U/t5d3cH4fAy9va+7eKvMHiKoiAUYho7DMx0VhRnIcvVeWdTqR9G7qwL9Ue5XIIsR6DrOrLZQxawRNQzPV/ZS5YjtTc9ok50XcfEhBfn5yqvUB4y9SsuxWLxofrQSoOXTqcQi+0jGAxBltf54ceGuLIXPcTIruylKHkoykmvN0sjRlHymJkRWcQOofp0VpLeQBByiMXiLFCogZnClkplpFI/sIWIiPqipz2yuVyWE9tTV9LpFJekHXKCIEBVP998/6LW80xUPb5n4PX6oCgnLGKJqG96mshyYnvqhqZpKJVKnK1gBDgcDqRSaShKHrIcYTo75pjCEtGg9SyRLZdL+PTpE/vl6E65XJbPkxEjirNMZ8ecouSZwhLRwPUskc3lcnj9Osgkhu6Uy2WRzR5aPQzqMaaz40nXdchyBJqmMYUlooHrWSLLie2pG4qSh9vt5vQ7I6w+nRXFGaiqavGIqF8UJQ9BeHHzPVNYIhq8niSy5hsVX8ToLrlcjh94xoCZzuZyWUjSG069NGLMFFZVzzkF24hoN5850TDoyTyyshyB2+3mUoN0q3K5hJcvBfz++yULmjFSfwFQLBbnB94h90fryEu2jhDRQPR1Hlld13F4mMP5OU8f0u0URcHMjMg3vjHjcrmRzR4inU4xnR1iTGGJyI4e3SOrKHlMTU1x7li6UyqVQjAYtHoYZJGVFQmKcoLLS429s0NGVVWI4szN959ZxBKRbTy6kE2nU+x5pDtpmgYAfAMcc2Y6K0kSJOkNotFtq4dEt9B1HdHo9s3/qyhSqTSTdCKylUcVsuVyiRPbU1e4khfVq09nZ2dnah90yD7MFPbyUoOinPBDKBHZ0qMK2VQqxRc3upOu61CUPCSJq77RH8x0NhgMYW5ORDwes3pIhMYUVpIkZLOHbB0jItt6VCHLJWmpG+bcsXwzpHZWViR8/KhAUfJMZy3WnMLy9Z2I7O7Bhayi5OFwODmxPd2JH3joLj6fD/l89fQ101lrMIUlomH04EI2l8vxVDHdqVwu4dOnT2xBoa5EIjLT2QHTtGqfMlNYIhpGD1oQQdd1TEx44fV6eQXrI3369AlTU1NWD6NvdF2H1+tDLBa3eig0ZOLxGOLxGLa3oyyu+oR/YyIaBrctiPDgQpZJSW98+WUQP/+cs3oYfeV2u3iakh5E0zSsr0fgcDgQi8X5POoR8+8KAPv7cbaIEZGt9byQpd657X8OEVVFo9vI5bKQ5XUmh49kprCRiMxlxYloKLCQtTEWskTdUVUVshyB2+1iOvsATGGJaFjdVis9emUvIqJBEAQBinICr9cHUZxBOp2yekhDIx6PYW5OhCjOIp8/YRFLRCODiazFmMgS3R/T2e6UyyXIcgS6rjOFJaKhxUSWiEZKczqrKHmrh2Q71WWhZyAIL5nCEtHIYiJrMSayRI9jprM+X3Wat3GfEtBMYUulMmKxOARBsHpIRESPwkSWiEaWmc5Wv38x1umsmcJ6vT4oygmLWCIaeX+2egBERI/lcDiQSqWhKHnIcgSCkBurdLY+hU2lfmABS0Rjg4ksEY0MUZyFqn4GMD7pbC6XZQpLRGOLiSwRjZRxSWd1XYcsR6BpGlNYIhpbTGSJaCQ1p7Oqqlo8ot5RlDwE4QUcDgdTWCIaa0xkiWhk1aezkvQGwWAIsrw+tOmsmcKq6jlisThEcdbqIRERWYqJLBGNPDOdvbzUIIozQ5nOmiksAKjqZxaxRERgIktEY8LhcCCbPUQ6nRqqdJYpLBFRZ0xkiWisrKxIUJSToUhnVVVlCktEdAsWskQ0dlwuN7LZQ0iSBEl6g2h0G7quWz2sGl3XEY1uQ5LeIBaLI5VK2z45JiKyAgtZIhpb9elsKPQamqZZPSSoqgpRnMHlpcYUlojoDixkiWismelsMBhCKPQa8XjMknHUp7CSJCGbPWQKS0R0BxayRESoprPZ7CEUJY/Z2ZmBprP1KayinGBlRRrYvomIhhkLWSKiGz6fD/n8CURxFnNzYt/T2XYprMvl7us+iYhGCQtZIqImkYiMjx+VvqazmqYxhSUieiQWskREbfQznY3HYwiFXjOFJSJ6JBayRES3qE9nQ6HXKJdLD96WpmmYnZ2BouSRzR4yhSUieiQWskREdzDTWUF4CVGcQTqduvc24vEY5uZEiOIs8vkT+Hy+PoyUiGi8cIlaIqIuRSIypqYEyHIEipJHLBa/sy1A0zSsr0cAAB8/KixgiYh6iIksEdE9CIIARTmB1+u7M51lCktE1F9/MgzDsHoQ48zjcaFYLFs9DCJ6AFVVIcsRuN2uhnS2XC5BklYAAPv7cRawRESPcFutxESWiOiB2qWz6XQKojjDFJaIaACYyFqMiSzRaFBVFV9+GcSzZy6kUmkWsEREPXJbrcSLvYiIekAQBACAopzA4XBYPBoiovHA1gIioh5iEUtENDgsZImIiIhoKLGQJSLqo0qlAo/HBb/fh2Kx2PH3q6tvLRgdEdFwYyFLRDQAlUqFxSoRUY+xkCUiGpBC4QLJZMLqYRARjQwWskREA7CwsAi/fxKJxIe2LQbNkskE/H4fPB5XrfWgm8cREY0TFrJERAOyt7fXVYvB5uYGdnd3sLX1DsViGWdn5ygWiwiHv0KlUhnQaImI7I+FLBHRgPj9k9jaendri0GhcIFM5gCrq2sIh5cBAB6PB3t7eygWi0gkPgxyyEREtsZClohogFZX1+D3T2J3dweFwkXL78/OzgAA09PTDbf7/ZPweDw4Pj4ayDiJiIYBC1kiogFLJr+D0+nE5uZmy++ur68BAE6ns+V3TudTthYQEdVhIUtENGAejwdra1+jULhoaRV4+vQpALQtWCuVa3g8zwcyRiKiYcBClojIAmaLQXOvbCAQAACcnp423F4oXKBYLMLv9w9sjEREdsdClojIImaLQT2/fxLh8DKSyQQymQMAQLFYxObmJjweD7a23lkxVCIiW2IhS0RkEbPFoNne3rfY2nqH3d0deDwuBAIv4fF4kMn837a9s0RE4+pPhmEYVg9inHk8LhSLZauHQUQ9wOOZiKj3bnttZSJLREREREOJhSwRERERDSUWskREREQ0lP5s9QCIiIaZJK1A1/+Y8zUUel373uVyIxaLWzEsIqKxwEKWiOgRBEHA+/fR2s+fPn2qfb+/H7JiSEREY4OtBUREj7CyIuHZM1fL7VNTUwgGWcgSEfUTC1kiokdq1z6wvf3egpEQEY0XFrJERI8kCAKmpqZqP79+HYTP57NwRERE44GFLBFRD5ip7JMnTxCNMo0lIhoEXuxFRNQDLpcbkYgMAHA4HBaPhohoPDCRJSLqAU3T8Msvx1CUPMrlktXDISIaCyxkiYgeKR6PYW5OxPz8AkRxFqI4g3Q6ZfWwiIhGHlsLiIgeSNM0rK9HAAAfPyq1C7xmZkRI0goUJY9YLA6Xy23lMImIRhYTWSKiBzBTWFGcRT5/0jBLgc/ng6KcwOv1MZ0lIuqjPxmGYVg9iHHm8bhQLJatHgYRdak+hd3fj985zZaqqpDlCNxuF9NZIqIHuK1WYiJLRNSldDqFUOh12xS2E0EQmM4SEfUJE1mLMZElsr9yuQRZjkDX9a5S2E7q09lU6gdO00VE1AUmskRED5ROpyCKM/B6fchmDx+1Yld9OisIL6Ao+R6OlIho/DCRtRgTWSJ7MlPYUqmMWCwOQRB6un1VVSFJbyAILxGLxZnOEhF1wESWiOge6lNYRTnpeRELVNNZVf188z3TWSKih+A8skREN3RdhyS9QalURir1Q18K2HoOhwOpVBqKkocsRyAIOaazRET3wESWiAiAouQhCC/6msJ2IoqzTGeJiB6AiSwRjTVd1yHLEajq+UBS2E6a01lRVBCNvmc6S0R0CyayRDS2zBQWAFT1s2VFbD0zndV1HaI4A1VVrR4SEZFtMZElorFTn8LGYnGI4qzVQ2pgprO5XBaS9AbBYAiyvM50loioCRNZIhorzSms3YrYesFgCIpygstLjeksEVEbTGSJaCzYPYXtxOVyI5s9RDqdYjpLRNSEiSwRjTxVVSGKMzff2zuF7WRlRWI6S0TUhIUsEY0sXdcRjW5Dkt4gGo0ilUoPdZJpprOSJEGS3iAej1k9JCIiS7GQJaKRZKawl5caFOVkKFPYTsx0VlXPMTs7A03TrB4SEZElWMgS0UipT2ElSUI2ewiXy231sHrOTGdFcRZzcyLTWSIaSyxkiWhkNKewKyuS1UPqu0hExsePChQlz3SWiMYOC1kiGgnjkMJ24vP5kM+fMJ0lorHDQpaIhpqmaZidHa8UthOms0Q0bljIEtHQisdjmJsTEQyGxi6F7aQ5nU2nU1YPiYiob1jIEtHQMVNYRcnj40dlrFPYTsx0NpfLIhR6jXK5ZPWQiIh6joUsEQ0VM4UVxVnk8yfw+XxWD8m2zHTW6/VBFGeYzhLRyPmTYRiG1YMYZx6PC8Vi2ephENmepmlYX48AAPb34yxg70lVVchyBG63C7FYnG0YRDQ0bquVmMgSke2l0ymmsI8kCAIUheksEY0WJrIWYyJL1Fm5XIIsR6DrOlPYHmI6S0TDhIksEQ2ddDoFUZyB1+tjCttjzemsouStHhIR0YMwkbUYE1miRmYKWyqVEYvFIQiC1UMaaWY66/P5EIvF4XA4rB4SEVEDJrJENBTqU1hFOWEROwBmOlv9/gXTWSIaKn+2egBERPUpbCr1AwvYAXM4HEil0lCUPGQ5AkHIMZ0loqHARJaILJXLZZnC2oQozkJVPwNgOktEw4GJLBFZQtd1yHIEmqYxhbURprNENEyYyBLRwClKHoLw4uZ7prB2VJ/OiuIMVFW1eERERK2YyBLRwJgprKqeIxaLQxRnrR4S3aI+nZWkNwgGQ5DldaazRGQbTGSJaCDqU1hV/cwidoiI4iwU5QSXlxrTWSKyFSayRNRXTGFHg8vlRjZ7iHQ6xXSWiGyDCyJYjAsi0ChTVRWS9AaC8JIXDI0QLloxWjwel9VDoCE1qPrltlqJiSwR9Zyu64jF9pHLZZnCjiCms6OHgQrdl10+ALFHloh6SlVViOIMLi819sKOuJUVqdY7Gwq9hqZpVg+JiMYME9kB0zQNuq433FZ/4QRP0dGwqk9hZXkdKyuS1UOiAahPZ+fmREQiMiIR2ephEdGYYCE7YLlcFj/8kG647csvgwCAqakpCMKhFcMiehRVVSHLEbjdLijKCVwut9VDogFbWZEwNSVgfT0CRcljfz8On89n9bCIaMSxtWDAZHkdT548afu7SGR9wKMhehxd1xGNbkOS3kCSJGSzhyxix5jP50M+fwJRnMXcnIh4PGb1kIhoxLGQHTCHw4Fo9H3L7a9fB9lWQENF06p9kZeXGhTlhK0EVBOJyPj4UYGi5DE7O8PeWSLqGxayFggGQ/B6vbWfnzx50ra4JbKreDyGUOg1gsEQU1hqi+ksEQ0CC1mLbG//UbhK0t85bQ0NBU3TMDs7A0XJI5s9ZApLd6pPZ0Oh1yiXS1YPiYhGCAtZiwiCgJkZEc+euXiFLw2FeDyGuTkRojiLfP6EF/JQ18x0VhBeQhRnkE6nrB4SEY2IoVnZS9d1aJqGy8vW6auGVaVSwf/8z//gv/7rv6weSs9MTQlwu1081TxCNE3D+noEAHglOj1a/QwXsVicrxU2wBUm6SEG+by5bV+2TmTL5RKi0W3Mzs5gYsKL9++3R+qiAafTOVJFbKlUQjy+j5cvBQjCFKLR7YY5cmn4MIWlXhMEAYpyAq/Xx3SWiB7NlomsqqqIx/ehaRpEcRaiKEIQXrKPdIioqgpFyUNRFACALMsIBkMWj4q6VS6XIMsR6LrOFJb6humsPTCRpYewSyJrq0LWLGBLpTJkWYYozrJ4HQG5XBa5XBalUhnRaJRLltpcOp1CLLYPSfo7+7ep7+pXhItG3/MDrwVYyNJDsJBtEo1uI5fL8s1zhFXfqLbh8/mQSv3ADyk2Y6awpVIZsVic8xrTQJnprM/nQywW5+vDALGQpYewSyFreY+sruuYnZ3Bp08qFOWERewICwZDUNXPcDicEIQXI9XvPOzS6RREcQZerw+KcsIilgbO7J11OBwQhBdQlLzVQyKiIWBpIatpGgThBbze6tQs7I8afQ6HA6lUGtHoe8zNicjlslYPaayVyyWEQq+RSqWQSv2AaPQ9kzCyjMPhQCwWRywWhyxHIEkrIzNLDRH1h2WFrLm8pST9HbFY3KphkEWCwRD292NYX5dZzFqEKSzZlSjOQlU/AwDTWSK6lSWFrK7rtSKWrQTjq76YZZvB4Oi6DklaYQpLtmaevRmWdDaZTMDjcWFzc6PjfRYW5uH3+1AsFgc4MqLRZkkhGwq9hijOsoilWjEbCr229ZvUqFCUPAThBVwul21SWI/H1farH2/2mcxBbfuZzAEA4OzstGG/yWQC4fBS7edA4CUAtNxW/zhzWyazqAkEXjbss/5rYWEehcJF7TGbmxsd/xYejwsAUCwW2/7OHE+v7O7uwO/3oVKp9GybD9Wcztp1burV1TUEAtPIZA5ang9A9W9aKFxgbe1reDweC0Zof4HAy5bndqfn9dnZKRYW5mv3W119W3u+Nh9zyWQCQOtrTafjqflxJvO4No+53d0drK6+bfv7Qb2mEQBjwGKxfUMU/2JUKpVB75psLBL5byMY/KvVwxhZlUrFWFl5Y3i9Xxj5/Eerh9PWwcGPRiLxoe/7OT391ZiY8BoTE17j+vq6dvvGxjfGwcGPDT+fnv7a8NirqytjaemrhjG/eiUYr14JDfebn58zJia8xtXVVW2f9Y8zDMNIJD60PC6R+ND2b+B2P7v154ODHxv291Cnp78ar14JxtLSVw1/G7vI5z8aXu8Xxvb2P235HnJ1dWVMTHiNV6+Ehr/fxcVvhtv9rOU5YBfNzyervXol3PpcPjr6tzEx4W04Xt++/UfD3/3t238YR0f/bvv4paWvjIuL32o/t/v3Nx+LBwc/Nvz/u7j4zZifn2u4LZH4UBtT82vFXf+mYTTI581t+xpoIlsulxCPx7C/z6lVqFE0+h6lUpn9sn1gprAAoKqfh3Ye30zmoCGxWV19i3B46UHb8vsnsbCw2DY5u6/l5b8BAI6Pj2rj9Pv9cDqdtz5udXUNAHqS0oTDy1hYWHxwKlupVBAOL2FzcwN7e98ik/npzvFbwUxnLy81iOKM7dJZj8eDra13KBaLDS0Gm5ubcDqdSCa/q922u7tza/pXLBaxuvq24T63tS2Mk83NDWQyPyEcXq7dlkx+13BMezye2rFlJqXmGZBi8Qoez/N77fPi4gKLi4u1n/3+SaytrfXtOLkr3eVz4Q8DLWRlOYJIROYqQdTC4XAgGo0iGt1mi0GPmL2wshxBLBZHKpUe2g+QlUoFu7s72Nv7FsViGcViGZOTk4/a5tra19jd3elJIbm29jUODqpvoAcHB1hb+/rOxySTCTidTzueZk4mE7Xi5q65GpPJxM2p1sVb79fpsX6/D36/H2dn5wgEpu+9jUFyOBzIZg8hSRIk6Y3tXjPC4WUEAtM4Pj7C8fERkskECoULbG29qxU94fASMpkDJJPfoVgsI5n8DonEh4biZHX1LYrFYu35fnz8C46Pj8a+gDk7O0UgMA2/v/X4X17+G46Oqh8onz59iuvrawDVItTvn8TZ2RmA6utJpwLUbONZXV2rfdgEgMnJSRwcHDR8WFxYWGz4cNJLq6trtf/37b729r7ty36H0cAKWVVVoWkaVlakQe2ShowozsLn83Ht9R4YlRS2Wf2bz+rqGjKZnx68LTM9SyQ+PHpcCwuLKBQublIUT9vitLkXd3d3B8vLyy33M5O63d2dO8b/x7YuLi5wfPzx3ulQOLyE3d0dZDI/YWvr3b0ea7WVFQmKcmLLdDaZ/A5OpxO7uztIJD5gYWGxlh5mMtViaG3t69oHj4WFRaytfY1M5gCFwgUqlQoKhQv4/f7aNv3+SRQK2tgXMMVi8ZYi1INK5br2fbFYRKVSwdnZKba2tnBxcXHTF9uaxtb3orcTDi9jcXERu7u7DalofR/56upaQ0pc7+zsnL3RfTKwQjadTkGS/j60iRANRiSyjmyW7QUPpes6otHtkUhh6zmdzps3/j8u7giHlx59gVM4vIzj46NHb8ccX6fiFAACgemGROXs7BxHR0ctp5Srp6bLdxaW9clMoXCBYvHq3uM2C1izoB02Lpfblums0+mstRg4nc6G4vPionp6uzk9DwQCAICzszM4nc6WVHfcmUVmtVhtfxFi9e/9FEC18C8Wi7UENxCYrh0n7QpK83i6zerqGo6Pf0GxWEahUJ1pZ2Fh7jH/rI7YWtC9gRSyuq7j5ERBMBgcxO5oiJlX0XPeyPtTVRWiOINyuTxSKayp+VSb3+9vSUTuyyw4EonWQsHpdLZsu1C46JgGmePr9tS8x+PB3t63ODj4seP2zFObyWSi478zHF5GMvldw1Xb97G6uoZCQUOhUOj57AeD0pzO2mE6P7NQ9fsnG54z5v+j5iv0FxbmAaB2OtzsAU0kErWUPhxeqvVij6tAYBpnZ6cNM36YDg5+rPWxVhPZK1xcXGB6unpM+v3V9oDbktH6Yrb+A4TH42o4vpo/oPQaWwu6N5BCVlHymJkRuXIXdUWSJCiKYvUwhoaZwkrSG0iSNDIpbD1zSqx6z5/f72KNTsLhZRSLVy0FwvT0dEMPbaVSQSKRqL0p9sLm5kbbXr9mV1et46tnXrz20FTV6XQik/kJe3vfYnNzA+Hwki2m3rqP+nQ2FHqNeDxm9ZDaMovaQkFrW6DUJ/FbW+9qCWA14b169Ie3UbC39y3C4aWGQnN19S2Oj48aTu1Xn9cHtQ8Vk5OTOD4+wtOnT+/cR7FYbPmQGQ4vNfTUV0CFCrsAABVfSURBVNuFevM6RI8wiGkTVlbeGNnsz4PYFY2A33//3fB6v7B6GEPh/PzcmJp6YQSDfzVKpaLVw3kQt/tZ26/6qWoSiQ/GxsY3xvz8XO338/NzLdNj3eXg4MeGfZhT5Zye/trwc/39Jya8tfvXT8eTSHxoO97m25v3Wf9VP83VxsY3He9nbufq6qrl8abr6+va1FmPtbPzL2Niwvvo7VilVCoaovgXQxT/Yvz++++WjOH6+tpwu58Zb9/+o+F28/nQPDXU0dG/295ez3xu1U8d1Qt2mX7r1Svh1mOg3unprw2vB2/f/qNlyrilpa+M+fm52s/m8WP+jZuPp+av+unxJia8RiLxoeH1oN0+zdeSTtsZJXaZfmsghazX+8XQvsmSNbzeLyx7AxoGlUrF2N7+p+H1fmGkUt9bPRwiW4rF9g23+5kRi+0PfN+dClnDqM4z/OqVUCuoLi5+a/gQcn19bUxMeBs+lFxdXRnz83MNhVmv2KWQpeFil0K2760F5XIJAEa6raDTyj3NK5OYPVHmSkFA42pBZjN7uxV+/H7fWDX8+3w+XF5a3+dmR5pW7QO8vNSgKCecCYSog0hExsePChQlj9lZe/TOAsDx8S9YWFiszRO7sDB/sypYdRYOs9UD+ONq+ur7h+fBM3WoqmqLC+GIeq7fVfT5+flYrNjUbuUew6ie2qg//bm09FXtFIXp6uqq7Qo/zac5l5a+GsjKR3YQi+1bkqLYXSy2zxSW6AGsTGft4LbXDiay9BBjlcg6HPZbIcZK5tyV923YTya/w8HBj2PT6D8u/85uaJqG2dkZKEqeKSzRAzSns+bZwnHyv//7v3j/PgpBmLLVvLtEjzGAQrY8dit5nZ2d1q6wzmR+apmOx+PxPOgKY6fTeTMpduu0I6Nmakpga8GNeDyGuTkRojiLfP5kpNt0iPrJ5/Mhnz+BKM5CFGfGdvGV//ynjC+/DCIUej2WBT2Nlj9bPYBRYq7cA+DOuSS3tt4hEHjZ1VKW9ey4/jn1jyxHcHiYw/ffp0ZuXlgiq0QiMlwuF9bXZbx/H7V6OJb59OkTXr4UrB4GEYBqH7fD4bh3+MlCtofMZv2zs9O2E6zXM1cC2tzcuNfExjzdPl5isTjcbjdkOQJZLrOlgKgH4vEY4vEYIhEZkYhs9XAGwvw315uamsL29nvMzYkWjYroD58+qYjHY3j9Ooho9H3X86H3vbXA4XDY5krRQam/+rRQuOg4kXl15Y6re62kU11/++4J1Ifdp08qvN7xaknpJBKRkUr9gFQqxVOBRI9Q32v+8aMyNkVss2fPXPj++xSy2cOxa/0j+zs8zEEQXnS9qEnfC1mv1wddH98UsVAo4PS0c6Fqrs/ejXB4CcvLfxub9oJx+Xd2QxAEKMoJvF7fWPf2ET1Uc6/5OBZwT548QSQiQ1U/sVWJbO1///d/EY/Hurow8U+GYRj9HEy5XLpZ+/r/6+duLJXJHGBzc6Pj78PhZeztfYtA4GVteTuPx4Ozs/Ob3y/dJLPVnzc3N5DJHDRsw2xFMNdeH3Wh0GsEgyEEgyGrh2I7qqpCliNwu12IxeK8+IvoFuVyCZK0AgDY34+PZQELVF833G5X29cLj8eFYrFswahomJnXBPXbs2cu/Oc/5Y7P0b4XsgDg8/0fKAqvtqbu+Xz/h6e9bqHrOmKxfeRyWcjyOntnidpIp1OIxfYhSX8f2zaCbrCQpYfo9fOmXR93JCJjZUXCxIS3474GcrGXILyEqqoIBlnI0t3MnmoWsZ05HA5Eo+8hirOQ5QhUVUUsFu+6OZ5olJXLJchyBKVSmR+IiYbQzIyIaDTaVQDa9x5ZwOzvUwaxKxoBnz6p7N/qktk763K5IAgvoCh5q4dkmXZLO3da1rlYLDYsD72wMF+bn7lYLDZsw5wTunk56bOz09qy0+2+zMeZmu+fTCYalqs2p+/r9FXfblSpVGrLmzaPf9yl0ymI4gy8Xh8UZTx7YUdZ/XFutuolk4mWY7P+Z7P1r/m2+seZ2zKZx/vm5kbb1xbzGK+fSaib14NOS9ovLMy3jGEceb1e/PxzDqlUuvuz+INYWqxUKhpu9zOjUqkMYnc05KamXhj5/EerhzF08vmPhtf7hbGy8masj7WNjW9alneud3V1ZUxMeI2dnX/VbkskPhhu9zPj4uK32s/1v6+XSHxoWCq6eRlqw2hdstpchvrq6sowDMO4vr42lpa+alh28fT0V2Nj45vaz/XLVjf/m5aWvmq478bGNy3LXI+bUqloBIN/NaamXhjn5+dWD2eoDNsStYnEB2NiwtuyLHz9MWb+3Ozg4MeG49c8duqPJ/N4rX9883FvGK3HoXnbXa8H7Za039n519Adw71+3pRKxQftayCJrMvlxsyMyCut6U7m1YlMZO9PFGehqp8BYOzT2dvs7u5gbe1rbG29q922urqGZPI77O7uAqhejGmmI4XCRUO6e3V1BY/Hc699VtPY6drjnE4nksnvHjUzR/1iKnt738LpfDq2iU5zCisInOR/1K2tfX3v6Stv21Ymc1BLVxOJD1he/tudj9vb+7Yn+wdQez0a12MYwIOvoxpIIQsAKysSUqnvoev6oHZJQyge30coxJkKHsrhcCCVSiMWi0OWI5CkFR5zTc7OTtvO/rGwsIhi8QqVSqWhkD07O4PfP4mLi/rWg/aFbDi8VCtazbmkgerc0sfHRw2tDk6nE4XCw+bYzmR+ahhDpVJBpXI9dlPW6boOSVpBKpVCKvXDvSZRp+FnLir0WB6PB+Hwcq2YPTs7xcLC4p2P29zcuPV+nV4PmpmtQn7/5L0/JNMAC1lBEODz+ZjKUkeqqkLTNF6B3wNMZ9srFou3Fnsez/ObQvY5isUrAMDR0RG2trbqemiv4PE8b3ic2U/XKZ3xeDxIJr/D6ekfvXurq28b+loDgemOq/zt7X2LcHi57e92d3cQDi9hbe3rsSpkFSUPQXgBl8vFFHZMhcPL8Hiet0xX+RCLi4tIJD7cmsbu7u409LUWi1dYXGwtZO96PQAae+L9fh8mJyeRTH736H/HOBpYIQsA29vvEY/Hxm6lL7qbruuQ5QgTlR5qTmej0e2xTWfNZMTj8dy6zHOxeAWn01n7KhaLKBavEAhMw++fRKFwgUql0lIwZjI/oVgsIxCY7rhtM5UpFqvzIU5OTt7MIf3wU4mZzAGePn2K4+NfOha6o8ZMYWU5glgszteMMbe2tobd3Z1HL99uHuOZzEHHY2lr613t+C0Wy9jb+xabm5stBWu3rwfmdlZX13B0dMQl6B9ooIWsz+dDJCJjfT0yyN3SEIhGt+F2u7gAQh+Y6Wy5XIYozty5SsqoCwSm285mcHx8BI/nea1I9Xie4/j4qHbqcHJyEolEoiWNrZfJ/FR786rfRzi81LJU9erqWq2d4aHC4eWxWSQF+COFBQBV/cxeekIgMI2FhcW2qazT+bSlOLy66ny8ZTI/oVDQuj6zEQhMY3l5GUdH7Zeh7/R60Gxr6x0WFxd70iYxjgZayAKoTUotyyxmqSqXy0JR8ojF4lYPZWSZ6awkSZCkN2Odzm5tvUMi8aHhTSOZTGB19S22trZqt3k8HiQSHzA9XX0jCgQCN8Vudz1sBwc/NqStu7s7DclNsVhEoVCA3z/5oH9HpVKB3++78xTmKGhOYVOpNFNYqjGXem8uWhcXF7G6+rb2c6FwgUzmoKv+124Ui0UcHBzg+fPOH25Nza8HzVZX11AsFnvSJjF2ejp3QpcqlYrh9X5hpFLfW7F7spHz83PD7X7G6XIGaFSnKdrY+MZwu591/KqfEufq6qo2/ZXb/cyYn5+rTb1lMqfkur6+rt326pXQMC3Xq1fCrfs0pwJaWvrKSCQ+GPPzc7fu0zCMW7dT7/r62piY8Lb820YNp5Xrv2Gafqv5ODePDfN4bT5Wdnb+Vbvvq1dCw7FS/xpQP/VV/e1u97NbX1vu+3pwcPBjw231U3pdXPzWcpudDfJ5c9u+BrJEbTuapiEUeo1o9D1PJ48pVVVv0kE+B6xgLt8ZDIYgy+tMuMhW6pdhjsXibCPoIy5RSw8xyOfNbfsaeGuByefzIZs9RDS6zTaDMZTLZfHll0EWsRZaWZGgKCe4vNQgijO8CJNsQ1VViOIMyuUye2GJ6FaWFbJAtZg130hDodcol0tWDocG4I/ZCbbx8885FrEWc7ncyGYPIUkSQqHXiMdjVg+Jxpiu64hGtyFJbyBJEnthiehOlhaywB9vpF6vD6I4wzfSEaYo+ZuUpQRV/cx5H23ETGcVJY/ZWaazNHhmCnt5qUFRTjifNBF1xfJCFqheUR2Nvkcq9cPN9CpTyOWyVg+LekRR8jf90FHIsoxs9pApiw25XG7k8ycQxVnMzYn8UEkD0ZzCZrOHD16qkojGj2UXe90ml8sinU6hVCpBFGchiiJ7pIaMpmk302opAABJkhAMhljADglN02rzPe/vx+Hz+SweEY0iVVUhyxG43S7EYnEWsBbhxV70EHa52MuWhazJLIY+fVJxeXmJqakpuFxuuN3VFzuv1wuHY3yWZLSrT5+qE+xXKhVcXmrQNA0OhxOCIPBDyJCLx2OIx2OIROTaHNBEvRCPx5BKfQ9ZXmcbgcVYyNJDsJC9J13XoWlarWgCAFU9t3BEZPJ6fbWVULxeL/7/9u6fp408j+P456St7Sdg5gEwYttooqT1zwjK2KslV8Vz0pGrPBS3pAnXhL2CmerCFfZWCycZyo3icZtVRrQg8wAMT8DzBHIFTGQRQwKxPX/8flWIZXe/iqLowzv2YNs2ZaVAqLOYpuT3U6lUosJmBEMWD8GQBZArFDR8Lwp/NjFk8RBZGbI/zOUCALnXanmqVo1ct/n5RwpT0/Atxqv++/chVR/A1GTiqQUA8iF59nPyuLxOp532Sci4IPC1unr1Wvler8+IBTBVFFkA95I8Ls+YmjyvRZ3FRFRYAPNAkQXwII7jUGcxUafTVqPxjAoLYOYosgAebFKdbbd/43nBC+ry8kKe11Icx+p2jxmwAGaOIgvgu43XWcd5pDDspX0S5qzTacuYqpaXbUYsgLmhyAKYivE667ov5DhH8v2AOltwSYW9uLhUu/2bHMdJ+yQAC4QiC2CqHMdRFJ1cf0ydLbLxChuGfUYsgLnjByIAmJkw7MnzWnKcx9TZAonjWK77QhcXl/L9gAGbc5ZVSfsE5FQWfiACQxbATMVxLM9rKYo+yvcDGVNL+yR8h+Sbk3q9Ic/b4psTADPHkAWQumQAGVPTzs6/GEA5M/4NCa+FBTBPdw1ZXiMLYC6MqSmKThTHsYypKoqitE/CNwrDnhznkSQpik4YsQAygyILYO6Ojrra2XnNX09nHC8LAZAFFFkAmVKvNxSGfZ2fD6izGXWzwjJiAWQRRRZAqjqdtnx/jzqbEVRYAFlDkQWQWc2mS53NiCiKZEz1+mMqLIDso8gCyIykzrru39RqeWmfszDiOJbv7+noqEuFBZA5FFkAuZDU2Sj6qFqtqsFgkPZJhZdU2PPzgcKwz4gFkCsMWQCZUqksqds9ljE1ra4aBYGf9kmFFMexdnZey3VfyHVddbvHqlSW0j4LAO6FIQsgk1otT+/fhwrDHnV2ym5W2GbTTfskAHgQhiyAzLJtW71enzo7RVRYAEXCm70A5MJgMNDWVkuStLcXyLbtlC/Kl+TXr1QqyfcDBiyA3ODNXgBy72ad7XTaaZ+UG0Hga3XVqF5vUGEBFApFFkDuUBe/DRUbQBFQZAEUSlJnl5dtGVOlzk6QVFhjaur1+oxYAIVEkQWQa1EUyfNaWlqqUGdFhQVQPBRZAIXlOI7CkDorXf1kNCosgEVCkQVQGItaZy8vL+R5LcVxTIUFUDgUWQAL4WadDcNe2ifNXKfTljFVLS/bVFgAC4ciC6CQkjpr27Z8P1CpVEr7pKlKKuzFxaV8P5DjOGmfBAAzQZEFsHCSOnv18aNC1dnxChuGfUYsgIVFkQVQeGHYk+e15DiPc11nqbAAFhFFFsBCM6amKDqRlN86e3TUpcICwA0UWQALJW91No5jeV5Lg8GACgtgIVFkAeDaeJ01pqooilK+6HZh2JPjPLr+mAoLADdRZAEsrKTO1usNed5WZupsUmGj6KN8P5AxtbRPAoDUUGQBYAJjagrDvs7PB5mps+MVNopOGLEAcAeKLADo6pFWvr+XWp2lwgLAZBRZAPiKZtNNrc5GUUSFBYAHoMgCwA3zqrNxHMv393R01KXCAsAtKLIAcA/jdbbReKbBYDD1/0cURTKmqvPzARUWAB6IIQsAE1QqS+p2j1WvN9RoPFMQ+FP578ZxrJ2d13LdF3JdV93ucWaelgAAecOQBYA7NJtXYzMMe6rVqt9VZ8crbBj21Wy6U7wUABYPQxYAvsK2bfV6fRlT0+qquXednVRhK5WlGV0LAIuDN3sBwD0MBgNtbbUkSXt7gWzb/qavL5VK8v2AAQsA98SbvQBgSu5TZ4PAV6PxTPV6gwoLADNAkQWAB7qttt632gIAbkeRBYAZSOqs4zyWMVV1Om0Fga/VVSNjaur1+oxYAJghiiwATEEURfrpp7qWl5epsAAwRXcVWYYsAEzJXX/YAgAehpcWAAAAoHAYsgAAAMglhiwAzNju7htZVkW//PLPO//52dnpnC8DgHxjyALAnBweHujduz/SPgMACoMhCwBztLv7RqPRKO0zAKAQGLIAMCebmy81HA61u/vmq197dnaqjY2fZVkVWVZFT5481v7+2zlcCQD5wZAFgDlZX1/X2tq6Dg8PdHh4cOvXDYdDbWz8LEk6OxtoOLzU8+d/1e7uG8YsAIxhyALAHP36679lWZbevv3PrS8xSIrt/v5/VS6XJV3V3LW19Tv/PQBYNAxZAJijcrmsly//oeFweOtTDP7884NWVn78PGITT58+1Wg04g1jAHDth7QPAIBFs7HxXKenpxNfYjAajTQajb4YsZI+f44iCwBXKLIAkILt7VcTX2JQLpdVLpcnjtXkc5Zlze1OAMgyhiwApKBcLmt7+5WGw+EXVfbJk6c6Ozv9Ysx++PBBkrSy8uPc7gSALGPIAkBK1tbWtbHx/IvPb2+/kiRtbv7985jd33+rd+/++FxyAQAMWQBIVfIUg3GWZenw8H+SpJUVW5ZV0cHB79refqXNzZdpnAkAmfSXT58+fUr7CAAoAsuqaDi8TPsMACiUu/5spcgCAAAglxiyAAAAyCWGLAAAAHKJIQsAAIBc4id7AcAUWVYl7RMAYGHw1AIAAADkEi8tAAAAQC4xZAEAAJBL/wcLVhSq+SLrlgAAAABJRU5ErkJggg==\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n</div><div class=\"specification\">\n<p>The algorithm is to be altered to restrict the values that are input to whole numbers between 0 and 1000.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the logic error in the algorithm.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the error in the algorithm identified in part (i) can be corrected.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the name of the method that could be used to restrict the values that are input.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A further change has been requested for the algorithm to enable it to calculate the average of all the numbers entered. The average will be output when the algorithm terminates.</p>\n<p>Based on the flowchart, construct this algorithm using pseudocode. You must include the required changes:</p>\n<ul>\n<li>correction of logic error</li>\n<li>only allow input of integers between 0 and 1000</li>\n<li>calculation of average of all numbers entered</li>\n<li>output of final average.</li>\n</ul>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em></p>\n<p>The algorithm will always output zero as the value of LOWEST, for any set of the input values (because all input values are greater than or equal to 0);<br/>The LOWEST is initialized to 0 and negative inputs are not allowed, zero will be the permanently lowest number;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p>Example 1:</p>\n<p>Replace LOWEST = 0;<br/>With LOWEST=99999999/ any very high number;</p>\n<p>Example 2:</p>\n<p>Before the loop/ <em>Accept “at the beginning”</em>;<br/>The first number NUMBER should be inputted and LOWEST set to NUMBER / LOWEST=input(NUMBER);<br/>Loop only 999 times (because 1 number already inputted)/ condition in the loop should be changed to COUNTER&gt;998;</p>\n<p>Example 3:</p>\n<p>If statement should be used within the loop;<br/>after the statement ‘input NUMBER’;<br/>For example, if COUNTER==0 then LOWEST = NUMBER/ set LOWEST to the first inputted number;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>Validation (check);<br/>Data type check;<br/>Range check;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[8 max] </strong></em></p>\n<p>Correct initialization of HIGHEST, LOWEST and TOTAL;<br/>Correct use of loop for 1000 inputs (doesn’t have to be while loop);<br/>Checking if NUMBER is between 0 to 1000(accept inclusive or exclusive range);<br/>Checking if NUMBER is a whole number;<br/>Running total of numbers (for average calculation);<br/>Correct comparison and changing the value of HIGHEST if needed;<br/>Correct comparison and changing the value of LOWEST if needed;<br/>Appropriate calculation of AVERAGE;<br/>Output of AVERAGE, HIGHEST and LOWEST (outside loop);</p>\n<p>Example answer 1</p>\n<pre>HIGHEST = −1    //any value lesser than or equal to 0<br/>LOWEST = 10000 //any value greater than or equal to 1000<br/>COUNTER = 0<br/>TOTAL = 0<br/>loop while COUNTER &lt; 1000 // allow COUNTER &lt;= 999<br/>    input NUMBER<br/>    if NUMBER&lt;0 or NUMBER&gt;1000 or NUMBER div 1 ≠ NUMBER/1<br/>                  //accept NUMBER&lt;1 or NUMBER&gt;999 or NUMBER mod 1 ≠ 0<br/>               output \" Your number is invalid, please try again\"<br/>    else<br/>          if NUMBER &gt; HIGHEST then<br/>            HIGHEST = NUMBER<br/>          end if<br/>          if NUMBER &lt; LOWEST then<br/>            LOWEST = NUMBER<br/>          end if<br/>          TOTAL = TOTAL + NUMBER<br/>          COUNTER = COUNTER + 1<br/>     endif<br/>end loop<br/>AVERAGE = TOTAL / COUNTER // allow TOTAL / 1000<br/>output HIGHEST<br/>output LOWEST<br/>output AVERAGE</pre>\n<p>Example answer 2: (the lowest and the highest are set to the value of the first inputted number)</p>\n<pre>input NUMBER<br/>loop while NUMBER&lt;0 or NUMBER&gt;1000 or NUMBER div 1 ≠ NUMBER/1<br/>    output \" Your number is invalid, please try again\"<br/>    input NUMBER<br/>end loop<br/>HIGHEST = NUMBER<br/>LOWEST = NUMBER<br/>TOTAL = 0<br/>loop COUNTER FROM 0 to 998<br/>           input NUMBER<br/>           loop while NUMBER&lt;0 or NUMBER&gt;1000 or NUMBER div 1 ≠ NUMBER/1<br/>               output \" Your number is invalid, please try again\"<br/>               input NUMBER<br/>           end loop<br/>           if NUMBER &gt; HIGHEST then<br/>             HIGHEST = NUMBER<br/>           end if<br/>           if NUMBER &lt; LOWEST then<br/>             LOWEST = NUMBER<br/>           end if<br/>           TOTAL = TOTAL + NUMBER<br/><br/>end loop<br/>AVERAGE = TOTAL / 1000<br/>output HIGHEST, LOWEST, AVERAGE</pre>\n<p>Example answer 3</p>\n<pre>HIGHEST = -1     //any value lesser than or equal to 0<br/>LOWEST = 10000  //any value greater than or equal to 1000<br/>C = 0<br/>TOTAL = 0<br/>loop while C&lt;1000<br/>    input NUMBER<br/>    if NUMBER&gt;=0 and NUMBER&lt;=1000<br/>        if NUMBER mod 1 == 0<br/>             if NUMBER &gt; HIGHEST then<br/>                  HIGHEST = NUMBER<br/>             else<br/>                if NUMBER &lt; LOWEST then<br/>                      LOWEST = NUMBER<br/>                endif<br/>            end if<br/>            TOTAL = TOTAL + NUMBER<br/>            C=C+1<br/>        endif<br/>    else<br/>       output(“invalid input, enter another number”)<br/>   endif<br/>end loop<br/>output HIGHEST, LOWEST, TOTAL/1000</pre>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The term logic error was not always familiar to candidates. Many candidates correctly identified the logic error in Part(a)(i).</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number gave less precise or incorrect answers to Part(a)(ii).</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of candidates did not score any marks for question in Part (b) because they were not able to state the name of the method that could be used to restrict the values that are input.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In Part (c) candidates were required to produce an algorithm based on the flowchart given in the question. Most candidates achieved some marks for this algorithm, but full marks were rare.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "21M.1.HL.TZ0.14",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Fenna has decided to use roulette wheel selection and cycle crossover (CX) for her genetic algorithm. She has two other important decisions to make:</p>\n<ul>\n<li>What values to assign to the variables when they are first initialized. These variables<br/>include population size, initial population routes, and mutation rate.</li>\n<li>What stopping criteria to use for the genetic algorithm.</li>\n</ul>\n<p>Discuss the impact that these decisions may have on the success of the genetic algorithm.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[12 max]</strong></em>.</p>\n<p><strong>Roulette wheel selection</strong></p>\n<ul>\n<li>Effective with large-sized problems.</li>\n<li>Low fitness parents of the population have a chance for be selected.</li>\n<li>This is important in hill climbing/avoiding local minima.</li>\n<li>When a population contains only individuals with scores of large, nearly equal values, the selection probability of all individuals becomes nearly identical.</li>\n<li>Other types of selection may be more suitable for smaller search spaces.</li>\n<li>Prevents overly quick convergence.</li>\n<li>Populations must be sorted on every cycle.</li>\n</ul>\n<p><strong>Cyclic crossover (CX)</strong></p>\n<ul>\n<li>Allows for two parents chromosomes/cities to be combined/preserved.</li>\n<li>And not be duplicated during crossover.</li>\n<li>Ensures diversity of chromosomes/cities from parents.</li>\n<li>Accelerates the search process in genetic algorithms.</li>\n</ul>\n<p><strong>Population size</strong></p>\n<ul>\n<li>It is difficult to decide upon a population size.</li>\n<li>Too large a population will increase the likelihood of a near-optimal solutions but will increase the length of time to run through a generation.</li>\n<li>A large enough population may have enough diversity to arrive at a near-optimal solution without any mutation.</li>\n<li>Too small a population may mean that you may never arrive at a near-optimal solutions;</li>\n<li>Because there is not enough variation in the parents.</li>\n<li>This may require you to increase the mutation rate, which will add greater variation.</li>\n<li>But may also not arrive at a near-optimal solution.</li>\n</ul>\n<p><strong>Initial routes</strong></p>\n<ul>\n<li>A random generation of cities would not be an optimal initial population.</li>\n<li>Some form of optimization of the initial population could be done.</li>\n<li>A scale map of the cities could be created and people attempt an optimal route by drawing the path between.</li>\n<li>These human attempts at the optimal solution could be used for the initial population.</li>\n<li>Along with some random routes to ensure variation.</li>\n</ul>\n<p><strong>Mutation rate</strong></p>\n<ul>\n<li>Typical mutation rate is around 1%.</li>\n<li>A higher value will result in a more random population.</li>\n<li>Too high and the population will not evolve towards a near-optimal solution.</li>\n<li>Too low and there may not be enough diversity in the population to find a near-optimal solution.</li>\n<li>Mutation allows you to escape from local minima optimisation.</li>\n</ul>\n<p><strong>Stopping criterion</strong></p>\n<ul>\n<li>A set number of iterations (generations). This could be based on run for 100 iterations or run for a set period of time. This solution would rely on chance for estimating the number of iterations required to ensure convergence.</li>\n<li>A set number of iterations (generations) when the best route does not change. For example, if after 100 generations the value doesn’t change for another 30 generations (i.e., 30%) then the algorithm stops. This would increase the likelihood of convergence.</li>\n<li>Set a target distance for the route and run the algorithm until that distance is reached.</li>\n</ul>\n<p><strong>Conclusion</strong></p>\n<ul>\n<li>A final measured conclusion is included in which the candidate links together the various points in evaluating the probability of success.</li>\n</ul>\n<p><em>Please see markband</em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>The names of vegetables must be always held in <strong>alphabetical</strong> order in a list in the main memory.</p>\n<p>The application program should allow insertion and deletion of the names of vegetables from this list.</p>\n</div><div class=\"specification\">\n<p>Given the following vegetables: potato, asparagus, lettuce, radish.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare the use of a dynamic linked list for holding these names of vegetables with a static one-dimensional array.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Sketch a single linked list holding these vegetables.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>List the steps required to insert cabbage into the linked list.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why deleting the first node in this list is different to deleting other nodes.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the dynamic data structure suitable for maintaining this list of vegetables which will allow faster searching for a given vegetable name.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The vegetable names are input in the following order</p>\n<p style=\"text-align:center;\">potato, asparagus, lettuce, radish.</p>\n<p>Sketch the data structure suggested in part (d) containing the vegetable names sorted in <strong>alphabetical</strong> order.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each comparison, up to <strong>[3 max]</strong></em>.</p>\n<p>The size of the dynamic list does not have to be predetermined as in an array;<br/>The size of the dynamic list is not fixed whilst the size of an array is always fixed;<br/>If names are sorted they can be added/deleted (more easily) by changing the pointers without having to shuffle the names;<br/>As records can be dynamically added/deleted the memory is better used because there are no wasted / missing spaces as in an array;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for a node containing two fields / data(name) and link (pointer) to the next node and <strong>[1]</strong> for showing an external pointer pointing to the first vegetable on the list, and null pointer in the last node, and pointers which link the nodes in alphabetical order up to <strong>[2 max]</strong></em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each step identified up to <strong>[4 max]</strong></em>.</p>\n<p>Create a new node containing cabbage;<br/>Traverse the list (from the beginning) to find the place to insert a new node;<br/>Cabbage should be inserted before lettuce and after asparagus;<br/>The pointer in new (cabbage) node should be set to point to the node that is before the insertion point / lettuce;<br/>The pointer in node before insertion point / asparagus / should now point to the new node / cabbage;</p>\n<p><em><strong>Note</strong>: award marks for clearly labelled diagrams in candidates’ answers</em>.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying a reason why deleting the first node is different to deleting other nodes and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong></em>.</p>\n<p>External pointer (First) must be changed/only in situation when deleting the beginning node the external pointer must be changed;<br/>And set to the pointer in the link field of the first node (Asparagus) which points to the second node/Lettuce;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>Binary tree;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for clearly a binary tree and the root is Potato.</em><br/><em>Award <strong>[1]</strong> for the correct left subtree.</em><br/><em>Award <strong>[1]</strong> for the correct right subtree.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18N.1.HL.TZ0.11",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The following method, <code>swap(A,B)</code> is written to exchange the values of variables A and B.</p>\n<pre>swap(A,B)<br/>  TEMP = A<br/>  B = TEMP<br/>  A = B<br/>end swap</pre>\n</div><div class=\"specification\">\n<p>Method <code>swapRows(MAT,K,L)</code> swaps the elements of two rows (row <code>K</code> and row <code>L</code>) in the two‑dimensional array <code>MAT</code>.</p>\n<p>For example,</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>A game consists of four rounds. In each round a player can score up to 100 points.</p>\n<p>The data about the game is sorted in alphabetical order of names and stored in the memory as follows</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Where<br/><code>PLAYERS</code> is a one-dimensional array holding names of players (currently sorted in alphabetical order).<br/><code>ROUNDS</code> is a two-dimensional array holding players’ scores.<br/><code>TOTALS</code> is a one-dimensional array holding total scores.</p>\n<p>For example,<br/><code>PLAYER[1]</code> is Boris. The total number of points he scored is 180 and this can be found in <code>TOTALS[1]</code>.<br/>Boris scored 40 points in the first round which can be found in <code>ROUNDS[1][0]</code>.<br/>The value stored in <code>ROUNDS[1][2]</code> is 50 which means that Boris scored 50 points in the third round.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State Paul’s total score. </p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State where Hugh’s score in the fourth round can be found. </p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>All the data stored in memory should be sorted in ascending order of total score using selection sort.</p>\n<p>For example, after sorting, the data given opposite will be held in memory as follows</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Construct an algorithm that will sort all the data in ascending order.</p>\n<p>In your solution you should call methods <code>swap()</code> and <code>swapRows()</code> given in part (a) and part (b).</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>105;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p><code>ROUNDS[2][3]</code>;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for the correct outer loop (loops through the array <code>TOTALS</code>)</em>.<br/><em>Award <strong>[2 max]</strong> for correct determination of <code>MINIM</code> / the position of smallest element in part of array <code>TOTALS</code> (range should be from J + 1 to 5), <strong>[1]</strong> for minor error</em>.<br/><em>Award <strong>[1]</strong> for correct condition / to check whether swaps are needed or not</em>.<br/><em>Award <strong>[1]</strong> for correct swap of elements in array <code>TOTALS</code></em>.<br/><em>Award <strong>[1]</strong> for correct swap of elements in array <code>PLAYERS</code></em>.<br/><em>Award <strong>[1]</strong> for correct swap of rows in two-dimensional array <code>ROUNDS</code></em>.</p>\n<p><em>Example answer</em>:</p>\n<pre>loop for J from 0 to 5<br/>  MINIM = J<br/>  loop for I from J + 1 to 5<br/>    if TOTALS[I] &lt; TOTALS[MINIM]:<br/>      MINIM = I<br/>    end if<br/>  end loop<br/>  if MINIM != J<br/>    swap(TOTALS[MINIM],TOTALS[J])<br/>    swap(PLAYERS[MINIM], PLAYERS [J])<br/>    swapRows(ROUNDS, MINIM, J)<br/>  end if<br/>end loop</pre>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.1.HL.TZ0.12",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A business has a range of different computers within the organization, including laptops, desktops and file servers. Wherever possible the organization uses a common operating system on its computers.</p>\n</div><div class=\"specification\">\n<p>Memory requirements and processor speed will vary depending on the tasks required of the computer.</p>\n</div><div class=\"specification\">\n<p>The business has decided to implement a computer-based system to switch the room lights on and off automatically. The lights will only be switched on if the level of light is below a specific reading and there are people in the room. The lights will be switched off when the room has been unoccupied for at least five minutes.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> resource management techniques that are likely to be carried out by the operating system of a desktop computer.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> way the operating system hides the complexity of the hardware from the computer user.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Contrast the memory requirements of a laptop computer and a file server.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Contrast the processor speed requirements of a laptop computer and a file server.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>two</strong> types of sensor that are required to control the lighting to ensure it switches on when it is required.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the system makes use of the data it receives from the sensors to determine when to switch the lights on.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the system will prevent the lights from being switched off too quickly when it thinks the room is unoccupied.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em> </p>\n<p>Virtual memory;<br/>to enable the hard drive to act as primary memory if required;</p>\n<p>Paging;<br/>scheme by which a computer stores and retrieves data from secondary storage for use in main memory;</p>\n<p>Multitasking;<br/>to enable more than one application to run at the same time;</p>\n<p>Scheduling (method by which work is assigned to resources that complete the work);<br/>to determine which process will own CPU for execution while another process is on hold.;</p>\n<p>Policies (ways to choose which activities to perform);<br/>according to which decisions are made about which operations should be authorized/ which resources should be allocated;</p>\n<p>Interrupt handling;<br/>so that urgent tasks required by parts of the system may be executed;</p>\n<p>Polling;<br/>to sampling the status of an external device;</p>\n<p><em>Mark as 2 and 2</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em> <br/><em>Award 1 mark for the virtual machine (presented by the OS to the user, hiding all the complexities of the hardware behind layers of OS software) and 1 mark for an expansion</em>.</p>\n<p>The virtualization of real devices;<br/>such as the use of drive letters for partitions to represent a virtual hard drive;</p>\n<p>Use of icons;<br/>to represent peripherals/to access different drives; </p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em> <br/>The primary memory/secondary memory in a laptop will be less / smaller than would be found on a file server;<br/>because it is a smaller device that is used by an individual, so doesn’t need to store / run so many programmes simultaneously;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em> <br/>The processor of a file server would be faster/have more cores than that of a laptop;<br/>as it has many more resources to manage/as it needs to keep track of many workstations/printers/peripherals;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em> <br/>Light sensor;<br/>infra-red/motion sensor;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em> <br/>The sensors send data to the processor continuously;<br/>The data received from the sensors is converted to digital (using an analogue to digital convertor);<br/>The readings are compared to pre-set values in the controller/system;<br/>If the light level is found to be below the pre-set value <span style=\"text-decoration:underline;\"><strong>and</strong></span> a presence is detected (in the room);<br/>A signal is sent to the actuator to turn on the lights;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em> <br/>A timer (set to 5 minutes);<br/>If the light level is found to be below the pre-set value a timer is activated but the lights remain on;<br/>When the timer reaches 5 minutes, the lights switch off (if no further presence is detected);</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was answered well by many students. Part (a) was reasonably well answered with many gaining at least two of the marking points.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Part (b) was well answered.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Students generally have problems with compare/contrast questions, describing the requirements individually without contrasting, as was the case in Part(c).</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Students generally have problems with compare/contrast questions, describing the requirements individually without contrasting, as was the case in Part(c).</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of candidates scored full or nearly full marks for the questions (d)</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of candidates scored full or nearly full marks for the questions (e)</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A large number of candidates scored full or nearly full marks for the questions (f)</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "21M.1.HL.TZ0.15",
    "topics": [
      "topic-6-resource-management",
      "topic-7-control"
    ],
    "subtopics": [
      "6-1-resource-management",
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline <strong>one</strong> usability issue of a cell phone (mobile phone).</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Screen size/ larger screen;<br/>Easier to view large amounts of data without excessive scrolling / reduced eyestrain / more accessible to those with weak eyesight;</p>\n<p>Screen size/smaller screen;<br/>Difficult to accurately select the correct character on the virtual keyboard;</p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> fundamental operation of a computer.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between fundamental and compound operations of a computer.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Retrieve;<br/>Compare;<br/>Store;<br/>Add;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Fundamental operation requires one machine instruction cycle/do not require the processor to go through many machine instruction cycles to reach a result;<br/>A compound operation is complex / it is an operation that involves a number of other operations to reach the result;<br/>For example, find the largest number in an array/ sort the array in ascending order etc.;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were generally able to identify a fundamental operation of a computer, such as retrieve, compare, store or add.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were generally able to describe correctly the meaning of complex operations of a computer but were less clear in their descriptions of fundamental operations, so as to distinguish the two terms.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.1",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>State <strong>two</strong> differences between primary storage and secondary storage.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Primary storage is accessed by a computer's central processing unit (CPU) and secondary storage is not accessed directly by the CPU;<br/>Primary storage has lower access time / has smaller capacity/ more expensive type of memory than secondary storage (which is slower than primary storage, with larger capacity but it is cheaper);<br/>Primary storage is volatile (uses random-access memory (RAM), cache memory, or some other specialized hardware to store data while the computer is powered on/ volatile devices), whilst secondary storage on a computer is provided by non-volatile devices (such as SSD or HDD);<br/>Primary storage holds data temporarily whilst data is kept permanently/ for a long time in secondary storage;<br/>Primary storage holds currently running programs/data/operating system, secondary storage does not.</p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>A new computerized system is being planned for a school library.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>one</strong> method by which systems requirements can be obtained from the stakeholders.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason for providing a prototype for this new system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2]</strong> for one method that is suitable for the given scenario</em>.</p>\n<p>Answers may include:<br/><span style=\"text-decoration:underline;\">Interviews</span> could be held (with the <span style=\"text-decoration:underline;\">librarian/stakeholders</span>);<br/>To establish the functions required by the system;</p>\n<p><span style=\"text-decoration:underline;\">Direct observation</span> could be made of the users/<span style=\"text-decoration:underline;\">students</span> using the present system;<br/>To gain an insight on how the library is being used;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Allows stakeholders to gain an idea of how the system would be/work/look;<br/>so they can provide feedback / suggest improvements;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18M.1.SL.TZ0.1",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A parasite is destroying olive trees in a region in southern Europe. This area is monitored by taking aerial digital images at midday each day. A neural network is then used in the analysis of these images.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Genetic algorithms follow an iterative process in developing a solution to a problem.</p>\n<p>Outline the steps involved in this iterative process.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why neural networks need to be trained before they can be used in this analysis.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why supervised learning may be preferred to unsupervised learning for training these networks to make predictions on the progress of this parasitic disease.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Many companies now use an automated response system when answering basic telephone queries from customers. This system uses speech analysis together with recorded messages.</p>\n<p>Explain why the content of the messages given by these systems must be carefully chosen.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The initial population set is chosen randomly/pseudo randomly;<br/>A fitness function is applied to each population;<br/>The fittest members are selected for the next stage;<br/>Genetic operators are applied;<br/>Such as crossover / mutation;<br/>The process is repeated until;<br/>An acceptable level of fitness is found / a plateau is reached / a maximum number of iterations has been reached;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Network networks initially have a randomly chosen set of weights;<br/>Which will not solve any problem;<br/>These weights must be altered;<br/>Until they fulfil their objective;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong></em>.<br/><em>Award <strong>[3 max]</strong> for considerations linked to each of the two forms of learning</em>.<br/>With supervized learning the processing of the image is guided (supervised) by the output dataset to the purpose of classifying certain features of the image;<br/>For example, to the value of the pixel (grey or colour) one can assign likelihood of belonging to neighbouring values of greys/colours;<br/>And this can be defined in advance as a way to represent a potential value for an infected tree;</p>\n<p>However, with unsupervised learning the processing of the image is not guided in any way / the output is not known;<br/>And the purpose is to discover and build clusters that have common characteristics;<br/>Therefore, this may clearly help in building up the main regions, but not specifically on focussing on the infected trees;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/>Questions/requests must be short so repetition is not required / so they are immediately understood;<br/>And chosen so that there is a restricted response set;<br/>That can be recognised by the system / that can be stored by the system;<br/>The requests must lead to a satisfactory end;<br/>Or customers will get frustrated / hang up / the company will lose business etc.;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18N.2.HL.TZ0.8",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline why computers use the binary number system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>because the binary system uses only two digits (0 and 1);<br/>and computer uses bi-stable devices / devices that can be either in one of the two possible states(such as pulse or no pulse, on or off, 0 or 1, true or false) /;</p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> cause of data loss.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>one</strong> way offsite storage can be used to prevent data loss.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Any malicious activity;<br/>Natural disasters;<br/>Human error;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Internet backup service could automatically back up (all important) data files;<br/>to a remote server that could be accessed/controlled over the internet;</p>\n<p>Copies of all important data files (backup) could be placed on two separate hardware devices;<br/>which are placed in two different physical locations;</p>\n<p>A remote file server could be set up;<br/>for uploading all important data files;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates identified a relevant cause of data loss, such as malicious activity, natural disasters or human error.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were generally able to identify appropriate methods of offsite data storage, but clear descriptions of how these could be used to prevent data loss were less common, with answers simply describing the method rather than how it could prevent data loss.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.2",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain why compression of data is beneficial when transmitting data files across a network.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em></p>\n<p>Compressing a data file reduces the file size;<br/>The amount of time it takes to send a file over the network depends on the size of the transmitted file, so the amount of time needed to transmit the file is reduced /transmission of a compressed file takes less time than transmission of that same uncompressed file;<br/>Also, compression of data files before transmission reduces the financial cost of running a network/ less equipment / bandwidth is needed to transmit the data file;</p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline the role of the memory data register in the machine execution cycle.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Holds (a copy of) the contents of the memory;<br/>Which are to be transferred from/to memory to/from other CPU components;<br/>Allowing the processor and memory to act independently/processor not affected by differences in the speed of operation;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.2",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A group of programmers are involved in creating a new software product. They create many new sub-programs but also use existing sub-programs within the product.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why a sub-program is considered an example of abstraction.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of designing and developing different parts of software products concurrently.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> way in which users can be informed of software updates.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em></p>\n<p>A sub-program is a named section of code that performs a specific task (in a program) / can be called by name / referred by the identifier when needed;<br/>without knowing the details (of code and data structures) as these are wrapped / hidden within the sub-program;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em></p>\n<p>different stages (of programming) (<em>Accept examples!</em>) run simultaneously (rather than consecutively);<br/>this decreases product development time / decreases the time to market;<br/>leading to improved productivity/reduces costs;<br/>however, it requires more resources/more software developers;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em></p>\n<p>A message can be sent to the user (When the software is installed and registered, the user provides an email address / phone number);<br/>With a link to the update;</p>\n<p>notifications/alerts are sent to the computer (a cookie is placed on the user's computer which communicates with the software developer);<br/>about automatic updates;</p>\n<p>(when the program is run) it queries a URL the program developer has built in to check whether the current version matches the latest version;<br/>if not, notifications/alerts are sent;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.5",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the function of the control unit (CU) in the central processing unit (CPU).</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the purpose of cache memory.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>It decodes the instructions and controls all the other internal components of the CPU to make it work;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Cache memory is a memory that a computer microprocessor can access more quickly than it can access regular RAM;<br/>It is integrated directly with the CPU chip or placed on a separate chip which has a separate bus interconnect with the CPU;<br/>and stores frequently used data only until a computer is powered down;<br/>Thus, when a processor requests data that already has an instance in the cache memory; it does not need to go to the main memory or the hard disk to fetch the data;</p>\n<p>Cache memory is a small-sized type of volatile computer memory;<br/>that provides high-speed data access to a processor;<br/>and stores frequently used computer programs, applications and data;<br/>Cache memory can be primary or secondary cache memory, where primary cache memory is directly integrated to the processor;<br/>And secondary cache memory is a reserved portion on a disk stores and provide access to frequently accessed data/applications from the disk.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally provided appropriate statements to illustrate their understanding of the function of the control unit in the CPU.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was generally well answered with many full or nearly full marks answers seen. Candidates demonstrated a good understanding of the purpose of cache memory.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.3",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how data is sorted by the selection sort.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> difference between a bubble sort algorithm and a selection sort algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/>The selection sort algorithm maintains two subarrays of a given array, the subarray which holds already sorted elements;<br/>And the subarray which is unsorted;<br/>In every iteration, the minimum element (considering ascending order OR maximum element considering descending order) from the unsorted subarray is found/selected;<br/>And then swapped with the item in the next position (at the appropriate place / in the last position) to be filled in the sorted subarray;</p>\n<p><em><strong>OR</strong></em></p>\n<p>The selection sort algorithm searches through the entire array for the smallest (considering ascending order OR the largest element considering descending order) element;<br/>When found, it (the smallest/largest element) is swapped with the first element of the array;<br/>Then searches for the smallest / largest element in the <strong>remaining</strong> array (an array without the first element) and swap it with the second element;<br/>This process <strong>repeats</strong> on the remaining items (searches for the smallest / largest element in the remaining array (an array without first and second elements)) and swap it with the third element, and so on);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p>Bubble sort swaps adjacent items;<br/>selection sort finds the next smallest (each time it goes through the list);</p>\n<p>Bubble sort can exit early/ is faster if already the list is sorted;<br/>selection sort will need to complete the procedure for the entire list every time;</p>\n<p>(The efficiency of Bubble and selection sort is different when applied on <strong>already sorted</strong> list) in the best-case bubble sort takes an order of N time;<br/>whereas selection sort consumes an order of N<sup>2</sup> time (where N is the number of items on the list));</p>\n<p><em><strong>Note</strong>: To award marks for such an answer it should be evident that the list is already sorted OR the term 'the best-case' should appear because the worst-case /average-case complexity/efficiency is same in both algorithms (O(N<sup>2</sup>))</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.6",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\">\n<p>Calculate how many different colours can be represented using two hexadecimal characters.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>2 hex = 4 + 4 = 8 bits;<br/>hence 2^8/16^2/256 colours;<br/><em><strong>Note</strong>: Allow <strong>[2]</strong> for 16^2 = 256 colours with no workings and also ONLY 256</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.3",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Tapetum lucidum</em> refers to the layer of tissue in the eyes of many animals, such as cats and owls, which helps to improve their night vision by reflecting light. The eyes of these animals glow in the dark.</p>\n<p>A student wishes to identify a particular animal which belongs to the above group.</p>\n</div><div class=\"specification\">\n<p>The web and social media are increasingly populated by pictures of pets and other animals. Most of these pictures are annotated with tags, such as those used on social networks.</p>\n</div><div class=\"specification\">\n<p>Search engines use indexing for the storing of keywords that might be used in a search engine query.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With the use of an example, explain how a multimedia search in the semantic web would be preferable to a text based search for this student.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> differences between an ontology and a folksonomy.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss whether it is possible to integrate the expressivity of folksonomies with the authoritativeness of ontologies.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the role that web crawlers perform in developing this index.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Open source software, such as Linux, is often developed by using the collective intelligence of the developer community.</p>\n<p>Explain how collective intelligence can lead to the successful development of open source software.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[2 max]</strong> for a general knowledge and understanding of multimedia web.</em><br/><em>Award <strong>[2 max]</strong> for describing a situation where the multimedia web may be beneficial to using a text based search</em>.</p>\n<p><strong>Knowledge of multimedia web</strong><br/>The multimedia web makes an expanded use of the semantic web (formally and semantically interlinked data of any kind) making possible to provide input in a form other than text, and possibly return output in various forms;<br/>Therefore, the search is made at the level of “concepts” and not just textual terms;<br/>In a way that the computer can retrieve semantical links among different concepts/concepts presented in different formats;<br/>That includes the ability for crawlers to go through scientific classifications of text/captions/description of images;<br/>That can all be indexed and clustered into a common “concept”;</p>\n<p><em><strong>Example 1</strong></em>:<br/><em>To detect/discover/verify/monitor the presence of species based on sound</em><br/><em>For example</em>:<br/>We might aim in recognising and searching on the web starting from the sound of some nocturnal animal that we don’t see/we don’t know, but whose presence is revealed by their eyes;<br/>Giving the sound as input would allow to perform a search based on registration/audio that would run on multimedia sources linked with semantical links;</p>\n<p><em><strong>Example 2</strong></em>:<br/><em>To detect/discover/verify/monitor the presence of species based on images</em><br/><em>For example</em>:<br/>We might start a multimedia search based on images/photos of nocturnal animals we may have taken;<br/>To see whether those sightings were associated to dangerous species or not;<br/>To signal the presence of some unusual species in the region;<br/>To signal the presence of some animal in difficulty to the protection agency;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each difference up to <strong>[3 max]</strong></em>.<br/>Designed or extracted by knowledge engineers VS;<br/>Created collaboratively by users;<br/>Laborious and expert VS quick/easy non-expert;<br/>Controlled vocabulary/dictionary VS no control of vocabulary/informal;<br/>Formal specification of knowledge domain (taxonomies) VS;<br/>Informal metadata on documents;<br/>Not just for the web VS mostly for the web;<br/>Essential for the semantic web VS important for social web;<br/>Explicit meaning and high expressive power VS;<br/>Ambiguity and low expressive power;<br/>Experts’ view of the domain VS social aspect of meaning;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong></em>.<br/><em>Award <strong>[2 max]</strong> for a superficial response that uses generic terminology.</em><br/><em>Award <strong>[4 max]</strong> for a response that shows some analytical comments, unsubstantiated conclusions and some use of appropriate terminology.</em><br/><em>Award <strong>[6 max]</strong> for a coherent analysis leading to substantiated conclusions with the appropriate use of subject specific terminology.</em><br/><strong>General, possibly described through examples</strong><br/>The ontology of the semantics web is extracted with expert means from documents;<br/>(For example, by making features analysis / aggregating concepts / eliminating disambiguation;)</p>\n<p>Whereas, the folksonomy is the result of adding different vocabulary (done by the users) via tags in posts/web pages/social web (like <em>Tumblr</em>);<br/>(For example, tagging the photo of a cat under an informal “cat’s eye” would add a different perspective to the ontology);</p>\n<p><strong>The problem</strong>:<br/>If not monitored, tagging may be source of “garbage” and hinder the research for the ontology;<br/>The ambiguity problem of tagging needs to be addressed so to extract a weight to ambiguous use of tags;</p>\n<p><strong>Possible countermeasures</strong>:<br/><span style=\"text-decoration:underline;\">Expand the query within social tagging platforms</span>;<br/>In order to extract a weight to ambiguous use of tags and use this weight to cut-out some irrelevant retrieved information;</p>\n<p><strong>OR</strong><br/>Use the <span style=\"text-decoration:underline;\">folksonomy tags</span> (at least some) <span style=\"text-decoration:underline;\">to update</span> (possibly as metadata) <span style=\"text-decoration:underline;\">the ontology vocabulary</span>;<br/>However, depending on the context, this may not always be feasible/applicable and depends on the domain of discourse;</p>\n<p><strong>OR</strong><br/><span style=\"text-decoration:underline;\">Re-engineer the ontology to include</span> a collaborative form of construction from tagging;<br/>However, this is method is not applicable in expert fields where quality of content must be preserved;</p>\n<p><strong>OR</strong><br/>Keep <span style=\"text-decoration:underline;\">the ontology and folksonomy well separated</span>, but let them work in tandem;<br/>In a way to have feedback from folksonomies, but merged in a controlled way;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each comment that indicates the role that web crawlers perform in developing an index that is used by a search engine up to <strong>[4 max]</strong></em>.<br/>A web crawler is given a “seed” page which is where it starts;<br/>It searches for hyperlinks;<br/>Which it recursively follows;<br/>Making a copy of each web page visited;<br/>Which can be indexed by the search engine;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> some example to fix the scenario of open source;</em><br/><em>Award <strong>[1]</strong> what is meant by collective intelligence;</em><br/><em>Award <strong>[1]</strong> some explanation/expansion to link the concepts;</em></p>\n<p><strong>The focus</strong>:<br/>“Software for Public Interest”;</p>\n<p>Open source and free software development is the result of initiatives taken by <span style=\"text-decoration:underline;\">sparse communities of programmers</span> that <span style=\"text-decoration:underline;\">collaborate</span> effectively to maintain complex programs/packages of code;</p>\n<p>(For example, Debian Foundation, Linux Foundation, Mozilla Foundation, Apache Foundation, Free Software Foundation, Wikipedia…;)</p>\n<p><strong>The structure</strong>:<br/><span style=\"text-decoration:underline;\">A distributed system of development</span> AND <span style=\"text-decoration:underline;\">tightly governed</span> /<br/>Each developer codes individually, but the whole project relies on the fact that portions of code may be integrated as components of other parts;</p>\n<p>Organisations may have different modes of governance, and have precise ways (processes) to contribute to a project, both technical and social;</p>\n<p>The objective is to guarantee coordination, quality and relevance for the project development (technical) based on contributions of the individuals;<br/>While modulating possible clashes/conflicts between different personalities;</p>\n<p><strong>Examples</strong>:<br/>OS kernels, Wikipedia, mail programs, GNU, …;<br/>Build a deposit of old software (free/open source), for preservation and sharing (with everybody);<br/>Old open source SW may in fact be at risk of not being publicly available for a variety of reasons, such as sites become proprietary and limit public access, crash of the systems, business decisions;</p>\n<p><strong>Role of collective intelligence</strong><br/>Individuals are expert developers, but act collectively, for public interest, and this becomes a movement that may make a cultural/political difference/impact (the key is high quality, not populism!);<br/>e.g. the French Public Administration adopts Open Source software and has rejected proprietary products (Microsoft, specifically)</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18N.2.HL.TZ0.12",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-6-the-intelligent-web",
      "c-5-analysing-the-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain the importance of the memory management function of an operating system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3]</strong> as follows</em>:<br/>The function of the memory management <strong>[1]</strong>.<br/>An example if its use <strong>[1]</strong>.<br/>Reason for the importance <strong>[1]</strong>.</p>\n<p><em><span style=\"text-decoration:underline;\">Example</span></em>:<br/><em>Allocates and de-allocates memory/ assigns blocks of memory to programs;</em><br/><em>Ensures a program has sufficient memory to run/manages virtual memory if needed;</em><br/><em>To avoid overwriting /clashing of programs/optimise system performance/maximise memory usage</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.4",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A network is set up with shared printers so that when a user wishes to print, print jobs are sent to a queue until the printer is available.</p>\n</div><div class=\"specification\">\n<p>The <em>factorial</em> of the positive integer <em>n</em>, which is written <em>n</em>!, is the product of all the positive integers less than or equal to <em>n</em>. A stack may be used to perform a factorial calculation as shown by the algorithm:</p>\n<pre>//stack for factorial(NUM)<br/>//creates a stack of (NUM - 1) elements<br/>//when NUM = 6<br/>NUM = 6<br/>loop while NUM &gt; 1<br/>    stack.push(NUM)<br/>    NUM = NUM – 1<br/>end loop<br/>PRODUCT = 1<br/>loop while not stack.isEmpty()<br/>    NUM = stack.pop()<br/>    PRODUCT = PRODUCT * NUM<br/>end loop<br/>output PRODUCT</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why a queue is the appropriate data structure to manage print jobs.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Draw a diagram to show how a print queue may be implemented using a linked list.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why a stack would not be appropriate as a data structure for managing print jobs.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the trace table for the algorithm shown for <code>NUM = 6</code>.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how a stack may be used in the implementation of a recursive function.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em> <br/>Print jobs are to be completed in the order received;<br/>Queue is appropriate because print jobs are added/enqueued at the back/rear and dequeued/removed from the front / because queue is a FIFO data structure;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em> </p>\n<p>Correct use of pointers and null pointer in the last node;<br/>External pointer to the beginning of the list (head);<br/>External pointer to the end of the list (tail);<br/>Each node consists of at least 2 parts/fields: data (print jobs) and pointer;</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em> <br/>Print jobs would not be managed in an orderly manner;<br/>Because stack is LIFO data structure / elements are added/pushed and removed/popped at only one end;<br/>And each time another one is added it takes precedence over those that are already in the data structure; </p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em></p>\n<p>NUM column correct;<br/>PRODUCT column correct;<br/>OUTPUT column correct;</p>\n<p><em><strong>Note</strong>: The trace table may be differently presented</em>. </p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p>A recursive function calls itself during its execution;<br/>First call (all local variables, data, return addresses, etc.) is pushed onto stack;<br/>Second/subsequent recursive calls are pushed on to the stack (added above previous call(s));<br/>When the terminating condition is met/ execution of recursive function ends;<br/>The function calls pop from the stack;<br/>In the reverse order to which they were pushed/LIFO;</p>\n<p> </p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21M.1.HL.TZ0.16",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\">\n<p>Assume X = 5 and Y = 3.</p>\n<p>Determine the value of the following expression:</p>\n<p style=\"text-align:center;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mo>(</mo><mi mathvariant=\"normal\">X</mi><mo> </mo><mo>&lt;</mo><mo>=</mo><mo> </mo><mn>5</mn><mo>)</mo><mo> </mo><mi>XOR</mi><mo> </mo><mo>(</mo><mi mathvariant=\"normal\">Y</mi><mo> </mo><mo>&gt;</mo><mo> </mo><mi mathvariant=\"normal\">X</mi><mo>)</mo></math></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max] <br/></strong></em><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mo>(</mo><mn>5</mn><mo>&lt;</mo><mo>=</mo><mn>5</mn><mo>)</mo><mo> </mo><mi mathvariant=\"bold\">XOR</mi><mo> </mo><mo>(</mo><mn>3</mn><mo>&gt;</mo><mn>5</mn><mo>)</mo><mo> </mo><mo>(</mo><mo>=</mo><mo> </mo><mi>TRUE</mi><mo> </mo><mi mathvariant=\"bold\">XOR</mi><mo> </mo><mi>FALSE</mi><mo> </mo><mo>)</mo><mo> </mo><mo>=</mo><mo> </mo><mi>TRUE</mi><mo>;</mo></math> </p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.7",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> essential features of a computer language. </p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Fixed vocabulary;<br/>Unambiguous meaning;<br/>Consistent grammar;<br/>Consistent syntax;<br/>Provide a way to define basic data types and operations on those types (ability to write functions/procedures);<br/>Provide ability of Input and output handling;<br/>Provide some kind of loop that can be stopped / conditional statement / branching (conditional and unconditional branching);<br/>It should have variables that reference computer memory, syntax for basic arithmetic and logical;<br/>Operations on those memory locations;<br/>It has to run on/be able to be processed by a computer (i.e. it must have a compiler/interpreter)</p>\n<p><em><strong>Note</strong></em>: do not accept aspects that address interoperability/portability/standards/user friendliness</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.1",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a logic diagram for the following Boolean expression.</p>\n<p style=\"padding-left:270px;\"><math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mstyle mathsize=\"12px\"><mi>NOT</mi></mstyle></math> <math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi mathvariant=\"normal\">A</mi></math> <math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mstyle mathsize=\"12px\"><mi>OR</mi><mo> </mo><mo> </mo><mo> </mo><mo>(</mo></mstyle></math><math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi mathvariant=\"normal\">A</mi></math> <math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mstyle mathsize=\"12px\"><mi>AND</mi></mstyle></math> <math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi mathvariant=\"normal\">B</mi></math><math style=\"font-family:Arial;\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mstyle mathsize=\"12px\"><mo>)</mo></mstyle></math></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3]</strong> as follows</em>:<br/>Clearly a logic diagram with 2 inputs, 1 output and 3 logic gates <strong>[1]</strong>;<br/>OR gate has two inputs one of which is NOT A <strong>[1]</strong> and the other is A AND B <strong>[1]</strong></p>\n<p><strong><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></strong></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.5",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Colours are represented by a computer as a combination of the three primary colours: red, green and blue.</p>\n<p>Numerical values are used to represent the different shades of each primary colour. These values range from 0 to 255 in decimal, or 00 to FF in hexadecimal.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State why hexadecimal numbers are frequently used in computing.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the number of bits used to represent a non-primary colour, such as yellow.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the maximum number of colours that can be represented in a computer pixel.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Hexadecimal numbers are used for shorter representation of data because a (modern) byte can be represented exactly by two hexadecimal digits;<br/>Hexadecimal numbers are used for shorter representation of data, because computers store and handle binary digits, and four binary digits make one hexadecimal digit;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>24;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>256 × 256 × 256 / (2<sup>8</sup>)<sup>3</sup> / 2<sup>24</sup>;<br/>256<sup>3</sup>;<br/>16 777 216;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question required candidates to recognise that hexadecimal numbers are used for shorter representation of equivalent binary numbers, and the relationship between groups of binary digits and individual hexadecimal digits. Answers seen, were often too vague, simply stating that, for example, the numbers are easier to use.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question required candidates to realise that non-primary colours are stored as a combination of the three primary colours red, green and blue. 8 bits are required for each of these, making 24 bits necessary to store each non-primary colour. Many answers seen incorrectly gave 8 bits as the requirement.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>As with part b, candidates were expected to recognise that each computer pixel would be made up from a combination of each of the three primary colours, with one byte, 8 bits, or 2<sup>8</sup> = 256 combinations for each primary colour. The answer would therefore be 256 × 256 × 256, or 16 777 216 combinations. Many answers seen incorrectly gave 256 combinations.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.4",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a trace table for the following algorithm:</p>\n<pre>I = 0<br/>loop while 6 &gt; I<br/>    K = K + I<br/>    I = I + 2<br/>end loop<br/>output(K)</pre>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max] </strong></em></p>\n<p><em><strong>Award</strong> <strong>[1]</strong> for correct values in column <code>I</code></em><br/><em><strong>Award [1]</strong> for correct values in column <code>K</code></em><br/><em><strong>Award [1]</strong> for correct output</em><br/><em><strong>Note</strong>: Column </em><code>6 &gt; I</code><em> </em>may not appear in the trace table.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.8",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> characteristics of a data packet.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>It contains a set amount of data;<br/>It contains a fixed structure (or identify elements of the structure other than data);<br/>It contains data that is to be sent via a communications channel;<br/>It contains specific details for transmission e.g. address of sender and receiver, error codes etc.;</p>\n<p><em>Award 2 marks if <strong>two</strong> of the contents of a packet are given and expanded</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.6",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Two fundamental operations of a computer are <em>add</em> and <em>retrieve</em>. State another <strong>two</strong> fundamental operations.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Compare (<em>accept</em> Check);<br/>Store (<em>accept</em> Write / Save);</p>\n<p><em><strong>Note</strong></em>: Accept the following wording for categories of operations<br/>Input (Read);<br/>Processing/execution (e.g. calculate, decode, fetch, delete, evaluate, sort, transfer, transmit);<br/>Output;<br/>Control;</p>\n<p><em><strong>Remark</strong>: Beware of repetitions with the wording in the question paper, or repetitions between the two given examples. Marks should not be awarded twice in those cases.</em><br/><em>For example,</em><br/><em>Search/look up, or comparable wording, are synonyms of the given Retrieve;</em><br/><em>Insert/put, or comparable wording, are synonyms of the given Add;</em><br/><em>Lists of repeated arithmetic operations that fall under examples of calculations (subtract, divide, multiply, square) are all examples of calculations, and hence processing.</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.2",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain why protocols are used in network communications.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Protocols are a set of rules;<br/>(That are used) so that both sender and receiver are using/expecting the same formats/methods;<br/>To allow data to be transmitted <span style=\"text-decoration:underline;\">successfully / without errors</span>;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.7",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A new computer system is being developed using prototypes.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of using surveys as a method of obtaining requirements from stakeholders.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> disadvantage of using surveys as a method of obtaining requirements from stakeholders.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> <strong>other</strong> method of obtaining requirements from stakeholders.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> advantages of using prototypes.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why more than one cycle of analysis and design might be needed.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why this computer system should be tested thoroughly before being put into operation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em><br/>Surveys allow analysts to obtain appropriate information quickly;<br/>from a large number of persons / stakeholders;</p>\n<p>Standardized question formats are prepared;<br/>that can provide data that can be easily quantified/allow quantitative analysis (the success of a survey depends on the effectiveness of questions);</p>\n<p>The use of standardized formats;<br/>minimizes the risk if the analyst adding their opinion;</p>\n<p>Survey is a straightforward/easy/practical way of gathering data;<br/>it can be distributed using various methods (printed copies, e-mail, embedded in a website, online forms)</p>\n<p>Surveys allow greater anonymity for respondents;<br/>which can lead to more honest responses;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p>Poorly designed surveys;<br/>May make it difficult to analyse the data;<br/>(Response may be limited/not all questions answered / not all forms will be returned;<br/>Can be challenging to analyse the collected data ;)</p>\n<p>Questions in the survey were not effective;<br/>Can be differences in how people understand the survey questions;<br/>Data gathered cannot be quantified;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>Direct observation;<br/>Interviewing;<br/>Focus groups;<br/>Examining existing documents/ Literature searches;<br/>Investigating previous solutions;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>The final product meets the requirements/ is more successful;<br/>as feedback is provided by the users during the development process;</p>\n<p>Costs/time saved at a later stage;<br/>as early feedback avoids later changes (which may require a considerable amount of time/cost);</p>\n<p>The best design can be decided upon early;<br/>As it allows different prototypes can be tried out/tested;</p>\n<p><em>Mark as 2 and 2</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/>Another cycle of analysis and design might be needed because the stakeholder could ask for modifications;<br/>because errors or omissions are found that need to be corrected;<br/>new/different features could be added that affect the current design;<br/>which affects the costs/ delivery time / hardware requirements/ contributes significantly to system quality and performance;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/>so that it functions the way it is supposed to / to ensure that the actual outcomes are equal to the predicted outcomes;<br/>so that it meets its design specifications functions;<br/>so that functions correctly / to eliminate any errors/bugs;<br/>so that the speed/capacity/compatibility issues are solved;<br/>so that all security features are configured and enabled;<br/>because wrong/incorrect/stolen information could have serious consequences for its customers (accept an actual consequence);<br/>because wrong information could harm the company’s reputation / loss of earnings / sued/ etc.;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.9",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>In the context of the networked world, state the role of a client.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>In the context of the networked world, state the role of a server.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>A piece of computer hardware or software that accesses a service made available by a server /<br/>The role of a client is to access a service made available by a server by sending a request for service;</p>\n<p><em><strong>Note</strong>: the term client is to be understood only from the computing perspective, i.e. this is not a human</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A program/host computer that awaits and fulfills requests from client programs (in the same or other computers) /<br/>The role of a server is to fulfill requests from client programs (which can reside in the same or in other computers)</p>\n<p><em><strong>Note</strong>: the term server is to be understood only from the computing perspective, i.e. this is not a human.</em></p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "17N.1.SL.TZ0.3",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A company has a large networked computer system.</p>\n<p>Some of its data is non-sensitive data that would cause no risk to the company if accessed. Some data, however, is sensitive, such as the company’s financial records and documents that contain trade secrets and personal information about employees or clients.</p>\n</div><div class=\"specification\">\n<p>Data corruption can result in data loss.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> ways in which access to sensitive data can be managed.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> ways to improve the security of the company’s network.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how corrupted data files can be recovered.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The company is considering implementing a virtual private network (VPN).</p>\n<p>Explain one benefit to the company of using a VPN.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p>Each user should have appropriate access rights;<br/>Managers, employees and clients/customers are permitted to access different parts of the data;</p>\n<p>Sensitive data can be protected by locking it with a password;<br/>preventing unauthorised access who doesn't have this;</p>\n<p>Administrator should record who has logged on and from which computer / and for how long;<br/>to discourage security violations/ to avoid undesirable events from occurring;</p>\n<p>File systems should be encrypted (as it passes throughout a network / resides on computers);<br/>to make data unusable if accessed by unauthorized user;</p>\n<p>Each computer should have access rights depending on its location;<br/>Logged in computers should not be unattended;</p>\n<p><em>Mark as 2 and 2</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p>Each user should have a user ID and a personal password;<br/>Passwords should be regularly changed;</p>\n<p>Two-factor authentication for remote users and administrators could be required (this could be a digital certificate, tokens, thumbprint scanners);<br/>In addition to the usual user ID and password;</p>\n<p>In order to prevent intruders/strangers from accessing the company's network;<br/>the router is set to accept only specific MAC addresses;</p>\n<p>Regularly installing updates and patches;<br/>to ensure the network is protected against new threats such as malware;</p>\n<p><em>Mark as 2 and 2</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>Restore files from a backed-up data file;<br/>ensuring it is a recent copy to minimize loss;<br/>a parallel/failover system could be operated;<br/>that could be switched to (if the live system is corrupt);<br/>data recovery software could be (installed and) run;<br/>to repair corrupted files;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em><br/>A VPN improves data security;<br/>By passing the company’s data through a hidden tunnel;<br/>And encrypting the internet traffic inside encapsulated data packets;</p>\n<p>A VPN improves the company’s productivity;<br/>as the workers will not have to be in a particular location to get to be productive;<br/>VPN is not dependent on any particular network or Wi-Fi connection to work/ can be used on any type of device;</p>\n<p>Remote access;<br/>VPNs is not dependent on any particular network or Wi-Fi connection to work;<br/>The company can have a remote workforce /employees or freelance staff working from different geographic locations/can connect their different office locations;</p>\n<p>Some databases/websites that support the company’s business operations may not be directly accessible in some countries;<br/>A VPN helps to unblock geo restricted contents / has ability to bypass geo-blocking;<br/>By hiding IP addresses / obscuring the access requests to appear to be originating from various IP address which are not in an unrestricted location;</p>\n<p>A VPN can reduce a company’s infrastructure costs;<br/>Because can be used on any type of device;<br/>So, the company can offer BYOD options to employees;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.10",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>A different airport has two runways for arriving flights. Two objects of the <code>LinkedList</code> class, named <code>runway1</code> and <code>runway2</code>, are associated with these two runways.</p>\n</div><div class=\"specification\">\n<p>Arriving flights are added to a list depending on the runway they are assigned to.</p>\n<p>Both linked lists contain <code>Arrival</code> objects, corresponding to arriving flights, and are sorted by ETA.</p>\n</div><div class=\"specification\">\n<p>The following diagram represents <code>runway1</code>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>Consider the following method.</p>\n<pre><strong>void</strong> process(LinkedList&lt;Arrival&gt; myList, <strong>int</strong> i)<br/>{ <strong>if</strong> (i &lt; myList.size())<br/>  { process(myList, i + 1);<br/><strong>    System.out.println</strong>(myList.get(i).getETA());<br/>  }<br/>}</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline an advantage of using a library.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of dynamic linked lists to static arrays when managing arriving airplanes.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Sketch the resulting linked list when an airplane with ID RO225, STA 12:05 and delay 0 is added to this list.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>recursion</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Trace the call <code>process(runway1,0)</code> given the diagram of <code>runway1</code> as drawn above. Copy and complete the following table.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>In case of bad weather it is possible that one of the runways has to be closed and the two linked lists (<code>runway1</code> and <code>runway2</code>) need to be merged using a method <code>mergeLists()</code> into one new linked list <code>runway</code>.</p>\n<p>Construct the method <code>public LinkedList&lt;Arrival&gt; mergeLists()</code> that merges the two sorted linked lists in to one sorted linked list <code>result</code> which needs to be returned. You may assume that <code>runway1</code> and <code>runway2</code> are accessible to the method and do not need to be passed. The original two lists are allowed to become <code>null</code> as a result of the merging. </p>\n<p>In answering this question, you may use the following methods from the JETS <code>LinkedList</code> class, in addition to any method provided or developed in this exam paper.</p>\n<pre>addFirst(&lt;object&gt;)   // adds the object at the head of the list<br/>addLast(&lt;object&gt;)    // adds the object at the tail of the list<br/>getFirst()           // returns the head object in the list<br/>getLast()            // returns the tail object in the list<br/>removeFirst()        // removes and returns the head object<br/>removeLast()         // removes and returns the tail object<br/>size()               // returns the number of objects in the list<br/>isEmpty()            // returns true if the list is empty</pre>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying an advantage.</em><br/><em>Award <strong>[1]</strong> for an elaboration of the advantage.</em></p>\n<p><em><strong>Example answers</strong>:<br/></em>Convenience;<br/>Because implementations of common tasks are available;<br/>Reliability;<br/>because these implementations are fully developed, functional and robust.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying an issue and <strong>[1]</strong> for a valid comparison related to the issue.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em></p>\n<p><em><strong><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAk4AAAE1CAYAAAARVG9qAAAgAElEQVR4nOzdfXhT153o+6/kDElT43AYmLJFMmGIB5NenCaxh5y23CLbiRwmTcIxhXQSbCf2E9xToMzlgFUISWcCITF2yUPeE65UwJQZSKQLJZnEJlZMCu3gkdNM4BRLl+aSM0aiJ0yKX5oCibTuH9qyJVmytl958e/zPPyBtbX22muvtfbSXmv/tkkppRBCCCGEEGmZL3YGhBBCCCEuFzJwEkIIIYQwSAZOQgghhBAGycBJCCGEEMIgGTgJIYQQQhiUcuBkMplGMx9CDAupt0JESFsQYmCMtpmrhiMRIS4lUm+FiJC2IMTw63fgJCGexOXGZDJJvRUCaQtCDJTRHxqyxkkIIYQQwiAZOAkhhBBCGCQDJyGEEEIIg2TgJIQQQghhkAychBBCCCEMkoGTEEIIIYRBMnASQgghhDBIBk5CCCGEEAbJwEkIIYQQwiAZOAkhhBBCGCQDJyGEEEIIg2TgJIQQQghhkAychBBCCCEMkoGTEEIIIYRBMnASQgghhDBIBk5p/Qfu8mxMJhPZda18ebGzIy4x3bTWFVze9SPoptxkwmT6Ae7gUI7gS4LuH2AymTCVuwkCve0nm3L3fwwwvaF815gvW+vINpkwZdfRmurQ+5RPsuM0qhO/Zzd1P3gx9f7ECAnT7W/mjTo7z7Z2D8N3R75+ikuTDJyEEGKUfNn6Gn9b9H1Wv/uni52VsefL3/DK3xawcHULodH8rrjiXHWxMyCEuMi0ErYrxfYRSfwGSrafQI1M4hfJVWglr6DUKxc7I+KiuhLrtjBC7jgNSphuvwenvThyu95kwmQqxu704O8O927W7cfjtFPQs42FArsTj7+zd5sU0yQ9UwhxUwGd+D1O7AWW1PsUIyhMt7+BuvLcSPlbyqk74KMjbpM2nMUWTCYLxc42Ys9M2O+k2GTCZFmLp/Nk723+N/6VVncd5ZZIPbGUv0hL8ELq/Ub37W4lqO+gd8rpGTy/jdm2YC1ufyd0+zlQV44lWm+2t/R8N1UdDAdbcfd8J3q8fgY2yZFkOiNmf2+0/mvMPnIpf/Zwb76SnYHgAdYWWCLbOo/peUlsj0naGSSUQS7ldQ34OwYzX5Zsqi5dnxD5zp/lr+Z3AL9bTf6fXcbTu5cUA/1x0E35n+Wz+ncAzazOHx8zPduJ/8CzPe0v0gbrcLcGI+035XdTTNWFg3Htuaeupe2n0+Qjpt5lP/MGDWv14y124j+lt6nsf8Td8I/6NWcRTv850vUfPf1Sz/ZR5/A7F0XyYffQmTTPY5RKoZ+Pxpj/pVxlNylA3VTrVV8opVTXEVVr1RSQ8E9T1tojqksppdQflLf2niTboLBuVt6uUCT5gEuVgYIq5Qp80bPXL7y16iZQlLlUQCml1HnV7lqqtCTpaWXb1PFoemPcSNbbULtLVWhJzqf+L1I/QqqjaU3kPNkcytdzWv6kfI6FkfNV3aQ6YupV0n8x302931mqwnVShVRMfUla35aq6rJZCX/PU9VNn0Z2kKwOpqzjvfvs6wsVcFVFtuupt9HjvEmVuf5Xwv6S/dOUzXFcTz/+u6FAo1pj1RLaWT/lo1Uox/EOfaOTylWRWAYx/26qVd4vVHJ9yifJcabtE2K+06fOjIwx04cb6Y+T1bmbapX3i9T9KtpS5Wo/3893k9Ttfvp9rcKl2lN20wbykaIOadVNqiNZHrU1qqkjZKD/+FQ1VecltD2lVOi4cti0+L7iCme0zcgdp0H40vc+rzQHQVtDU0cIpUJ0eTdjJUjz6jr2+M/Bl7/jvVfeAvKobvoUpRSq6wi1Vg2aa1m7x8+A7hN1HuK5ZS8S1CpwHO/Q0zuKo2wWwR3P87OWz0boaEXEGZqf24gzCFj/gabAeZQ6T+DIC5RpsduZycq/k1INaHyHQyf0X3DhkxzafQhYyIbKb5EV+xXrGly+DpQ6T7trKRpAYwvHfv8lcI4TDf+MM6hhrT1Cl1Ko0ElcFbOAYxz8+ExCPZpFmeMoXT11Emh+kXpWcbwr1FsHacX1wScp7nacw7+njtXNwfhjbfoHrBzDuexVmjuH4y6nhrXaha8rFHNMQRoP/pbfJ2569gO2/XglTzeDdc12dq2cTSbQe16ix61QqoPjjgq0oJN1P/PSSZjO5ldZ5jwG2FjT1E5IKUKBQ2wumzUMx2GkT/gSreQVvvDWchPATbV4v1CcWJUn6yWGyFB/rJWw/QsvtTcBWKn1dqFOrCLP/DENr7oJcg+13j+glCLU7qJCA4L/k49PX0j93SQnLux3s3b1W8TXs0bWWDWCzo0813wm+UGEDeQjTnS7L/A/Htuf9PYTIf9a/mvWBQP9x0Tyi21oBGnc/StO6E07fOJX7G4Mgu2HVFonDe0kXWFk4DQI5inTmasBwacpmvkIdW/spfGTHOoC51FqDxUzrgHzJKbPnQW0sqmogPK6Xbgb2/nrulZCKkBDxcwBFf6X/+8HuIJA0EnlzddFbreOz6VyxzGglfqGj+RW6kgKn+HkhwHgJsqWV1CojQPGoc1+gPLSvPhts26huDQPOMTuQycJE6b7N29T3xgE293Myb4mZmMNW2kZ82dkAeOY+s1C7opL7BpmVOxBqZPsKjhDo/sNnGuWsMB5DIDPP+3g89jNtXsp/97XycRM5je+wz03AeRRWn4PMzPNkPl1Cu7JSXOwn3Ls4Ad9j7XwJ7ynFCqwkcKs4eg65lBa+bfMyDSDeSrfvOfbKbb7HTsq50fqulbC8qVz0aK7//ITPnC1AsfYUZnLeJMJk+k6bq50EgSC9e/i7TzH6ZMnIlNqZVUsL5yKGTBr36Sy/F60FHsdCEN9ghgRQyp780wqGgKo0FYK2j243VtZs3hZ5AcSXXzacS71d/v4kt8fa6EREurZXWx8L4BSXmoKUwxABpoPWwn33TYBuIrMzNjjm0PpfbeQCZgzM7nWUP+R7MfeWX7zCzeNaNge+BbZMlKII8UxCOap32XD/h1UWzUI7mD1wgUsWHAP+ZZpFNjdkbls843M3+BgW7UNOMaO1Q+xYMEC5udbyIiuO0npSz795ERkLUTS//cVPH2WPw7XAYq+wn/ks98FgQlMmfCVmA+u4brJ4xM2nsjsRQ9h7fkF9xkte35OM7OoqCpK6ISuZcqEr/Y2xMk3kntT3I7pbttOueVqLPn3sGDBQio3NfZ+e/J1XBuX3ESuuzaxWY9n8nUDuHB/+b/5+HB/tW24TGTC+OjP9quYfGM2N/W7PRB08/w/fdi7zurTTzjab8P4jLN/vEDXZ58CoE2ZwFd7PjRz7XUT48tvkAz1CWJEDK3sO2lzVmLJsJA/fwELFixhU3N0VekA2w3nCHzsG+RRDCwf2q3TmJLs6q1lM23KuJg/GOw/sm5n0cp76Pmx1/kBeza/BVoJVcXTZaCQQMpjUMah5ZVS814A1eWjyeXC9XotZVqQ5k3LWK5Pw5m12ZTXNKBUB76mvbhcP6e2bBY0P82C5W/gN9yXmvnqhImRX8b6LX6lEv5tLxmWX84iBfNXmXiTBgT48GTs9Ng5Oj7tStyYzNvmUWrTIr/gvP9KQ30raPfy0J03DKzRhf3sWbGGHUENa/VruPZ6CYS68NZah+GgUrjqL5j+7ZuAzzl99o8Dm1IeKdbNeNsbqdZipl8AvjqBKRr0TKEktgv1CiXaNYyfOBmA4IcnOd1zQGE+7/gs/o7doBnrE8RIGHzZh/1vsKLSSRAb1Y7X2esNEOqZlhuoa7BM1+/mnj5L1wBO+EDz0ecHU88HCT+cDPcfE7jtvhJsBGnc/Uu8R96lPghaaQl3Th2HiCcDpwG7wCn3ssiTOQX/iKdrGoUlJZSULOT+eb3rJcKn3FRaIk93rPV0kV14PyUl3+Pv7p8bP8Dp6fgP89avTxEGwsH3eP75X8RsFHMr9XfbeW5H5Gmi3ieM8rF7Usydi+FhnsacB+YAQRrrd9McvABcINiym+31rf1sfwiH/Wm9E7qT/IFOcXUH8B0NAn/O9DtszL8/j6/9/te43hrsL1sjJjNr7u3EH2v0l2uyp29G2k2ULf8eeVPn8qMXlqLxOutq/oVTYWKmRZvZ/JyLtu4whE/h0Z84ijwNdA3Zc+7GBtC4m23N0Xb2axzb9w8wgGUyxvoEMRKGUvZhuttPcBRA+2vuKL6P+/P+nN+//yZvDeqG61V8bdbsPvWM7mM4y3OTPmk7MvmIMYD+w5z9LR6wadD4KvaNewiSR2nxLfHrMQUgA6dBGMdUWxkrrRo0/wNFlqsj640ypkXmjfVbm1dNLeSHK+8BGnm66HoyTCZMpqu5fsGLBGOnbDJv4o55kYV6zgXTyDCZyLCUc5C/ih9gZeXzyIYKtJi1HBkWG083B9HKlvPI7IkXpTTGjmvILv5+ZLFmz3m/Gssdy9iR9Mob3T7Ir5p/RTDZonAj0tSPPmuchsU1zFi0Sn+QIXqsGYy/+eHIL9c1jzIv+2Ks2elte0Gnk3/6zVlgIrMfWU6ZBsEdD3Pz+AxMGddT9HQjaBVseCSfLMCcXURVxSxi22OGZQ4rdxwb1nz11yeYAfP4iZHpSAlHMEyMl33vXeNoSIHNtN2UzzwNCL7Iguv1Nl20H6w3Ebe2qM93k0eaN88oYWNtQr8fXYtqrcI+L9m0l5nMHIP5GKiB9B/m6RRXlaDRSnPz72RReD9k4DQYmbNZtb+ZJkc1vTc8NazVDpqaN1IydRwwgbxVu/A1OSJz71HWahxNLraU3Kg35ti1UAA2qh072fr4vQm3YrOYWfEszXHpzaKs9h2aXyyNLPwVI8o8dT5bmt+JTLcCaGXUNnppSjFtZp76HR6KLhzvsyjc6E6T1I9te3lj5//gLqKLn0dgEihzNit37cdVW9Y7gNfKqHXtZ9eGu3oXZ4+2zHx+ULcaK2+xeq0bf9hM5sxSXmxuwtFTRqCVbaax+VkqZupDVfONlGxx0dhzPJG28z+bnk6/rspQvoz0CWDOnsf6zdE8aNxAiNG8d3dFMlj2mKczb/1jvU/B3pBBxp//be/6KABrNdu8/w87l99J3EM3Sb7LuWQxxCeQt3IrXldtzNO2syirdeHdtUZ/0KKvuHVa/eVjoAbUf4xj6p0lkZkNWRTeL5Meu6DvByYTKT4S4pJ1SdXb7mM4l/4dlTv+E5vDw9sDfJJSiKG4pNqCuAyE6W6rZ2nhw+wILsTh2zHmngY12mYkhIgQwy3optyygB3R/2trsH9vhgyahBCXoC8JupdhWfBqz1+06h/wvTE2aBoI6cuFGG6xIQWsa3A1/3iY4h4JIcRwiw0FEglK2/y4VRaF90Om6sQVReqtEBHSFoQYGKNtRn4GCyGEEEIYJAMnIYQQQgiDZOAkhBBCCGGQDJyEEEIIIQySgZMQQgghhEEycBJCCCGEMEgGTkIIIYQQBsnASQghhBDCIBk4CSGEEEIYJAMnIYQQQgiDZOAkhBBCCGHQVf19aDKZRisfQgwbqbdCREhbEGL49TtwkhdEisuNvNhUiAhpC0IMjNEfGjJVJ4QQQghhkAychBBCCCEMkoGTEEIIIYRBMnASQgghhDBIBk5CCCGEEAbJwEkIIYQQwiAZOAkhhBBCGCQDJyGEEFeYMN1+N/YCCyaTCVPxc7hfW4TJshZPZ3iE930Gjz0fU7ET/0jv6lLR7edA3VNs958bhrTacNuLI+fNtAjncKQ5zPoNgCmEEEJcdsJ+9ixfRv30F2hvKmGqGeBHqCUXO2NXojCdLdsoX32CDfcNNa1z+Pc8wYL6v8bVvp+SqeOGI4PDTu44CSGEuDJNmsB4ucpdhrKYMP7Sva8jVUoIIcYkfUopcfqq04PdYsJi99AJRKa9PDh7pk9MmArsOD1+umNSCwdb2N6zjYUCuxOPvzNmi078Hmfv9FnSbfoKB1tx15Vjie7bVIzd6cHfnXweLOx3UpxxM5WNQYKbirjOlI/d8x/4ndGpui/pbn2WAlMule5P6Emlu4W6AguWSjenwkaPKTF/uZTX/YIPTp9PX/zhIK3uOsotphTpR6f8nsHd8GzvdgV2treeotPfQF15buRvlnKebQn2Hkufc5aYdphOz1ospnzsnjMxmUqoE50e7BYLxVsP0LLdToF+Dizlz3LA36mns46ZRU8T5HUqc74SU2+S6PbjcfamE1ePwm04i6eTU/k6BJ+m6LqM/tO6iGTgJIQQIrVuL69UVXMw56d0KYVS5wk8fi31RevYo68/CZ9y82heJZ4pTxAIKZRq4+Wcwyy2rsV96gIQprvVQdXiw+S83IZSChX6Nx7P2E3R8jdSrwXqbmHzg1Xsy1hCa0ihlCIUWEVG/RKqXvHGDdyizDMqaAgdx2HT0Kqb6FBeagonx25BZt4j1NXeiHNZLXtPXQDO0vrKk6z2lfDCk99lqtnIMUXz9wDPf3o/zV0hlDrMY9NP8NaOY2kK9Sytmx/l3n1fYWnr+fjyqHLQGjsobHyO5z038pg/hAq10/TND3k4/3pmPnWC7zzTilIdHN9wFbXzn9KPJUx3Wz1LrSs4GM17qJWaKQdZnPMgda1njZz1GEEal9ThGv8I+5VChdrZOfUdbFUOWrshq3ADbU1r0FiIw/cnAjWFZCVLpvsYzqULWHzwL6kJnI/Uo5q/5OBiK/fWtdBtnklFw8f4HAtBW0NTRyh1WhebSqGfj4S4ZEm9FSIifVv4VDVV5ym0NaqpI9T7544mVa2htOom1aGUCvkcysZC5fD9qf90bA7lC/X9eySdPymfY2GSbfoTUh1Na5SWmD8jaYWOK4dN6zmGnu/EptV1RNVaNaVVvK6OH9msrMxSFa6TKmT4mFLlL9V3Y3Q0qWotT1U3fRqf7biyTnZ+9H0mnI/I96Lp6XmscKn2JHmP5CuaTmIeEvaZUBeS5yF5nhJOiF5WS5Wr/Xzfv/d8N8l5GkVGrx9yx0kIIURKZsss7rIeYp3jTVo8b/adWuv8iIb6VrRbpzEl7oqSyfU50wnWv4u38yos3/gm1saXcOz9JR53c8qptpg9k1W4kUBgA7NPv4/b7cbtfgOn/f7IdM5QZebzg7rV5DgXcvMdtVDrYEvJjZFpGEPHdAZvQyPB3GyuzzT32aZfWYXUBLzUzP4Mj9sdOTannaKcShqHelydH9FQHyD3219HS5J3jp6gPW3Z98dM5vXZ5PIxvvZk9/yS+Uwvq9uZpcUu+B5MWhefDJyEEEKkljmbVfub2XnHH3CtX0JRznVJ1xlF1hOZetdBmb4SM8Axk5m3gv2+Ou44+ybrFxSQMz4j6VqpON3HcJZ/g/E5D/L8kf8EzEwo3oTXsXB4BgC3lbC8YhZwO/cUzCAzYYt+jyl0hpMfBga5707anJVYxudQ9PwRzgJMmMfL3tewJQ4i+gzM+hc+fZIPg/1sEDzBydMXBpnvQQqnK6sAH548w+USvUEGTkIIIfqXOYPCkkepeS+ACgXwuu7i9LoirOub9cW7GjbHcUIqsg4p7l9gI4VZZsBM5gwrJRU1vKcUoYAX1z2nWVf0IOvjFihHncO/50kqD8zF1X6S92oepaSkhJLCqXT4Ph6Gg7rAqb21LHNejdXaxupVP4tfW5TumP7LXzDtVsug9hz2v8GKyhbmuU4Seq+GipISSkq+g6Xj/+PoEI/KPGUat2r9bKBlM23KKD/mb56Upqws3Dpt0mUzILlc8imEEGJYGZhSSsaskVfyMOWleQQ/PMnpzFsoLrVw9PBvCcbdMjhLa913UwaCNGt5lCwpp1RLdbehm3bfx5A4vRP+I2fPGHhqLY3wqTd5YpmbnNqX2LdzExW+WlZFF5xnGTmmieQX29D63PnS8516z3S3n+AoN/PtWV+LuQh/SdfZYXiGLGXeo+WZzfWZV+lTZKMlWlYfcCwYe7crWhbTybk+8X7fpUsGTkIklRh52Ik/3InfvVZ/lNZCsfM3tDkHEo34XMwj0cN9UzpMt7+BurW7ei5SYb+T4ks08q64FFxD9py7sQX3s/2N30YGDN1tuJ+qYVPMVE+kHhVjd7fpU2phuv3v09ACFVVFZJsnYf3RWua9/RPWbjmsX6w78bs3sWo11G4sYYZZr/sFa3H3rJHqxP/uu7RQQlXx9CQXI/1i27ibbc2nIgOrcJCWLU+wzJnuqbU0wp+w94mf4MxZTd0P8sma+l2efKEE3+oneaX1LGDkmMxkWat4Yd5+Fi+vp607rG+zmfWbWvvZuZms/Dsp1Q5Rv+2XetoXCLZsZe2yF+lvls2Yicx+ZDl3JeS9zWln8aYpet7BnP0tHrAFqN/+1gDynvx4egdhYT7v/jzJINhM1uwH2XDXQZat3UpLsPfpv+WLt5FTu4pFM64Z4nGPHhk4CZFMbOThkEI1VJB94g2WL9jPdNdJQipAQ8VtzKzYEzMVkc41zBjQ9gPxGS2Ox1jdKoMkYZx5xvd4vrEC1uUy3mTCdO/P6FqwitdsWsw2i9nmrWLyvoWRbUwZjLfuY/LyV9kwP7KY2jx1PluatzD39JNYMkyYTNdh3TeR5d6trMybAFzDjIe34F0+kX3W6/T1Qvo2+x9jftII0WayrMvZv+1Wfl10PRkmE6brf8z7N5Sz11VNf7NR/YtO0d1Ibd0j5GWagXFMnb+aFyo+6ZmyS39MgPlGSra42J7roXB8BibTTKqO3Mzy2vn9ZyFrDn+/v4bZvy7X087jx+9Ponzv61QP/sB0ZjJnlvJiXN5n8t9932anbxerevI+g0XP/4yV1HHz+AxMpoU4umw8/trCge8xuxj7mg4qc77KVxf8MyeS/S7MnEXFiy52zv1f2C1XR+rRf/8tc3c2s3/V7D7ryy5lJv0RvL4fmEyk+EiIS9aw1dtwG855hay7dSdteiyRsN/JvJyXuLXpHWoKJw19H8PqDB773RR9+EN8b1cwwxzN7zs84NtBxWX0a04MD+nDhRgYo21G7jiJsSkcpHV78gi2fSMPR7bJyKmkkVY2FU3Wp9s+TzL1doFga33vFJ+lnLoD0aeGkk3VJWzf52mlaITfRWz1NMdEMc6lvK5B304fNG1qhcZKcjISowFf4JR7GZZkU4SdHuyWxO2jjEaWNhIROt1x0jeqcJoI0UOLrNx/Hegt9/9Gndvdm46pGPv2FoKdfg7ERot+9nD8epL+IiTH5r3Azta4qNj/R5JzEdn2Uo2iLMRYIwMnMfaEP8H9qI17PdEItiG6Xv46BxcvYIX7E+gTeViPWOxzYCOP6qZPU0y3XeCUeyV5+TthuYcuFaLLU8jR8ki6fS//+vb3vsuUmtbI0ztdPyXn4AqsK/b2vPYh4nWWrG9kfOXrevTkzUx9a6kePXkShTXv0FSdBzYHvpA34Y7YOKbeWUIp+/n5u/8R91qGTu+71GOjOH/iYAvTQERoI8d5ltZXVrL44Nd5uSsUFyF6+R5//48pDziyMmnrQO/+9rL6eS/THzsciXTc9C1aHr4Dy8ynOPadZ2hXIbqOr4LaH7Bur/69dBGSY/Pe/C8cmrgavzpPwNeCu/or1P/8/fhz3/kRDfVQWnzLpRlFWYixZqgRNIW4lKSvt6mi3CZEAe4TeTgxOq9SfaLcJvlOfLTehO07mlS1pimb47iKi5EbF1U4TYTfnujEfaMVx0ch/oPy1t6TEM04NgpyMkYiSxuI4mzkOEPHlcN2U99t+jXYyMpG6sAXycs92bHEnXejEZKT573Lu1lZ4/KVKjp1etKHCzEwRtuM3HESY4wewbZPLBP9yZBgIw3ezwaVcvjEr9jdCLk5lpiFjpMorPGiGiLrjmK2jtztCSaJX5JpISc3QH3DR/1MzQw0CvAEbruvBFvjOxw6oS8gH5Y7GePSRIQ2eJzmKXzjrpk0rvsZe1s8uPsLijhkI1cHhhYh2UzmbfMotX3A7kMn9btekfQovZP8YX+gQAgxGNISxdikv327NyJwdA3TaNPXTMVGJ9bXVw03c3YRVRXHWef4FZ3RAU3OQyyaPdhpOjAeETrdcU4gb9UufDvv4KyrhgVFOYxP8Tb6PgYYWbmHoTowwPgyQ42QbJ5OcdXdHF23g+bOsD64ncLKRbfLNJ0QlwgZOImxyebAF0oSEVglrg8aaZE3ivfNhxr+N4Obb+DOh+6FnvdsHSS3dB63DWbQEZ+wgYjQRo4zixmFJVTUNETWBXlf4J7Tz1JkfWYE4l4xMnVgyBGSo+vRGmnwnokMbnNLuO+2CYPLjxBi2MnASYwx/USwbX2WgiEEjIwElIOjvkDMnRb9Sbo+6UaD4B3n8LHfx9+B6G6hriCbYmfbML+7yUzW7PmszGnk57tf5636qTwwZ1o/ncDgIkvHR4T+jMxBHec4tLz5LCm/F23Y3601cnVgWCIkZ93OopVTqP/5P7P7rYPkPvAtsqWnvmREAoKmehI1aqDBbgcbHHckg+qKVKQ5ijEmGu03IYKtfy/rV70IQ4lga57OPHsVOZtqeMYTiXQcDv6SbfUfYE2WbtYcfvTCXN5e9gRboo/Kd7fhXv84q1nKxkUzBtBAYwc55+ju/jLFZrdwX+l0nEuWsTn3buZk93esRiJLG4gIbeQ4w204i7MpsLt710h1+3m3oRUqvk9xv/kcqBGsA8MSITmyHi3XuYIlm9MNbsWlaSSD3V6M/YhYUtJi7NGj/cZFsLXuY/Ly3exaOZQItuPQCtewy7uY0Pq/IcNkIsNSR+jhXSnSHcfUko0075zLaXteJDLy+IXsm1yFd9dSPaKxUdeQPe9R1lxYR07GdBbsOZHibtU1ZBd/nwpNw2bgTkb6yNJGIkIbOE7zTB7etpvlk/dhHa+vO9K32b/hu0wd7p5qxOoAwxIhObIebRbY0g1uhRCjbmiP5UUfgX5aud7ZrMo0FKCwVqtt3nbV4XtH1ZbNivxNK1ObjwTiHjUOBY6obdW2yOdoylrtUE2+noe/9cd356tal6s3HWyqetsRFejwqcbaMqWBglmqbPMhFYhNvJ/gv5QAACAASURBVMunmhzVykpvnhxNPtUV/Vx/rNhavaU37W9ZlVVLfOy79ziTP7ad5nHxnseIQ6rL16QcPcebJE9py0SkY6zejm0hn0PZ+jwuLy4poePKYctTFa6TAwjPEC99Wxha/61CAeXdlr6PtdW+rt7p6as1Za3eobyBT5WvsXefWtkL6kggtj52KF+TQ1Vbtd5+39GkfF2h+Lxbq9VrPWnnKav1piT9dD8hHfoLIZIszIW2RjUeeVXZmKXKah0x16VZqqz2nZj8JYQeSVJeWtlm1djTt0e3L1O1/1zbey60MlXbGH+NiJe4H2PXGZGc0evH8PyOG0QAuvApN4/mVeKZ8gSBkEKpNl7OOcxi61rcp2LXBoxkALogzfX/zsQ1h1Gh3+Pb8X/zeCkjE4Cu28srVdUczPkpXUpF8vT4tdQXrWOPvp7CeJkIMUjhUzRvc3NhZRm2pO8HExffBYLNu6m/8BA/tN0w8tMCIxpANEjj6q14pq/BrxShwHa+2WIn31LAU8dm80y7QnUdZQOvMH/dm3q/20mb8//CuvhgT8DUUOAJphxcQc69W2iNDXcRF0D0dXY+vgjq3bwb11/2E9LBPI05D8whWP8u3ugaoc6PaKhvhbi1dTFpTLgKOMaOt07o16XEgLRJ6OWVvy2D5b4OlOrAM/cY5Yl9e/AAb30wPXIu1HkCO6fzlm2l/uJhAwxcZ8QwGNrIa7AB6PoG64tNLy6Q3EgFoIsL4te7zcAD0Bm74xQfjLCfsuy3TEQ6xurtWKT/MkVT1mpXzC9jcSmJ9BMorGuUa4h3mtO3hZEMIBpK0ccm6+eSBYad1fduW1zfnyI4a9cRVWtNCKQaF1C2r8S+OXJHdr4qezCvN52YNPoGwo05hp7jij+mpN+JO54kd6iUMhAYNtl++rvOiP4YvX5cnDVO+oheu3UaU+Ij4nF9zvT40f+AXZoB6MyWWdxlPcQ6x5u0eN7sG5tmRMtECH0RqQrwXk0JM4YcgkCMBPOMChqUQr23kZIZl2rkppEMIBoNmHoz3571tSQBUxOfWk2QeQv3ld5O4+5fcSIck14/rxWKPA0b7fPPceLQOxwtrWRp0XR9XwN4NVHSgLSRNBsTn6jMKqQmEKChYmaau4qf93/MsceS7jojhsXw9J6DDEAX+wLVyL+vkFP5esJWV0gAuszZrNrfzM47/oBr/RKKcq5L+hJTY2UihBDDZEQDiGoJkfTTucDpkyfoL/xr8MOTnE7ZgesPPxx9CUfzGSKDvIPkrJzP7FQ/fM3TmPNAdLDVTbvvc0qL/yv5c+4mt/5dvJ3nOH3yxOURvd3gdUYMzUWsBRo2x/HICz8T/w3l0cpLOQBd5gwKSx6l5r0AKhTA67qL0+uKsK5v1l+tMUJlIoQQw21EgsiOY8q0bLR+tuh7Vz6eeep3eKiUyKt8Oj+ioX4qpffd0s/gTQ+7cfQE7ad+Q0P9teRcn4l5yjRu5QQnAz4O7T51+bxkOe11RgzVxbkSZ91CcamFo4d/SzBuEHyW1rrvYip26m9VH4zRDkAXnQIcILNGXsnDlJfmRX5BZY5kmQghxHAZyQCi/QWGDeA7mvguyOT5m73oIXLq97B79y+oTxuvDH2QtB/Hupd6t8+6heLSU9Q/XUf90bnpp+lS0gdmictEwm04iy0j27cnXmfkGjIsLtItjElYf7SWeW//hLVbDusDhU787k2sWg21G0sSXog6EKMfgC4yRx6gfvtbtHWH9WPZzPpNrT3bRKLNFmN3t+lz1WG6/e/T0AIVVUVkm0eyTMSY0unBbrEMQ+TxMJ2etViGdCEUV56RDCAKZOXzyIbZCQFTj+FcvoJNOasNBIbV16rm/oIlS/YYi7yedQvFpVeza8de6Lmjlcn1OVNp3rEL31DXuGYXY1/z52xa/yKe4AV6npxsvH1Y+/b015nh2c9Yd9GK0Tx1PluatzD39JNYMmKC5nm3sjJviNNiox2AzjyDRc//jJXUcfP4DEymhTi6bDz+2sKYTRazzVvF5H0LI4EEewLuvcqG+TdiZoTLRAghhstIBhAli5kVzyYETP0f+OZuwbd/hbHAsObpFFeVoDHHYOR1/S4aeTFTctE7RQNdp5UsP1Mp3LAN78Ofs95yNSbT1VjWf87Dw9y3G7nOiKEz6Y/g9f3AZCLFR2NDuA3nvMUcrnKxtUQq3OVizNdbiNxxmrmYDzd4eDvtEzv9CdPpWcfMohNs8O2gYih3EcSoG9tt4Rx+ZxnWw9/n37aWDH/keXFFMtpmpDolNcoB6MSoCwdbcdeVY+l5IijxyZMw3X4PTntx71NDBXacHn/qx4KjU2RbD9Cy3U6B/j1L+bMcSHgsOBxsYXtP2hYK7M6+jw6Hg7TGpJN8/xcItrqpK8+NbGMpp+7tDzjd94DTpxUO0uquo9wSzfdm3v7g1IDKVYhLQeQdkV+w8oeFMmgSw06qVILIHPHVWNafZ/mrlQN8X5i4LHS3sPnBKvZlLKFVfyooFFhFRv2S3si/g47AG6RxSR2u8Y+wXylUqJ2dU9/BVuXoiXhsKEK8ocjMYbpbX+TB/Ff59P7XI/n0r2H6BwfYEfs8t6G0ztK6+VHyn/+M+5s7UCqE/7HpfPDWgX4fDRfikqIvuM6w1BFa/jQ/kCUOYiQMNYKmEJeS9PU2VST4+Mi/g4rAmyIafXyU5YFEzU8TmTlVVPm4iMQDifKcGA051XfF5UD6cCEGxmibkdspYowxk1W4kUBgA7NPv4/b7cbtfgOn/f64QKPDF4E3IWK9oQjxZ4xFZu78iIb6QN+Fq5kWcnKv1f9jJMqzHq8smBiqY5ChNoQQ4gomAycx9nQfw1n+DcbnPMjzR/4TMDOheBNex8LeVyaMcAReQxHi00RmDp8+yYdG59H6Tet82mjNQgghImTgJMaYc/j3PEnlgbm42k/yXs2jlJSUUFI4lQ7fx/GbjlgEXoMR4tNEZjZPmcat/YVYjtVvWlPTRmsWQggRIQMnMcZ00+77GBJfAh3+I2fPnE/9teGKwGsoar7ByMzRtBJfANodwHf0c/0/RtK6oEdrTnwBdjTavrgyncPvXITJshbPSLxAvLsNd8+To4tw+j+n2+/GXmCJ/K3YSVubk2JTPnbPmYuc3078B55j7fahBo0VY8JQF0kJcSlJX2+jC55tak1TuwoppVQooI5sLlMaKPTF0pHF4TZV7TquuvTvdflcqtqapypcJ1UoWdJJF4dHF5r3LrwOtbtUhTZLlW0+pAIhpZTqUD7XGmXlHlXr/YP+pZPKVTFLaWUvqCOB8zH7v0lZa4/oeYpJy3E08reu48pVbVMQXRxuNK3zqt21VGlahXIc74jZRlPI4vDL0sXtw/WHLbSlytV+PvKn0HHlsGlKq3Cp9qQN6CKKe6BCjFVG24zccRJjjJks63L2b7uVXxddH4lKfP2Pef+Gcva6qnumq0YyAq+hCPEGIzNH0qoj9+DfRfI5fgVH/qaCWtu1MTs0ktY4ppZspHn7LA4WXhfZpsrL3yz/EbYhHKsY67KYMP6q+D9NmsB4ufKIy9lQR15CXEqk3goRkb4tRO8KxYak0JTttUZ1ZFu1soIClFa2WTX6OuK/Ggoob8w2WKuVo8kXuXup31ki+lnSf5E7sIl3Y5U6rwLeHfqdThRamapt1NNNzG+y7bGpakeT8nVFP4+G/3hBNR1Jka5+p7gnb31ClYixwuj1Q8b9QgghdOkDuKYNqGqeSUXDx/gcC0FbQ1NHKPIgQug4DpuGVt1Eh/6AQ7wLnHKvJC9/Jyz30KVCdHkKOVoeG6g1yfb3vsuUmtbIwxZdPyXn4AqsK/ZyKvYLjU+x3vVVKve3R4LZ7pzOW7aVvNJ6FrIKqWlrolrTH9qIfUBDiCSkdgghhOihVdt5rGRmZArXrJF/Zx5a86/598AFIExn86ssc97Mhscqma2NA8xkzizl+Z338vayV2ke7MLt8Mc0vOqGnv2byZx5D+WlV+N8tYkTicl2HuK5ZW5yN6xhxWwtcjHLnEXF81sofXsjzzXHLDjXHubxx+YzI9MMjEPL/z+ZrX3AgX8/LYvBxYDJwEkIIUQKCQFcDQVU/WxQewqf+BW7G0kI6DqJwhovqqGCGXFXq7AetNXCrdMmxV/IMi3k5Aaob/goddiQTAs5ufR9IlUIA65Kv4kQQggRI/g0Rdc9neSDPG4d1Yy0sqloMpuSfKKNbkbEGCIDJyGEEANjc+B7O/EuUKz+XoQ9nBbi8O2gYsY1KT43Eh9KiIGRqTohhBAGGQzOOgjm7G/xgC1x+kwPetknXbMetPU4h4/9Pn6dUncLdQXZFDslmKUYGTJwEkIIYZCZLGsVL8w7yLK1W2kJRhaMd/v3sn7Vi1C7ikUp7/6kS3o68+xV5Gyq4RnPKcJAOPhLttV/gDVZullz+NELc3l72RNsaQlGBkndbbjXP85qlrJx0QzjF7jYF2N/3s0wvI5SXMFk4CSEEMI4g8FZB24cWuEadnkXE1r/N2SYTGRY6gg9vCtFunrQ1p1zOW3PiwSzHb+QfZOr8O5aSl7mAC5v5unMs5dyofJmMr5awZ4TozXVKC5HJj3oU98PTCZSfCTEJUvqrRAR0haEGBijbUbuOAkhhBBCGCQDJyGEEEIIg2TgJIQQQghhkAychBBCCCEMkoGTEEIIIYRBMnASQgghhDBIBk5CCCGEEAb1+646k8k0WvkQYthIvRUiQtqCEMOv34GTBE8TlxsJ+idEhLQFIQbG6A8NmaoTQgghhDBIBk5CCCGEEAbJwEkIIYQQwiAZOAkhhBBCGCQDJyGEEEIIg2TgJIQQQghhkAychBBCCCEMkoGTEEKIXp0e7BYLxc42wgDhNpzFFix2D50jvvMw3f4G6tbuwh8e8Z1d+Ub13I0d/QbAFEIIMcaZZ1LREKBiVHb2GS2Ox1j94Q+5b1T2d4Ub1XM3dsgdJyGEEEIIg2TgJIQQY9YFgq1u6spzMZlMmCzl1L39AadjN4mb7jmDx56PqcDO1rpyLCYTJstaPJ1hPa167AWWSFqmYuxOD/7uhDm3cJDW7XYKTCZMJhOW8mc54O8EzuCx303RplZorCQnIx+754yxfJtMmArsOD1+ug3tKzadmDxbyqk7EJ9GONjCdnuxvh8LBXYnnrg0wnT7PTh7tkmSl24/HmdvPlKWTQ+9nHvKVtfpwW4xxUy9deL3OGPKPCF/cecuTKdnLRbTIrZ6mmOOKZfyuoaEvHTiP/As5ZZoudXxhtNOgam/c2LkvAwkD5cuGTgJIcSYFKa79UUezH+VT+9/nS6lUP41TP/gADuCab7a/C8cmrgavzpPoLmK2Vlfcsq9krx732VKTSshpVBdPyXn4AqsK/ZyKno9DH+C+1Eb+dsyWO7rQKkOPHOPUW5di/tUFoU179BUnQc2B76Ql5rCSanzfe8+MpY2RvalzhN4/Frqi1bySutZA/u6AFyI5Dl/Jyz30KVCdHkKOVq+gBXuTwgD4VNuHs2rxDPlCQIhhVJtvJxzmMU9aQDdXl6pquZgzk8jZdiTl3Xs8Z8DztL6ykoWH/w6L3eFUEoRCqwio34Jy/f4GfxQIUx3q4OqxYfJebkNpRQq9G88nrGbouVv9LNG7HWWrG9kfOXrel42M/WtpVS94tUHNxc45V6LtfwYcz0dKBXCv2Yy+9dtojltfgycF0N5uMSpFPr5SIhLltRbISLSt4VPVVN1ntKqm1RH7J87mlS1pimb47gKKaVU6Lhy2DR9u8h30Naopo5Q6u/E/T1PVTd9qpRSKuRzKBu9/+/7XT19m0P54hIykO/QceWw3dSTh7T7ijuu+LQj+0+Vl/j9R/azUDl8f0qe3YR8GdNfOaPv+0/K51jYf1nFHWNIdTStUVpimSSWeUeTqtZmqQrXyZj8pvpu6nJJfvwG83CRGL1+yB0nIYQYizo/oqE+QG6OhczYv2dayMm9dgAJhen0vkt90MKt0ybFT2NkWsjJDVDf8BGdnOPEoXdoZDo518fsMauQmkCAhoqZBqdAJlFY4yVQk89pzz7cbjdu91bsRYVUNn6ub5N+X5z4FbsbSTj+SNqqoYIZ3R/RUN+Kdus0psQfFNfnTCdY/y7ezjBmyyzush5ineNNWjxvJkzjAeYpfOOumTSu+xl7Wzy4E6cTB20clm98E2vjSzj2/hKPu3mQU12R4+HoCdq7v9TP5c18e9bXYs6Hmczrs8ntNx0j58VIHi796ToZOAkhxBgUPn2SD9NNyQ1IK5uKJveubTGZMGXcTGXjsO4ECNPdtp1yy3XkFL3EkbNh4C8oftmNwwZHfYFhne4JbiriuthjMn2FnMrXezfInM2q/c3svOMPuNYvoSjnuoQ1TBPIW7UL3847OOuqYUFRDuOTrpUaKDOZeSvY76vjjrNvsn5BATnjM5Kv9RoVo3teLiYZOAkhxBhknjKNW7XhTHEhDt+fImttEv4FagrJGq7dhP3sWbGGA/NctIcaqKn4HiUl91No+Rzf0eEepGnYHMf19ToJ/wIbKczSL6GZMygseZSa9wKoUACv6y5OryvCur5ZX8SdxYzCEipqGiLrfrwvcM/pZymyPhO/+HvAzGTOsFJSUcN7ShEKeHHdc5p1RQ+yvt9F3CNgVM/LxSUDJyGEGIuybqG41NL3TkB3AN/RdFMrscxk5d9JqXacw8d+H7/YubuFuoJsPZjmNWTPuRsbH+Nrj9mj/uSXqdhpLOhldwDfUcj99tfRYq5g4a6z9A4V0u/rxPRv8UCfOyHn8DsXYTItwnn6ryPlc/i3BOPydZbWuu+mzq9ZI6/kYcpL8wh+eJLTfbYZh5Y3nyXl96IFT3Dy9IUkiehTVwNk1vIoWVJOqRbgw5NnBrHwPHouE8qNMN3tJzja31cNnZcrgwychBg0vZM12uGPRd1+DtQ9xXb/ucj/BxPJONyGszi7N5K1GCaTsP5oLfPqV7DceSwyeOhuw/1UDZsGeoMgaw4/emEuby97gi0twch56m7Dvf5xVrOUjYtmYAbM2cXY1/w5m9a/iCcYebIt2Lyb+sbbqd1Ywgxz7IDhHN3dXybZV2TA11i/m+ZgZNARDh5my9qf4IzJd9p9XTWdefYqcjbV8IznVOQpuuAv2Vb/AdbaVSyacUOkfN7+CWu3HNYHT5343ZtYtRo9vxD2Oyk2FWN3t/U8ct/tf5+GFqioKiKbSP0tsLt71yB1+3m3oRUqvk9x9jVJClQf+AX3s/2N36Y4N3r/U7AWd8+UXyf+d9+lhRKqiqcP7gKfNYcfvfBfqV+/WU83TLd/L0+t30a/1cLgebkSyMBJCDFCwnS2bKN89b8Tiv5Jj2Q8rFM3YtDMU+ezpbmO3IN/x3iTCdP4FRz5mwpqbQNZHA4wjqklG2neOZfT9jwyTCZM4xeyb3IV3l1Lycs0R3dI4YZteB/+nPWWqzGZrsay/nMe9m5lZd4E4Bqy5z3KmgvryMmYzoI9J5IMlidh/fuX2Db7VxRZrsZkMnH9j3/NDeUv4arOiz24NPsah1a4hl3exYTW/w0ZJhMZljpCD+9i18rZZPaUzxbmnn4SS4YJk+k6rPsmsrwnDTDPWMw2bxWT9y2MlKEpg/HWfUxe/iob5t+I2TyTh7ftZvnkfVjHZ0TWSells3/Dd5ma4ipsnvE9nm+sgHW5kXTv/RldC1bxmi06v3oNMx7egnf5RPZZr9PXX+n52/8Y86eOG+A5TDiXayfr6WYw46lPKFz+I2z9fs/gebkCmPRH8Pp+YDKR4iMhLlmjW2/P4XeWkbP7bnxvVzBDfoYkCNPpWcfMohNs8O2gYkayX9ZGkmnDOe+77H7gTd42/OSVkD5cDKew38k86wnsbRt613ZdYYy2mWE4+tGL4JqYRt/9jEQEV3FFSqwHifUk+qLTrQdo6bfe6sKf4K7MTTIFpUfKTYwAHPN52jqLkejFEA624o5GczblUl7nxl3332KiDxuNRkz6tpa2fKKDpqcJ8jqVOV+JpJ9kqi4+34Ntkwb7oQFHik4S0Vj6IXEl6/Rgt+RSHp2+RZ9ye+olLqycz+wrdNA0EEMsgdGL4BpN417PX1ITOI9SIbpe/joHF8dsc9EiuIrLSvcxnEsXsPhgtC6dJ1DzlxxcbOXeupaYC2CQxiV1uMY/wn6lUKF2dk59B1uVg9bEC5z5Bu586F6od/PuqdjFnp/hbWiE0jvJT9bhpK2zRqMXt7D5wQd4/tP7ae4KoUKNLM3Yx7LVewdePkbaWtrygazCDbQ1rUHTn7ZKOj3X3cLmB6vYl7GE1pCKa5PGowgbjVhsNFL0fLZRha8rhOr6J+YeXdUb/Vr6IXGly5rD3+//x97pW5OJjLzXCN3/as8U5pg3tAiaoxXBNRptNDE6q/53PbrqyERwFZeT9PU2WmeWKlf7+b5/j9afuAi9KbZJjNzbdUTVWhPqV0Lk5D65SVdnU0bUjW178e2gN/FIu+qNPmwkGrGxtmasfJKklSyScWJ+Ess1bbs11g+ptP2Mvt+4/MQewx8vq37IWB8uhIgy2maGeMdplCK4miO/2oNaNtOmxC5406OZBhtp8H52ESO4isuHXpdyb2eWlqQuJT6+HCfNNpm3cF/p7TTu/hUnIrdJI1F4sVGcPzF5iunqbKeR6MVn9GPK5vrMmI3M05jzwJy0JRLPWFtLzkgZxm+fVbiRQGADs0+/r/cfb+C03x8fYDAtYxGLw+n6GU5yaPchiCtHPY9qDxUzPpd+SAgxDFN1oxkpNPg0RddlxEWmzcippDH6+UWL4CouG+EznPww0M8Gg41/AnAN2cXfp+LoSziazxAZhBwkp791AWnrbETa6MXDLV1bGy7dx3CWf4PxOQ/y/JH/BMxMKN6E17FwAK9fkH5ICDF6hjZwGu1IoTYHvlCSCK4q5i3aFy2Cq7gsmCcx7VZLPxsked/WQJKf+h0eKiXybq7Oj2ion0rpfbf0vy4gbZ01GL14OBlpa0N2Dv+eJ6k8MBdX+0neq3mUkpISSgqn0uH72Hgy0g8JIUbR0Hrc0Yrg6r+W/GIb2tEPOBaMXXgbprv1WQpMi3BGA+zFHd1wRHAVV5aJqetS+wmOJk4pDyL92YseIqd+D7t3/4L63LuZkzTAXQqJdTbzFgPRi1MdUzftcQMQI9GI+ymf/traoOj5S5w2Df+Rs2fODyAZYxGLzdlp+pkTUyJTmwl3uiIBDi0UO/83t0k/JMSYN7SB06hFcL2WLGsVL8w7yLK1W2kJXiAazXT9qhehdhWLZlwzQhFcxZXFTNbsB9lwV0Jdaqtn+eJt5Oh1aSjpZ942j9LcX7BkyR5yH/gW2f20srR11jzJQPRic0z7+CfausP6NptZv6k1Zm9GohGbDbU1w2XR80b1MJ93f54wBaoP0hp3s635lP70bJCWLU+wzHnM4D4wHrHYnK6fmRAJvpizh/XPNEXKOnyK5m27abSuZuOimfwX6Ycumki55mMf8jvY9LAcEvFfDNZQV5eHAkfUtmqbAhSgtLJa9XrT+8qV+JRLKKC826qVVd8Oa7Xa5g3EPFVyXgW8O1S1VdPTsqnqbUdUIPaxky6fanLEpKGVqVqXN2ab8yrgdanaslk9+UncJhTwKldtmdKinzNLldW6lDcQ+4SVuFwZrbd96pK1WjmafKor+nnSp8YSnxBNePqrx3nV7lqa5OmrZNLXWaVCqsvXpBwJ7cwV134Sj0lT1uqXI9+Je0qsQ/kaN6syLaYdHmlUryU+bZaurRkqH6VUqF01rdHzbXMo3xcJT7Yl9gtamap9vUkdcVX3Pm1n4Ck0w/2QgX4mPi1NWat3xPcPl0k/ZLgtjDmpnlQVY53RNiORw8UV5dKot5GI4tbD3+fftpakfKXCaOUjZ102TVdwtF+R3KXRFi5FZ/DY76bowx9KxH8RZxQjhwshYkWmf75g5Q8LL+KgSQgjDERc16PEF9if69nOYt/JG/Z8TAV2tkajvkcj0oeDtLrrKLdEnzqMf2IwfMpNpSXZlFtkCi3VC6Djp+qi020v4mlJFwk+MaL+L/jgdOIauoSI8olPtna3UFdgwVLpjgRCBXrWGVqW9QaiFWOCdOtCDBf9gYcMSx2h5U/zA/0loEJcmoxGXAcI0lz/70xccxgV+j3NS77JRIDmf+HQxNX41XkCzVXMzuqkdfOj3LvvKyxtPR952jD0bzyesZsiPeJ+z5OnP38/ZhCCHrMMSotvMf4C6ManWO/6KpX72yN53zmdt2wxeU+MqK8O89j0E7y1I3YNnR5R/t53mVLTGimHrp+Sc3BFb8T4zHx+ULeaHOdPeGLvJ4QJ0936M1at/oSKF1YP4YW64rI01Lk+IS4lUm+FiEjfFgxGXE+6nq2/KPR9I+XHR1MPqS7vZmWNWwOYKop8YhrRtPvbv/5GipRpJqxxivuOSkgr9lj+oLy19yi0pcp1/H1Va9WUVuFS7bJO6oph9Pohd5yEEGJMMhZxfUCyCqkJeKmZ/RketzuSptNOUVzwVP3JU9sH7D50Un/SMs07HY3KtJCTGw058VnyiPpxYTn06P7BJPHbMi3k5AYiMdkAmEDeD56gNsfNgpu/w2pWs3/LfJmOH4PklAshxJg0EhHXO2lzVmIZn0PR80c4CzBhHi97X4uP5WeeTnHV3Rxdt4PmzrA+TTeFlYtuNz5Nl/bw0r0lIFYrm4omx0WDN2XcTGVjQgDVzFv5u+UlaGhY7/k2OZlyCR2LrrrYGRBCCHERxEZcj336s9OD/WgQbh1Mkm+worKFea6TbC25Uf9lHqbT08hRYpMcx9Q7Syilmgbv35PPu9TnltB82zCuC4y+JeBDIxsvxOHbQUWaGGXhU2/yxDI3f2X9K3yrn+SVgp2skrWMY44Ml4UQYiwyGHHduGj0/Zv59qyvxVxcvqTrbJLn5LJuZ9HKKdT//J/Z/dbBtMFiBy4aBT/xnYexEfXNKKq/FwAAIABJREFUZOXfSal2nMPHfh8foLW7hbqCbIqdbXpw1k/Y+8RPcOas5rl9O3mh4hNWr/oZrYbepyiuJDJwEkKIschoxHXDooOQQ9Rv+6Ue5f4CwZatrF32In2TnMBt95WQ61zBks1TeWDOtGG+IEWj4O9n8fL61BH1s+bwoxfm8vayJ9jSEowMkrrbcK9/nNUsZeOiGZi5wKm9tSxz3kht3SPkZf0V85/8Ryp8tax6xTu8L5EWlzwZOAkhxJg0Cevfv8S22b+iyHI1JpOJ63/8a24ofwlXdd7gksyaw9/vr2H2r8uxZJgwmfL48fuTKN/7OtVa383N2UVUVcwC2wDf6WiU+UZKtrjYnuuhcHwGJtNMqo7czPLa+TEbjWNqyUaad87ltD2PDJMJ0/iF7JtchXfXUvIyzT1TdDm1T/SEGTFP/S5PvlASmbKLC90grnQSOVxcUaTeChFxWbSFcBvOeYs5XOWKWRMlxMUhkcOFEEJcwvSXvV94iB/abpCLkbhsSF0VQggxqiKvT7kay/rzLH+1kjx5rF9cRmSqTlxRpN4KESFtQYiBkak6IYQQQohhJgMnIYQQQgiDZOAkhBBCCGFQv69cMZlMo5UPIYaN1FshIqQtCDH8+h04ycJCcbmRBbFCREhbEGJgjP7QkKk6IYQQQgiDZOAkhBBCCGGQDJyEEEIIIQySgZMQQgghhEEycBJCCCGEMEgGTkIIIYQQBsnASQghhBDCIBk4CXFZC9Ptb6Bu7S784eFIy429wILJZMJU7BxkmufwOxcN4fsXy3CWpTCsuw23vThS50yLcLb9BmdxNsXONkbzNIT9TopN+dg9Z0Zxr6Mk3GagTC/Xdjv6ZOAkxGXtM1ocj7G69dzQkwr72bN8GfXTX6A9pFANFcwYUz3EMJalMOgc/j1PsKD+r3G1n0epPVTM+MpFyYl5RgUNyktN4aSLsn9x+RhT3aIQwoBJExgvPYMYVVlMGN/viyyEuGRI9yjGpHCwFXddORaTSZ8iKMbu9ODv1u9Rd3qwWywU171BQ892Fgrs9bQGz+A/8Czllsh3LeUv0hK8EJN6J36Ps3fKKzFtzuCx52OyrMXTGXNPvNOD3WLCYvfQSZhOz1ospkVs9TSzvWcqI5fyugY9rTN47HdTtKkVGivJyehvmiFMt9+DsycdCwV2Jx5/Z+RTv5PijJupbAwS3FTEdf1OWVwg2Frfz/EN5jt6mRQ/g7uht2xNBXa2t56i099AXXlu5G+Wcp5tCcZPOYSDtG63U2Dq/Z7T46c7rmwtFG89QEvMdpbyZzng7+ynLBPLLUnal7X+60Xq6Ru9fvbUYYPnt8DO1rh29xVyKl+H4NMUXZeh1/0kuv14nCnOb7gNZ7El4bvJ2lhinhNKInGqLnGfhuq5gb4lyTnobesHeDZaz03F2Le3EIx+Ta/DBfbnetpCzzH3Vz6xPjvGgZ68xfYlqYxwu+1beLS663rT6VMfkxyrwfMyrFQK/XwkxCXLUL3tOqJqrXmqbPMhFQhF/hQKNKo11puUtfaI6lJKqY4mVa2hwKaqXcdVV882moJZvd/tOqocZbOUVuFS7SGllOpQxx0VStPK1OYjARVSSoUCh9TmslkK62bl7QoppT5VTdV5Cm2NauoI9eZL36dW3aQ6VEh1NK1RGiisa5TL15E8n9G0bA7li0kqXkh1Hd+myrSYfIcC6sjmMqVxj6r1/kHf7Lhy2DR9/6mcV+2upXHH17cM/qR8joUxeTLyHf040JS12qV8XSGlQu2qaY1NAUore0EdCZyPKd+lytV+Xs/3SeWqmBWzTe/xVrhORvaX5Hz2pJ94XmLLMlpXHEf18j6vAk3/oKwsVA7fn1KW0qUgfVswVi9CPoey9TneSFlF6spAzu8svSzPq4DvpOqK1pXYthA6rhy2m5TNcTw+rZ7ze14FjrygyjRNbwdJ0ug537H5js1zktLwOZSNPFXd9KlS6g/KW3uP0sq2qeNdkTSjba8nX8kY6VuSnIeett5ThiHV5XOp6mR9klahHMc7lAr9XvlOdBgoH9XTtuP7ruPKVR1b/0e53fYRLfPod1SSNjrI82KQ0XGPDJzEFcXIxaKjaY3SEgctiZ1G3CAmKtkgJaHT7mhS1bEX7KiOJlWtaXrjHsjAKdqRp8qDkYGTfsHo6exSpGVk4BR3HIl/j+Y1WVmm+06yMomWQfxFO/4Cl3ybPuc56flM/G7fskw+aLg8pG8LA6kXCRf+2HM36POrVJ/2o1TCwCl6HhMvuPHnLvE8hXwOZdPmq7IH83rzFZefvuLqVeLgzRCDfUuq75HYbwygDqcpn562nXiuL1q7TSLF+Yk7t4M6L8YZHffIVJ0YY8xkFW4kENjA7NPv43a7cbvfwGm/PzJlMCRhOr3vUh+8mW/P+lr8PHimhZxcOOoLDHGKJ5Prc6bD0RO0G7013fkRDfUBcr/9dbT4TA0wrejxWbh12qQkxxegvuGjhKmWwXxnID7D29BIUMtm2pRxMX83k3l9NrnBRhq8n6X4rr4NH+NrT35WzJZZ3GU9xDrHm7R43oyfMrjcGa0X5ukUV92Nb/NeWvRpuVPvuqnPeYhFsyeMzvnNvZ1ZWpLzq587c/a3eMD2AbsPnSTMOU4ceoejpZUsLZqutzm9HmKjOH9i+t2ap/CNu2bSuO5n7G3x4DY0NTvUviWx3zBSh42VT8S1fc/1RWu3SWQVUhPwUjP7Mzxud6T8nHaKcipp7DmswZyX4ScDJzH2dB/DWf4Nxuc8yPNH/hMwM6F4E17HwoRBhEZuzv/f3vtHN3Xe+Z8vyT0kbYxhWZhBgjQs5WuRHpwmscdpG6b4RyLH25IwpkBLcJzaG9wOOLQMWMUh9Ez4kRg8cJxAfjFWISJsIbEWxskkNli1M6HZeGwmjdlg6XjzJTNGohMmBaFJgjfS3T90ZUuyJF/ZMhj4vM7JOcG6z3M/z+/PfX68HyPpmiPu59yZXjwJnvB8cIZzV/iob+DcGT5IaFQvZ871J3ggmi62F04b3POj06FT90eNOkzWHGamj6BbUvfIhMefFt7hjpT0XNY1tXHgnj/TuHklhaZJV2dPxRigvV5MYMZ9JZSiDuCBj2l+6W2ySou5a6CsRlIntBh5njMfuBM84OaDM+cJ6Gcxf9ndtBz6A70BH33Ozykt+i458x8gy3acTu+XnDvTC6X3kZOhpX5NJnvdQZwH7uFCYy2LC01MjLXfJhrNfUsyqGmM9ZPW/BnBW4OMcbuNwEuPtQLjRBOFz73PBYDJxbzQ+TLmAQdwhOWSYsRxEm4wvsR1+Ckqji2gse8Mv699jJKSEkoKZnDR+fEo457A9FlzMCR4wnDnLKZf4Vannz6LOxMaFT1bMxxLaHB+gRJc6o/4z11bQEbKwiSBuQGnf2jcSiqOl6dnUlDyGLW/d6P43XQ23s+5jYXkbW5L7Rf3FSapepFxB0WlYGv+kAu9f+BQy90smz8rbAAZo/LVT2XWncYED4RmRG5mzvwHMHf30nf232i2fQPTzPRgGunljNvJu4fOUlp0RxK2ZJBZUEJ5bTOKchl3525+eG4XhXnPxNxcPnZ9S4xZnxCa82ekjHG7DSPgep01FR0UN57B//tayktKKCn5AcaL/5PuiCeTLZfUI46TcIPho8/5MURPbQf+mwvnL48ybj0ZOfdRajjNiVN/ivzK87lxdqPOYKlLIVeKjDsoKjXSfeKjwRM6QaPUvND6tZgofR3U5ccS2BtJmGSYQk6RGUP3SU5FnGwM4OvaRb5uKVZXCnWZ9AaySx6lrDT7qsweppSk6kUwn7HZ+Ud7Ey3mB5g/52auavn29dLNbEwzg3PCQSepiYaNz2PLUu3LuIOi0rPYnq7D1r1A2zJdTCZgyF7EyrKFGOLO0I62b4leMlbTaEi0vKg9f+DzoVsFfG6c3cYYDuVYl2s0IXujlyu/4tKFRJ8nWsol9YjjJNxgqB1NyyH2tZ0NNvyAh476Tay2nhp99Bk5/GxLLm+t3kR96Oit7xTWqjVsN61n29LMwa9jTxP7X/8o2JH5erBvrWV70isb4U7Yl/h8X8V4Zgq5P6vi/rd+Q039CXWQ9NJjtbBi+3R2bCvRLnSZMZ/Hdy+ISl8P9s1Psp5VavpSEEYzejLyKtld3M7qmr2qLEQAn+sIm9ftgR3rWJp5s8a4huZl8Ih6ERZ7jzrgBPC53qG5A8orC5lzTfegydQLPRm5i1hrsrN+w/uYl31/MO1jXb65y9lyf1T59tioWrEPU3j5ZtxBUelNHHzlCAzM7KYz0zSDtlcO4tS8TMeA0na+xT64JOtzcby5C8p/QtGcWHVqtH1LF9s378Tu8oalsYni3ZXkxbU7ifwBPNtr2Rqqy2q/ZCuu4fG8GLOyY1quMdKRcx+lhnex7fsXtS724+nYS83qPYPbH0ZULqnnmm72gpA8ejLyqmjadyfvFc4kTadDN/PXvHNrGUcaqxMus2kjg7nlu2g7sIBzluxg/BP/DueCepxNa8hWv+D1mT/muZZy2JjFRJ0O3cLfcmnxOl42J2vBzcwpfowN/Rsxpc1m8eHeGF+BetLnlrKnrZ4F557CmKZDp5vLL5z3csB5kHXZk5N43wRmlGyLSt8Sjk6rpPPgqoH0jT5MEuhvo6S+kQML/h2L8SZ0ujQm5h1lWtUhDq7NTWKP2tC8JHMF+zormXZ0SbCcBuJ+iS2LbrvGO9Ak60X6HTxYOh8MJVQWzQ5L+xiXb/o8yvdEle8vPmLBgTaa1oWXr+q4kB02g6J+pCS7X1E/l0f3HaJq2lHyJqp759Q0NW35ETNir5uNsm8xU116Ox9vvTeYxgIHWfsbqS8Zpp5pzp9vYN7xGAUfP02mTodu4k9pz6qjrX5RnPSMcblGkzGfXzbVkvtemVoXs/n1O1MpO/Ia1aHMG1G5pB6degRv6A86HXF+EoRxi9RbQQgibeFaIYDXsZG5hb1scb5CueYZUiHVaG0z1/YHkyAIgiAIwhVEHCdBEARBEASNyFKdcF0h9VYQgkhbEITkkKU6QRAEQRCEFCOOkyAIgiAIgkbEcRIEQRAEQdCIOE6CIAiCIAgauT4cJ18PdkuRegnhUl62P0tRqq9aEIQhfInLuhRdkRXXSO8eUJVwQ9cXBJWqc7A4zo/eLmONendTAJ+rmbqagyO3c9BgvI4ajAnbVwry5YYgleUyQnwujtVtZf9AWUbXnfFDatrGSPHisteQr9Oh0xkpevkwLxcZMVocY39fodeBxWhM8RUn4xkvrmPPUrM/FemNKrcU5eF14Dh9ievwJhbb/geNfZdRlMP8H/O0awULwnhCn1lO86gvpr2ZzPLDKO5tFGTogc/oaHiC9V3yITG+uNrlEsDbsY+y9X/Ef5UsSIbUtI2REXC9TtXiJmY3nsGvuGleuZSVze6UX3QrAN5OGsqeoSsFlXJIuZXPTYnTcx04TiEymDzxa1fbCEEQBOG65CamTr7leho0bxBSX24piCuAz+XAOrBUZiTfYsXhUicwo5YiBjmPw5ITNtXZj6fLhiXfqMZThMXqGLzIT31el29hb10ZRp1Ofe7rmCpeA8/TFE5Kw2hxcHFENkZPu6rvi5iyVpcpxuE0tpAkPhcOq0WdwtWhy7dgdbgGbw4PTY/vPUbH/sHnjGW7OOaKMTkf+AR7RVaMqfvk6kzkcoRaB4uewd68izLjoK37u87idTVTV5YV/JuxjF2hizgjllv+E4flAQq3d0FLBaa0sKWOgIeu/QnyIPSMvW7g3caynbx18qzGTP5PTh3bFRZ2MO8CZ+1UGGMtu0T3CzHyyNOFPaIPiOorQmVX9zrNA88ZybfY6PKcxxVh0x71YtQQXlwO6/D9UHR5eh1YjDrV7sHlzL2ONvYP9DtZlNU1q3Gdj18uV4TQNR9P4+E1Kkxfj8rzs5x8a+dgnTOWUXcsRt1IVH/Ucsi3PDtQTwfeoaXuRVscvVQX3YaHlFUs+vF02QfbzbDvDralNFMFLXSxvXBasOz//FHYmHGBrrofoTOuxn42VJcC+Lp2ka/LosL+idouhxvjYthnLKPurZOcS5AibekK1cm/oc4e/lwRlv0deLwujg20lSzKdoUufQ5xhdqF14FlbiHbPR5aKm4nLWG/mWhcj1NuKRq3R+k4BW9hXpW3hvbpm3D7FRR/F7XT21lhWk5d1wXQz2L+srtpOfQHesNt9n5Isw31MsZ+ztrXkr3wONNru/ArCsqlf8DUvoa8NUc4Gx6u7Z95d8p6XMpl3M4zXFK+wNmwBAwbaL3ox11bwKQR2Tgfj+04naGM9X5Is60LPL2cORdqDJ/R2dwCydyyLYw/fKewrlrMivZvUuu+jKJcxl37TdpX5LGwriOsA/XQsrKOxok/o0lRUPx9HJjxNubKBrqiO2f9rdz38EKw2Tl+NnwgTkGdaXmW5xy38YTLj+Lvo/V7H/Bozkzmbu3lB890oSgXOb3la+xYtJUjEe8GmEpB7du0VmeDuQGnX13qCHyC/TEzCx2hPPBz6YVv075iMWsGOvoLdO18jJznPuOhtosoih/XE7M5+eaxwdvKE9q9gdUHJ7Cq6zKKcpG2hz5lm9rm9DN+wMOlYHv1ncj2HdEvxMDXwc7llRxNW0mXX0FRFPzudaTZVlL5Ymdk2a3fi2P2BlyKgt+9n+91WMgx5rP1VC7P9Ckol7rZwoss2viGaoOXHuuvyFvRPtAP+d2bmN6+BtPC+qFlPiyvsXJzCxMrXlPt3MmMN1epdsYplyuGnoyCLfS0bsDAEhqcX0QuO3mO8ebJ2cE6p1zGfWA2b5rX8mLXheDvmuoPgIc22x+ZsuEEiv9PtK3MIUNz2ERcoOvFtaxo/zYvXPJH1IOqw644cQTwde1h+cKjpK1qCY4zymXcT34DW2FY2iIILnv7nQ2Yyaa69dPgEvik8LY8meyfb2KHyc7qTWpd8nXy4rodOMv/nqcW3YZe0xin2pfzEp8+9BqXFAXFtYHZJ4/xSsIGl0y6jrD+uU5mP3Ei+Ezr9+l49B6Mc7dy6gfP0Kf4uXR6Hez4ORuPhMriCraLjAJqe1qpNhgwN5zGP7DdIEaaE47rX8Yut1SN20ocEvwUxqdKa3W2YihvVPr8Q/+OuUFx+hXF72xQzPxQ2dH5Z/V3v3KxdYNiMGxQWi/6FeViq1JtMCjmhtNKRDQXW5VqQ7ZS3frpYJyhMAN8oTgblkT8Pfi+JUqD84skbQyFUf9tWKQ8sjx70K4Ie4TxyPD1NlT3VimNfZeH/j1UBy62KtUGFEN1q3Ix3jOhuqfWIeXS+8qOvG9F1uPh6oz/tNJgHgwTrIeJ6ny0DepfI8JFt4nIuh4vjtjtMtr2eGHDCb0/Oo/VtljdqlxU/Mqlzp1KXkQ8Ue8fmllxfo8qh5hlF50H4XaGp3eeUt54JkY/FOqf4vRDEe8M5VF03kXbEMum1KG5LUSUwdD+NPhoeD1Npv4M14bihI1lbXgdj2o32givf+ERDx9XZPsKhTGExRWqz/OU8saTyvs7fhhZ/5MY44bYFy9sUumKUydjxR2dtivdLoZNb1iaNY3r2sdsbX6PoozO/fJ+SLPNTda938YQEVM6M02zobuXPl8A/ZxCKss/Yefhk+o07X9w/NUWTGsXkZsB3s7j2DxG7pw1NXIKLN2IKcuNrfnDkZ9c0Gzj91lmPsmhd88Q4Et6332b7tIKVhXOptvpxkcgaCdminKmjNQa4aoTnAHyZN3NPMOEsL/rSZ85hyw+xtkXb8FgmGfS7+DB0vDZ1fFaZ9Q8MMxh1vQYeeBpobnzvNouZ2OamT70GS2vGZLHwTYXnNmF9LuKKR1oc4N2xZ+d05NRsA23ewu5597Bbrdjt7+O1fJQcLl+VKhl5bmde+f9ZYx+CLUfGA2Rfc61x+dqHmipP5/FiWM0YcMfn8537p9Ly8bfcqTDgX2YZb4gUymo7cRdm8M5x1G1/uzFUlhARcvnw78zsUGkZ/+Muh23YV18N/eshx1NWyiZMYHBujXMGBcaq0xG0oc8842rlK5x2i40jutjxagcp8C5M3yQaAoxtMw1sIwRXAoL9LbyknU2pQ/eEVZB1HXIgfVqHbq026lo0bQokAIbw5cUffQ5P6e06LvkzH+ALNtxOr1fcu5MryzTXesEznPmA3eCB9x8cOb8CI+s3sycop9Q3v08DW3nCQ4S7eoHwijqTNYcZqaPQZ1T9wWGt7ngngCAy5w706ttSW6k6GdTVPkA3Rtfoc0bUDvD6axdenf8k0q+U1jLvsNE03Kee/+/AD2Ti7bT2bAkqrM0DB2AEtI/bHo9H5zh3LXo74wVCevPGIYFYDLZ6w7iPHAPFxprWVxoYmL03tUhBPD17KfMOAlT4fO8fyEA/AVFL9hpMKfCAZjMXT8tp9wA5BWSb4quxYnHOP9wY1VckklX9IfQcIzPdqF5XB8jRtUb66fP4k5DggcGvir0ZOTcRyktNHeepffdt2kxP8D8OTeHPRxcZ1cUZch/oznyqd3Gm5kz/wHM3b30nf03mm3fwDQzPRieXs64nbx76Gz8vRfCtYF+KrPuNCZ4IMZXYTLRh/buDHxBzoj6QBhHmBtw+oe2N0XppLZgBtNnzSFR0xk9E5hxX4naL6gzXFklPHjX5DjPf4nr8FNUHFtAY98Zfl/7GCUlJZQUzOCi8+NR2zJceg13zmK6fDMNkrD+DLNfazRhB8ggs6CE8trm4H6dzt388NwuCvOeib0JOODi8JoNHCtupM/fTG35jykpeYgC4+c4u1PwiRD4hCObfoP1f8sjz7mDdRF77mC4Me5/GW6sivvesUzX+GwX2sf1MXr/qEJn3EFRqZHuEx9F7cD30ef8OPJLOeMOikrB9uar2A+dxLzs+8zRB03IyLmPUsNpTpz6U+SXvq+DuvxYJ/LGxsagk9REw8bnsWWpjl3GHRSVnsX2dB227gXjbMlFSJ4p5BSZMXSf5FTEaaoAvr5eupP+Ihsaf+7ShzHZDnPo0D8N1qNxRYI86NpFvm4pVle/2i6jlyVD+aSBIdPlwTZnCJ+1zbibpWunY3v1dxx6s52sgX4hFqE2G7UEGPhvLpy/rMWiBCTqh9w4u1FnsNSlgBsaLfUnnjbVaMImYgKG7EWsLFuIId5sQ6gco5Z3ApcuMPrzjP2cPbKD1dbb2PHsIQ7sLsG5/il1Y7bGMS40VkXPfPncOLsTLLmNabrGabtIxvcYA0YZ8xRyf1bF/W/9hpr60PFFLz1WCyu2T2fHthIy9WHPLn0Y084NbGi5m2XzZw2+PGM+j+9ewFurN1EfOlLt68G++UnWs4ptSzNHYWgSNmbcQVHpTRx85QgMeNHpzDTNoO2Vgzhlme46QE9G7nK23N/O6pq96lH04AmNqhX7MO1Yx9LM0Tg6+uDenax/YuXKw8M4AleK8E7tS3y+ABl5lewujsoD1xE2r9sDoTzImM/ju7+LbYUFa4934Jmtm/dpW8Lz7GPz1iPqkWW1zdm+y+7H54fN2k7mrgdLyLKuYeXOGZH9whDUQbflEPvazgb7iYCHjvpNrLaeGmnmDJKRw8+25Eb1Q6ewVq1hu2m92g+pM9OeJva//lFwgPP1YN9ay/akP+6jy+Wr0achKcL3qwX43Pe5xg9Uvbb6k/KwYagyN/kW++CReJ+L481dUP4TimJ9rKiDbYvtEG2q0xbwnKC+5jdYRzkxEzj7BptW2zHt2MTPs/+CGYvWs7v8E9av+23w1JmmMW4qeY/XUGxbQ5X1lPa6NYbpCsZ/hdtF+J6uz33E3qqUjO+RekYZtZ70uaXsaatnwbmnMKbp0Onm8gvnvRxwHmRd9uTIZ+8qptRswDCkYk9gRsk22g4s4JwlmzSdDt3EJRydVknnwVVkj8pzTMZGtWMmO2xJTq0QSe+ZEMYt6fMo39PIgQX/jsV4EzpdGhN/8RELDrTRtC539GWsn01RZQkG5g/jCFwpbmZO8WNs6N+IKW02iw/3EtDfRkl9VB7kHWVa1SEOrg3lgdou98+jvWBS8JnKTv6q6nHMWl5rfpyqgk/YmpmGTjeJgvZ57G/bpm6YHSR4eGQeDFm+j0ZPRl4VTfvu5L3CmcF+YuaveefWMo40VqdgWTGDueW7ovqhv8O5oB5n05qBfkif+WOeaymHjVlM1OnQLfwtlxav42VzshbEKJdRpyE59HOKsGy4SIXpFm5Z/LtIyZiEAbXUnzEIOxDHXB7dd4iqaUfJm6julVLHjKYtP2JGzEY3lbxfPs++3D9QaLwJnU7HzF+/x61lz9NYna0x4TEILdGZ1lP385yg/frbWPTU31M+sGSnbYzTz1hEfVsdWe0/DdatiWt4/6/K2WFOvDl8TNI1wBVuF/rZFFtK6a+4nbRbyjncG2sGMplxPfXo1CN4Q3/Q6YjzkyCMW8ZHvf0Sl/UR8k78hH/dWxKnExcGCPRgLV7BicpG9pbcNg4czeuD8dEWBOHaQWubkT5KEFJMwPMv7LP9f6z92wJxmoalH0/bIWz9D/O35lulQxIEYdwj/ZQgpAr16p40Yx3+qqf5+RhPF1/rBK/QuAnj5stUvVQxyiV5QRCEK4Ms1QnXFVJvBSGItAVBSA5ZqhMEQRAEQUgx4jgJgiAIgiBoRBwnQRAEQRAEjYjjJAgxCeBz2bHkG4MaMUVWXHJP2VUnuKE8B4vjPEHZh6VJlo22MMH3jFTF+jpDFZsc1Q0OVwUvrmPPUrN/7O2OrC8jqZdXkvM4LDnj2L7xjzhOghCLgIvDVauxzd5Nn19BaS4fUyVaQRv6zHKak7rPTLhh8XbSUPYMXf6rbYhwvSFDgSAkYupkJkorEQRBEFRkSBBuTHwuHFYL+TpdcCku34LV4cKHOu2edjsVLR482wuZNLA0FE1oyvsZ7M27KDMOxrW/6yxeVzN1ZVnBvxnL2BW650kl4Olgv6Uo+LvOSL7FisN6i839AAAgAElEQVTlDf2K11GDUfc31Nntg/HoirDs78DjdXGsrgyjTodOl0XZrhORl10mSB8AXgcWo5F8y7ODcd+bT74xVlqD6TRaHHiJTcDThX3AHtVOq0O9Ryze0oWaRmONept9P56u8LQOtTtyqS6mIXTZ6wbLYki+hvhPTh0bLDNj2S6ODXkmOupE5XXtMpJ0DRtGrV9Fda/TPFAvjORbbHR5zuOKyPs96p11IfrxdNkGl8kj6hIMtrs9ODrCnjOWUXdMrSteB5a5hWz3eGipuJ20UB2LbhdD4o7F8PVyeLy4HNY4aVLbx0A7gMH2H1XXvQ4sYW1US9lFts0syur+iZPnRnsp9o2NOE7CjYfvFNZVi1nR/k1q3ZdRlMu4a79J+4o8FtZ18HlmOc3+0zSYDRiqW7k43NJQy7M857iNJ1x+FH8frd/7gEdzZjJ3ay8/eKYLRbnI6S1fY8eirRw5q17CedbOY9kVOKZvwu1XUJQeXjCdYEVeDfaz4YPIEdY/18nsJ04E7Wz9Ph2P3oNx7lZO/eAZ+hQ/l06vgx0/Z+ORTwYv4EyQvsHO3kOb7Y9M2XACxf8nnK/8I0+Wgu3VdzgbPo54P6TZRtj9jdH52cHO5ZUcTVtJl19BURT87nWk2VZS+WInvtB9jy1v827EvVOf0dncAqX3kZMBvq49LF94lLRVLfgVJWj3k9/AVrhWvWV+OC7QtfMxFh79Oqu6LqMoCor/X3ky7RCFlQ3By1YHymwDqw9OUJ+7SNtDn7LNtJy6OO/RXl7XFiNJl/YwHlrW78UxewMuRcHv3s/3OizkGPPZeiqXZ/oUlEvdbOFFFm18Q61z/Zy1ryV74XGm13YF68Glf8DUvoa8NUci62XLVjY33kJFU1+wrhyYzZtmta5kFFDb00q1wYC54TR+9zYKMrx0vbiWFe3f5oVL/oh6WnXYFWcfVCAF9dJLj/VX5K1oH0iT372J6e1rMC2sp8s3Qb0gt4Xmzs/UMMG24cHNB2fOq7YF8HYex4aZopwp2srB18HO5ct47tOHaLvkR1FO8MTsXt58JQWXYt/IKHFI8JMgjFuGr7d+5WLrBsVgWKU09l0e+neWKA3OLxTFf1ppMBsUQ3WrcjFuXJ8qrdXZCoYNSutFf+x4Qn91NihmspXq1k8Hw5kbFKd/aHzBd4biCYVRudiqVBsMirnhtDIQNMJWjem72KpUG4hKn1+51LlTyYuwPRRfeBpj5Wf0718ozoYlg2n0n1YazN9S8na8r1yKSEtkngzJbzVcKL2R+Rj1joj4wqJwNijmgTSpYYbkT+T7I8NoKa/xx/BtQWO6IspAY5iY9StW2FB5qPUnVv0eiC+q/UTXueiw0f+OqkvaSKZeRtWxiHo5TylvPBMjTap90bb5TysN5mxl+SOLFMNAfoXbkkQfMqRtxgsraPV7ZMZJuMFQv+Sy7maeYULY3/Wkz5xDFh/j7NM+AT8ivB/SbOvCcOcspke0wHRmmmbjsR2n0zvS4y6jSZ+e9LuKKTWf5NC7Z9Sv3PBZoVjdhZ6Mgm243VvIPfcOdrsdu/11rJaHMFW8FvbYbIoqH8C58wgd6rLc2eN2bKaHWZo7BZhKQW0n7toczjmOqvHsxVJYQEXL59qSnlFArbuT2tzPcNjtwTisFgpNFbREPzskfxLk/ZiW11VkJOka07xQZ1Q8Ru6cNTVyOSTdiCnLja35w7jLxcFnoNvpjr2Epp/Od+6fS8vG33Kkw4Fd01LbaOtlKE23c++8v4yRJtVe/SzmL7ublkN/oDcAgd4/cKjbzKOr/neyunvp8wUiZ341lcN5tS+Yw8yI64yCzwgjRxwn4cYicJ4zH7gTPBA+Na6RIR2TNoL7p3SD+yZ0X490NgCYjWlmuvZIR5s+1cHp3vgKbd5QZz2dtUvvjr1MB8GlwbLvMNG0nOfe/y9Az+Si7XQ2LIFQp88EZtxXQinqckTgY5pfepus0mLuStcDAXw9+ykzTsJU+DzvXwgAf0HRC3YazAkGwwi89FgrME40Ufjc+1wAmFzMC50vY06BQ6ytvK49RpIubWEMZJmMJFF7VbrYXjgtLG4dOnXP4eiYTPa6gzgP3MOFxloWF5qYOOyertHWy37OneklkeWeD85wLhC+nP05vr5e/t/S+7gn569ZltVOc+dnBM6d4QN1mW4gbKJy8A/XFwgj5WtX2wBBuKLopzLrTiN8EO+B0NdunM3HKcOAucHBW+Vz43y9BOJ/WSdCc/ridaghB6ea5s5fksNxbFkltN0V78LiL3EdfoqKYwto7NtJyYzQLM55HJaPgTmDj2bcQVEprGj+kA0zz3Co5W6WPTcrmP6Ai8NrNnCsuJG+vSXMCGWK14Gl2wN3Dp/0gOt11lR0UNx4hr0lt6n5GsDraKEbTVEkYLjyulbRkK4hXraGMKPaM7+EBucrlGfeHOf30bTNDDILSsgsKKG8th9P1xu8+uxvKMzrpbVnCwXRs6qjrpcTmD5rDgZ64z4RmjXSz/k+y8wNOPvO0NnczrdMj5Cun8qsO+HQmf/AdeZtukt/Hpz59cLw5XAeR8K+QBgp11cfIAjDMoWcIjOG7pOcijjJE8DX10t3sjM8IyHjDopKjXSf+CjyJBwX6Kr70SiF6VKQvoy7Wbp2OrZXf8ehN9vJWvZ95sTtKXz0OT8euvQV+G8unI8+uRO0DZudf7Q30WJ+gPlz1MHR58bZDVn3fhtD2LsCly5oHCZD6YteEvmKSxdijOIDM2GR6TDEWpIc0/K6iowkXWOaF3oycu6j1HCaE6f+FOmv+Tqoy0+1COcEDNmLWFm2EIOnlzPnYmyGH3W9TJQmNe7QrJx+KrPuvIytoZYG2wyWzZ+FninkFC2g21bH07azgwc0NJVDqC+IXdeFkSOOk3CDoScjdzlb7m9ndc1e9Rh0AF+PjaoV+zDtWMfSuF+6qWIqeY/XUPzWb6ipD8kIeHHZt7NuPezYVjIKsc1UpG8ydz1YQpZ1DSt3hjrweKidc8sh9rWdDQ4MAQ8d9ZtYbY0+uaMnI3cRa0121m94H3O4Q6YOBC22Q7SpDl/Ac4L6mt9g1bRCExqg3sW271/UPO3H07GXmtV7hi6VePaxeesR9Ti4lx6rhRW277L78fkxliTHsryuJiNJ1xjnRcZ8Ht+9gLdWb6I+JN/h68G++UnWs4ptSzO1D1rpRkxZ3wj+/+c+fF8FFdDzLfZB+QGfi+PNXVD+E4rmxGgXo66XQEYOP9uSG5WmU1ir1rDdtD4sTeqHxcFXOMgcZk2fwMDexLaDvOJcELZMp6Uc9GTkVbK7uIkVVTZ61Lrusu9k8/YurbkoxOCabO6CMCrS51G+p5EDC/4di/EmdLo0Jv7iIxYcaKNpXe4I9mQkj37GIurb6llw7imMaTp0uknkHZ1CVede1mbHWxbTSArSp59TSGX5PAifFYr9JBl5VTTtu5P3CmeSptOhm/lr3rm1jCON1RiG2HYHD5bOB0MJlUWzwzqgqeT98nn25f6BQuNN6HQ6Zv76PW4te57G6mxt6c6Yzy+basl9r0zN02x+/c5Uyo68RnW0IebHqSr4hK2Zaeh0kyhon8f+tm1hS41RqRzL8rqKjCRdY5sXE5hRso22Aws4Z8kO1qeJSzg6rZLOg6vITmYvoX42xZZS+ituJ+2Wcg5/PItH9x2iatpR8iamBfcEqXE3bfnR4DJcBCmol2Qwt3xXVJr+DueCepxNa8LSFHL+iZj5DC7hGYbspdRUDvrbKKlvZH+Wg4KJaeh0c6l8/3aqdizSno/CEHTqEbyhP+h0xPlJEMYtUm9TRKAHa/EKTlQ2hu0XEq4lpC0IQnJobTPSHwqCEEU/nrZD2Pof5m/Nt0onIQiCEIb0iYIgDBC80uQmjJsvU/VSRXJLI4IgCDcAslQnXFdIvRWEINIWBCE5ZKlOEARBEAQhxYjjJAiCIAiCoBFxnARBEARBEDQijpMgCIIgCIJGEt5Vp9PprpQdgpAypN4KQhBpC4KQehI6TnIiQ7jWkJNEghBE2oIgJIfWDw1ZqhMEQRAEQdCIOE6CIAiCIAgaEcdJEARBEARBI+I4CYIgCIIgaEQcJ0EQBEEQBI2I4yQIgiAIgqARcZwEQRAEQRA0Io6TIAiCcAPyJS7rUnTGGhzewNU2RsWLy15Dvk6HTmekyPoRXlczdTUHcY0XEwV0ShyFNBFPE65FpN4KQhBpC9ceAZeVYtMuZja+wd6S29BzHoflAQo/+Fucb5WTKVMdY4rWNiPFIAiCIAjjhpuYOvkWGZzHMVI2giAINyTncVhy0OVb2FtXhlGnC1u26sfTZcOSb0Sn06HTFWGxOnD5IteLAp4u7KGwsZ7zOrAYjeRbnqWuLAudTofRcoz/cNRg1BVh2fsMZUYdOl0OFsd59b32gWd1Oh26fAtWhwtf8I14HTUYdUvZ62hjv6VIfS6LsrrmSPt8Lo4N2JZFWZ0Nq6UoLI1RS3WqrUV7j9Gx36Iul+kwlu3imMsblmovrmO7VLt1GMvqeN1qIX8gDfHw4nJY4+Rp0JY0UwUtdLG9cBo649/w6MMLKNzeBS0VmNLC4g946AqzMTKPwsq2aA+OjrByNJZRdyz8OWEkiOMkCIJwI9P2z7w7ZT0u5TLutkpyM77irH0t2QuPM722C7+ioFz6B0zta8hbc4SzId/E18HO5ZUcTVtJl19BURT87nWk2VZS+WJn2ODsoc32R6ZsOIHi/xNtK3OYBEALtncNbHD58bsPsTJ3Mr6uPSxfeJS0VS3B9yqXcT/5DWyFa3mx60KY0a+xcnMLEyteU9+7kxlvrhp8b+AT7GsWU9ZdgOOSH0U5wYYpbWzc3jJMZnhoWVlH48Sf0aQoKP4+Dsx4G3NlA12+oEN51l5DXtkpFjguoih+XBum0bRxO20J4/XSY/0VeSvaB/LU797E9PY1mBbW0+WbQGb5YfzOBsxkU936KYr7/2Lfq+20VmeDuQGnv5PagqnBtD1mZqHjm9S6L6Mofi698G3aVyxmjf0TIlzblq1sbryFiqa+YF4emM2b5ui8FJJFHCdBEIQbGcNCyn78bdKZgCHzNtK97/LsajtZWzawJtcQHCTS51H+XD2lb23j2bbzQABvxxF2Os2UVXwPgzqS6A1/zaOld9N27BTusBHcUPowP56bAfq/IPNbk9S/ZlNa9kPmpuvRG77Ft9Iv0HH4VZylZVSE3ssEDHnLKDX3cOyP58Kcgmyqn1xLSWaG+t67uC93svreAN62l1j91gJ2b/spc9P1QAZzy2s5UJ09fHZUW3iiZC7pwYjJuS8bQ9t7/NHdD953eXZ1O8W7N/Ho3AxAT/rcUp47sAFDoki9nfx2YwfFu58ayFO94V5+9Vw91c4d1Bx2oW3vt5o26+1seaKCXMOEMBsW8tbql2gL3+hueJQnn1hEZro+mJc5f02u4WRUXgrJIo6TIAiCoBLA23kcm8fInbOmRg4Q6UZMWW5szR/iRU9GwTbc7i3knnsHu92O3f46VstDmCpeG+G7p1JQ24m7NodzjqNqnHuxFBZQ0fL5MGHTmWmaDd299PnO09ncgifrbuYZJgx9Jin0pM+cQxYf4+zzqnlzO/fO+8uwvAk9E49AnHCoeQrdTrfG5bPPgmkzzGHW9PC0qTZ4Wmju/Cx+8KTfJ8RCHCdBEAQhCnWfzcDeJR26tNupaPEMPuI7hbXsO0w0Lee59/8L0DO5aDudDUtUBybZOY0Avp79lBknYSp8nvcvBIC/oOgFOw3ma3mw7+fcmV48CZ7wfHCGc8lkl+dpCielRZRPcH+UcCX42tU2QBAEQRhvLKHB+QrlmTfH+f1LXIefouLYAhr7dlIyIzT7cR6H5WNgTvKvDLg4vGYDx4ob6dtbwozQZ73XgaXbA3cmH+X4YALTZ83BQG/cJwx3zmJ6MtMY5oZh5AkSbVIXRovMOAmCIAgqejJy7qPUcJoTp/4UuQ/G10Fd/hyKrD0E8NHn/Biil8MC/82F85dH9mqfG2c3ZN377YE9UwCBSxeSdAOmkFNkxjBk1ku1ecSE8uZjnH3hc18BfH29dA8bLlaeqmk2GYN7qoYllLaTnPL0R9rQtYt83VKsri+TSZQwAsRxEoQbiiupljwelZlTjZrGIuv1o+ycMZ/Hdy/grdWbqO/wBAd6Xw/2zU+ynlVsW5qJPjSAtxxiX9vZ4DMBDx31m1htPTXC995BUamRFtsh2lSnIOA5QX3Nb7AmWucagp6MvEp2F7eweesR9bi/F5d9J5u3d43MtgEb5/P47u9i27wTu8sLBPC5jrB1876ES3Fk5PCzLblReXoKa9UatpvWq3kai/B9WV/i8wXUtLWzumYvHZ7+ARs2r9sDO9axNO4soZAqxHEShBuKm8ksP4zi3kZBxlg3/yv5LiF1TGBGyTbaDizgnCWbNJ0O3cQlHJ1WSefBVWSn6wk6J1U07buT9wpnBp+Z+WveubWMI43ViU+YxWUqeb98nn25f6DQeBM6nY6Zv36PW8uep1HDabgI9LdRUn+QmmlHyZuYhk53L1s/zqFqx6IRWTaImjc10ziaNwmdLo3MrZ9QUPU45oThMphbvisqT/8O54J6nE1r1DyNxc3MKX6MDf0bMaXNZvHhXgL62yipb+TAgn/HYrwJnS6NiXlHmVZ1iINrczXOXAmjQa5cEa4rpN4KV5YvcVkfwXTogXF3JYa0hWiCZZXn/Dk9tQVkpDDmgMtKcV4vlp4t8pFwDSNXrghCTELKw7FUiyHg6QhTIzaSb7HiiFANFrVk7WrJ0Ut1AXwuR9CeuIrHsdCiYj1cHqppiFBuDi/ffs7aV2OMtazodWAxDqcK/Z+cisifwfwLnLVTETN8UN3ZaHHgHRqhMGLU9mKswNoTytl+PB0NbN34OWuX3j1yp8nrwGLMosx6aqBeBTwnqN/6PP1rF5ErTtMNgZSycIMSrVo8hcBZO49lV+CYvgm3X0FRenjBdIIVeTXYz6obMUUtOQm15Ch8nbxYWU276R+4FJHOjRyOu6G1X4OKdUBDHgbwdTVQueIEphd6UBQFxf+vPJl2iMKq13EFJjDjvhJKaeLV4/8RtoFX1eDBTFHOlPhpa9nA6oMTWNV1GUW5SNtDn7LNtJy6rgvoZ/yAh0vB9uo7g6rbAN4PabZBadEdKZ39ENRlxN23014wSXWSbyJ7zxc81LSXtdmTRx51xnx+2fT3ZLX/lIkhGYDsl/E/9JIsk91IKHFI8JMgjFuGr7d+5WLrBsVAtlLd+mnY3z9VWquzFcwNitOvDPm7obpVuRgKa9igtF4Mf+gLxdmwZDDsxVal2oAaRtt7I59VFMV/Wmkwf0sxN5xW/JpsDtm2Smnsuzz0mQGbVVtD/05o6xKlwfmF+sw8pbzxjOIf8ky0TeFEvsvvbFDMoTi1crFVqTYY1HyI/nvo3VryMKqMYvJnpXPHD6OeiRP3kDTGzvdQvbnUuVPJi0h7vLqUOqQPF4Tk0NpmZMZJEED9+u+KoacSPNXisR2n04uoJQ95JolYjfO4P+9dNja8QYfjjSFLoEPRqmKtJQ8nYPzO98hreZ6GI/+Cw9425MJamMxdD5Zgbnmbd3vVGTCts0Jx8j1Ub9LvKqbUfJJD755RZ7OCCtCU3keOLO8IwjWFtFhBCMOzvZBJ4WrJuq9HOkWiljxy0nNZ19TGgXv+TOPmlRSaJsXZrxTNcCrWWvJQT3r2Gpqcddxz4Q02L87HNDFtyD4o/ZxCKstPs7HhD3hDjpvpYZbmJlim04J+NkWVD9C98ZXgXWLeD2m2TR/dfhtBEK4KohwuCAMYMDc4eKt8bpwvii9xWUUteVSkZ1JQkklByWPUBjx0HXmVZ1cXkudsTXDSaRgV60APVk15qCc9M4+SzDxKymsJeLo48uqzrC5cjrP17eDN8/pbue/hhbDiOJ1PzIPmdrJKG7gr7nFxrYT2UFXT3PlLcjiOLauEtrtGsd9GEISrwrU/4xTowVpkHIOTKSMUtvO5OFa3lf2hza5jZp+QUlTxve4TH+GJKO8LdNX9SK0HopYcZuEwaslaojaQXfIoZaXZce7q0qhiPcI81BuyKVlZRqnBzQdnzqvx68nIXcRaUwuvHnqNN20zWDZ/1vAdZZx8N4QvxWXczdK107G9+jsOvdlO1rLvM+eq9sBXU6D0GhIO1dSHx8hLXw/2gROkS7G6Psfnaqau5uBAmgMuK0UJT6YK45Fr33HSz6W82Y07xbocIyOAt2MfZev/iD/0p3FlnxCfqeQ9XkPxW7+hpv6E6jx5cdm3s2497NhWQqZe1JKTUkuOIjhIFGGx9wzILPhc79DcAeWVhbGdCC0q1pryUB3Y8mvUNAB4cR0/TgclVBbNHuwM0+/gwdLZWFeuZmfWA8yfo0GJ2bMvIt97rBZW2L7L7sfnh7X74B6qLOsaVu7U6JCNKSJQmjqi8/JLXIc3sdj2P2jsu4yiHKY883M6Gp5gfdfgCVJ9ZjnNSmdwtlO4ZpDWIggq+hmLqG+rZ8G5pzCm6dDpJpF3dApVnaEjzKKWnJxacpR5mSvY11nJtKNL1KPcIcXjl9iy6LY4nZEWFWsteXgzmY/W01k1RU1DWPk2PcGiGeEbu29mTtFPKDcYMGudFTI/TlXBJ2zNTEOnm0RB+zz2t20LW85V82BOIZXl88Cs0SETrnEymDxRdsRcd4zuWF7oOPTTSuPbO5VHDCiAQl61sq+zT7nofFvZ8ci84N8Mjyg733eHHSm+rLg7Gwd/V8M1tDqVS4qihB+Jfrn198q+arP63DzlkR1vK85Lakz+00qD2RB5XJwlysutLcrOgbjNSvW+9xV36OWajovHOL7sdyudjTsG04lByatuUFqdF8PCM5AeQ3WrcjHCvhAXFWdrg1KdZxi0r6F1ME0D+bpbaX3/lcHnDI8oO1pC+SPEQlu9vZEI1uP4x+lHjt/ZoJjH8Dj91SSYtmiJgVREfFppMGdHSTuMDcO3hVjSFAbF/HKL8v6+aiUv1I89slNpcV4Ms/9bQ+UhomUbLjmV1obBOCL79rB3mxsUpz+eVES0ZMNlxd35SoJ+U1GG9q3hfXQ8hgkT6sMf2aH8bscjA318RL6E5+Vn/4/SYDYMjmugGKptymvV2WF/C44zQYmOSFkNTf3+JafSMmDLPOWRHa8oDdXmMNkRYSRoHT9SM+PU8izPOW7jCZcfxd9H6/c+4NGcmczd2ssPnulCUS5yesvX2LFoK0fOqpcSpkr0LyavsXLFAVjVgl/xc8lZCfuWs3xnxyhOKF2ga+djLDz6dVXkLkxAr7KBLh9kFGyhp3UDBpbQ4PwizvKclx7rr8hb0T4g6Od3b2J6+xpMC+tVwcFQvm5lc+MtVDT1BfPnwGzeNEfnjyCAqCWnkMBZ2vbZ6V/7COaoGaPR0Y+n7RC2/of5W/Ot43S6fxgxVP0s5i+7m5ZDf6A3npin7xTWVYtZ0f5Nat2Xg31X7TdpX5HHwrpYffDNzJn/QKQMBBAp2fCVRiHURCKnsdKrPYznlWOcnL0BV0yR2DDS5lLe/DHOhiVg2EDrRT/u2hX8uPZtWquzwdyA059geW64fn/EQrdCqkhN2zU8ypNPLCIzXQ96Azn3ZWNgCVueqCDXMAHIIHP+vWR5/m/ed3qBz+g4/CrO0jIqcg2qERMw5C2j1NzDsT+eC9sImk31k2spyQx2+3rDXdyXO5m2Y6dwx91UOI/y3U+xJteAHj3pmYt44smlOHceoWOkmyC9Jzm88xylZcvUNAH6GeQ9ugxz23v80d2vMZ5Ofruxg+IB+0BvuJdfPVdPtXMHNYddg2kPz1cmYMj5a3INJ6PyRxBA1JJTgboPKu2v2Owv56Wf56QsbcH9XTdh3HyZqpcqElzqevUxVFt4omRuMO2h/nygjws5OXb+6d9CH3Dh6uqT8XYcZOOxBeze9pjaV07AkPsLnjvwKM71dTFV4vVzvs8y80ls//ThoGMV7ox53+XZ1XaytmwY6DdJn0f5c/WUvrWNZ9uCVxe5//gebVn3Ml8dL9DPoGBbM0pzvHsEtYeJzJcR9P1aSdjvB/C2vcTqtxawe9tPmZuuJ3iBcC0Hkl3aF0bMVWq9qRL9i+c+xBHr87TQ3PnZyEzOKKDW3Ult7mc47PagzVYLhaYKtPv5gThigqiCfsPo9mh5Rrhx0RvILlnHfnfwKhhFUXDvX0dJtmGUDX0ChuwS1u3vHohXce9nXUl2xCm2ax91g6/i5ve1JerAlRqCm4AVlN9vG/gIvDYIF0MN9jrBfVqfsPPwyeAps8B/cPzVFkxrF5GbcSGOEOvQeCJfE9S5Gvy47efscbuqoTVZoxCqFpHTaEYSJpw46UklEf3+ZykUuhVGSmp6hqw5zEyqk7laon/hx46TxUuPtQLjRBOFz73PBYDJxbzQ+TJmzY2nn3NnehOeRIp9LFsQBGGcENK6sh2n0xsg0NvKS9bZlD54B+mB85z5wJ0gcLw+OKRzpX7cBj6m+aW3ySotDtPQGk4IVZvIaVRiRhBGuNG5Ot+L4aJ//mZqy39MSclDFBg/x9md1BnsJInxxaKRgOt11lR0UNx4Bv/vaykvKaGk5AcYL/7PJLRsJjB91pyEp6+GXvkhCIIwnlD1tWihufMsve++TUvolKB+KrPuNCYIm6APzriDolKwNX/Ihd4/cKjl7ijJhuDe0YFZz7D/BveTqiKn5bX8XlHwuztp/OE5NhYuZ3NcraSRhBFuZK7OEJ0y0b94xBHrM6g3nKcbMWUlc3g8JPYXvcT2FZcuJCNrmUjQT80Tk/E62jMiCMJ1ScjJefNV7IdOhsk2hIRYT3LKE773J9SHzsY0M14PFwyLzc4/2psGnTGtQqgxYowtcpqYkYS5coyV0K2QDFfHcUqZ6F88utgeLtbXY6NqRRPFuyvJy9CrJ0Pm47G9yus9WgT9Qg33XWz7/kUVR970j3cAAAmOSURBVOzH07GXmtV7wsKEX3wa4HPf50MbXUYOP9uSGyXodwpr1Rq2m9YHBf1SkQXCyBmnau/XpsrwCNWpoxX4hXHGFHKXPoxp5wY2RMwM6cnIXc6W+9tZXbOXDo96irrHRtWKfZh2rGNpvKtzBlTb7azf8H6khpYWIdRkRE4HGEmYkRK+D+lLfL6vRhDHGArdCpq5apvDUyb6FxMz1aW38/HWe4MiewUOsvY3Ul8SEtm7mcylW2hZ+xUbb5+ETjeThQ3/zeInn4gv6Jcxn1821ZL7XpkqjpjNr9+ZStmR16gOm7zSzynCsuEiFaZbuGXx7yKP7AYjYm75rihBv7/DuaAeZ9OacX3aRri6XJsqwyNRp46hwC+MM/Sk31VMqdmAofwnFIWLeabPo3xPIwcW/DsW403BPvgXH7HgQBtN64Y5hZl+Bw+WzgdDtNOiRQg1GZHTECMJM1JuZk7xY2zo34gpbTaLD/eObDZrzIRuBa3oVNGnoT/odMT5aRwTwOvYyNzCXrYkuhRUuG5JSb0N9GAtLmDjnQcSXDwrjB3SjlPBtdmHCyPjS1zWR8hz/lz6rFGgtc3I9IZwYxLw0LXfQr56OsdYtotjrqiFuXMneauuDGPcZ/rxdNmpK8saPOUTcRpHFaXULWWvo439Axd+ZlFW1xx17NmL69guyoyhd9XxutVCftjSXORS3Xkclhx0RXtwdNiw5BuDcRvLqDsWfhooELxYNGSjsYy61/diyU+0FBlu9zF2DaSvCMv+jqhLkL24HNbB9+uKsFgdYWmLWqrzOrAYjRTtPUZHzPwPOU1P4+E1KkxfD7OzH0+XLcG71DzJt7A3VG7GGhzer/C5HFgH8j+6nAThWmEMhW4FzYjjJNx4BD7B/piZnH1pVDkvoigXcSw4RVleDfazgxtaEysFp1L9vp+z9hryyk6xwHERRfHj2jCNpo3baRsuLcOoDAfOHmFN3jq6F/yfXFIUFNd6pjQ9y/Y2LZsJh1PgT0IFP4JE6tTxFPj7NShHq7T9M+9OWY9LuYy7rZJc/UlerKym3fQPwTwYKKeNMcUYBWH8MoZCt4J2RntniyCMJ7TU28j7oVRC93Q1nFb8Me8XDIVbojQ4v1CG3M018FD4XV7R9x+GCN1Jpd7NdbFVqTbMi7q/bGjYmPdaRd9NFZ6OkI3ljUpfxHVese5qjEiE+u44NkXcbRb9TLQNse5Di3dPZChvo/8dHWf0uxLnSWS53ThIHy4IyaG1zciMk3CD8WVQdyb6WHRGAbVuN83lc4eZhg1JXaRK/f6rOGry4Sc0kyBcZdj7Ic029xDZD+1yHIkU+M+PTgU/gmFUpQcU94dTjo4Tu3Ee9+e9y8aGN+hwvIEjeklWEAQhCcRxEoQRcbXU78cDbj44cxb3FVfBH045Og7puaxrauPAPX+mcfNKCk2TYuyPEgRB0IY4ToIwEq6a+v14wMids2ZgvOIq+FqUo+OQnklByWPU/t6N4nfT2Xg/5zYWkre5bVxpdQmCMP4Rx0m4wVBvd49eFlJFL3VFVlxaJiFSpn4fEleNo3afVFxRqEKzQ2a/fG6Nzl0iBf6pV1AFf+TK0bGjM5Bd8ihlpdlyN6QgCEkjjpNwwxEUKf1f2b55Dw5PP9CPp+0Qtpa72bGthEwtrSKV6vcZ83l893exhavdJ1Sy18pU8h6vodhWy1Z7T9B58vVg31rLdk0RD6PAP2Yq+DEU+DUpR8cmKONQhCWUBwTwud6huQPKKwsH1amF2AR6sBYl6ZyOC7y4jj1Lzf5rzW5hvCNdhnDjoZ9BwZZ9dD76OZuNN6HT3YRx8+c82pnMcd5Uqt+rqsg101T14jQyt35CQdXj8ZXsNaKfsYj6trVMO7qEiTodusyn+bjgMXaYtWwOH06Bf+xU8Icq8GtRjo4TV+YK9nVWDuaBLo2JeUeZVvUSWxbdJp3g9Yq3k4ayZ+gS+XkhxVxnyuHCjc71VG8DLivFeb1YerYkcV2Jloh7sBZX4LQcjXN9iyh3Xw+krC0EerAW/4hDy97grWFPnY4jvA4sc1fwwRbHtWW3cNUQ5XBBuFbwOrAYsyiznhrYixTwnKB+6/P0r11E7oidpqCStrFsPz2h02MBDx31T7Ox/8cszZ2SCuuFa5yApyNM1d5IvsU6rGTDsGFCCvF1r9M8oL5vJN9io8tzPkolf496GXAIjQrxiVTzvQ4scwvZ7vHQUnE7aSHlep8Lh3VQsV5OVwojQRwnQbjaZMznl01/T1b7T9WlJB1p2S/jf+glDq4d5lLUhEwl75cvsTvLQcHENPX4vpk9/odoGmZ5S7gxCJy181h2BY7pm3D7FRSlhxdMJ1gRpaI/sjAeWtbvxaGq7/vd+/leh4UcYz5bT+XyTJ+CcqmbLbzIoo1vqOrvSSjEJ1LNzyigtqeVaoMBc8Np/O5tFGR46XpxLSvav80Ll/yqiv860mwrqTrskn1QgnZGq6ApCOMJqbeCEGT4thClYB/19wF19wg1fI1hYirExwobS1l+ZArxQ8JG/zsiHYIwFK3jh3xyCoIg3Ih4P6TZ1hVDbyuobO+xHafTGxh9GM2MTiF+WMV6/XS+c/9cWjb+liMdDuxyybMwQsRxEgRBuIHxbC9kUrgau+7rmCpeS0EYwwi1vEaoED8sk8ledxDngXu40FjL4kITEzXu6RKEcMRxEgRBuGFR9wDFUGNX3NvinOYcSZhkGIVC/LBkkFlQQnltc3BfVOdufnhuF4V5zwQ3jwuCBsRxEgRBuBEJKcuf+AhPhM9wga66H8VW0R9JGM2kWCF+WCZgyF7EyrKFGDy9nDkXezO8IEQjjpMgCMINiaos/9ZvqKk/oTpCXlz27axbTxwV/ZGESYJRKMQPId2IKesbwf//3Ifvq6ACer7FPig/4HNxvLkLyn9C0RzRKxO0IY6TIAjCDUpQWb6eBeeewpimQ6ebRN7RKVQlUNEfSRjtjFwhfqihsym2lNJfcTtpt5Rz+ONZPLrvEFXTjpIXkudQ427a8iNmyGgoaESUw4XrCqm3ghBE2oIgJIcohwuCIAiCIKQYcZwEQRAEQRA0Io6TIAiCIAiCRsRxEgRBEARB0Ig4ToIgCIIgCBoRx0kQBEEQBEEjX0v0o06nu1J2CELKkHorCEGkLQhC6knoOIkGiHCtIdo1ghBE2oIgJIfWD424ApiCIAiCIAhCJLLHSRAEQRAEQSPiOAmCIAiCIGhEHCdBEARBEASNiOMkCIIgCIKgEXGcBEEQBEEQNPL/A+03CcgJR70zAAAAAElFTkSuQmCC\"/></strong></em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for a good attempt (for example the new node being added as the 3rdnode in the list).</em><br/><em>Award <strong>[2]</strong> for a fully correct diagram.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>When a method calls on itself.</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for correct recursion levels i including i = 3.</em><br/><em>Award <strong>[1]</strong> for the correct flight id's (with or without the line for i = 3.</em><br/><em>Award <strong>[1]</strong> for 12:55 and 11:05 in the correct order.</em><br/><em>Award <strong>[1]</strong> for 12:30 (ETA) in the correct order.</em><br/><em><strong>Note</strong>: that there are many ways to trace a recursive algorithm.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>For answers without iterators<br/><em>Award up to <strong>[7 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for correctly instantiating the <code>LinkedList </code>variable <code>result</code>.</em><br/><em>Award <strong>[1]</strong> for declaring (and instantiating) all other variables.</em><br/><em>Award <strong>[1]</strong> for correct main loop.</em><br/><em>Award <strong>[1]</strong> for correctly comparing the first Arrivals in the two lists.</em><br/><em>Award <strong>[1]</strong> for correctly removing the earlier Arrival from its original list.</em><br/><em>Award <strong>[1]</strong> for correctly adding this Arrival to the end of the resulting list.</em><br/><em>Award <strong>[1]</strong> for attempting to add the remainder of a non-empty linked list.</em><br/><em>Award <strong>[1]</strong> for correct loop for copying the remainder of <code>runway1</code>.</em><br/><em>Award <strong>[1]</strong> for correctly removing/adding the remainder of <code>runway1</code>.</em><br/><em>Award<strong> [1]</strong> for also copying a possible remainder of <code>runway2</code>.</em></p>\n<p><em><strong>Example without iterator</strong></em>:</p>\n<pre><strong>public</strong> LinkedList&lt;Arrival&gt; mergeLists()<br/>{<br/>    LinkedList&lt;Arrival&gt; result = <strong>new</strong> LinkedList&lt;Arrival&gt;();<br/>    Arrival temp = <strong>null</strong>;<br/><strong>    while</strong> (!runway1.isEmpty() &amp;&amp; !runway2.isEmpty())<br/>    {<br/><strong>        if</strong> (runway1.getFirst().compareWith(runway2.getFirst())&lt;0)<br/>        { temp = runway1.removeFirst(); }<br/><strong>        else</strong><br/>        { temp = runway2.removeFirst(); }<br/>        result.addLast(temp);<br/>    }<br/><strong><br/>    while</strong> (!runway1.isEmpty())<br/>    {   temp = runway1.removeFirst();<br/>        result.addLast(temp);<br/>    }<br/><strong>    while</strong> (!runway2.isEmpty())<br/>    {   temp = runway2.removeFirst();<br/>        result.addLast(temp);<br/>    }<br/><strong><br/>    return</strong> result;<br/>}</pre>\n<p><em><strong>Alternative example without iterator</strong></em>:</p>\n<pre><strong>public</strong> LinkedList&lt;Arrival&gt; mergeLists()<br/>{<br/>    LinkedList&lt;Arrival&gt; result = <strong>new</strong> LinkedList&lt;Arrival&gt;();<br/>    Arrival head1 = runway1.peekFirst();<br/>    Arrival head2 = runway2.peekFirst();<br/><strong><br/>    while</strong> ((head1 != <strong>null</strong>) &amp;&amp; (head2 != <strong>null</strong>))<br/>    {   <strong>if</strong> (head1.compareWith(head2) &lt;= 0) // or &lt;<br/>        {    result.addLast(head1);<br/>             runway1.removeFirst();<br/>             head1 = runway1.peekFirst();<br/>        }<br/><strong>        else</strong><br/>        {    result.addLast(head2);<br/>             runway2.removeFirst();<br/>             head2 = runway2.peekFirst();<br/>        }<br/>    }<br/><strong>    while</strong> (!runway1.isEmpty())<br/>        {<br/>             result.addLast(runway1.removeFirst());<br/>        }<br/><strong>    while</strong> (!runway2.isEmpty())<br/>        {<br/>             result.addLast(runway2.removeFirst());<br/>        }<br/><strong>    return</strong> result;<br/>}</pre>\n<p>For answers that use iterators<br/><em>Award up to <strong>[7 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for correctly instantiating the <code>LinkedList</code> variable <code>result</code>.</em><br/><em>Award <strong>[1]</strong> for declaring and instantiating iterators and all other variables.</em><br/><em>Award <strong>[1]</strong> for correct main loop.</em><br/><em>Award <strong>[1]</strong> for correctly comparing the first Arrivals in the two lists.</em><br/><em>Award <strong>[1]</strong> for correctly adding the earlier Arrival to the end of the resulting list.</em><br/><em>Award <strong>[1]</strong> for testing <code>iter.hasNext()</code> and loop termination.</em><br/><em>Award <strong>[1]</strong> for attempting to add the remainder of a non-empty linked list.</em><br/><em>Award <strong>[1]</strong> for correct loop used for copying the remainder of <code>runway1</code>.</em><br/><em>Award <strong>[1]</strong> for correctly copying the remainder of <code>runway1</code>.</em><br/><em>Award <strong>[1]</strong> for also copying a possible remainder of <code>runway2</code>.</em></p>\n<p><em><strong>Example with iterator</strong>:</em></p>\n<pre><strong>public</strong> LinkedList&lt;Arrival&gt; mergeLists()<br/>{<br/>    LinkedList&lt;Arrival&gt; result = <strong>new</strong> LinkedList&lt;Arrival&gt;();<br/>    ListIterator&lt;Arrival&gt; iter1 = runway1.listIterator();<br/>    ListIterator&lt;Arrival&gt; iter2 = runway2.listIterator();<br/>    Arrival curr1 = <strong>null</strong>;<br/>    Arrival curr2 = <strong>null</strong>;<br/><strong>    if</strong> (iter1.hasNext()) {curr1 = iter1.next();}<br/><strong>    if</strong> (iter2.hasNext()) {curr2 = iter2.next();}<br/><strong><br/>    while</strong> ((curr1 != <strong>null</strong>) &amp;&amp; (curr2 != <strong>null</strong>))<br/>    {   <strong>if</strong> (curr1.compareWith(curr2) &lt;= 0)      // or &lt;<br/>        {   result.addLast(curr1);<br/><strong>            if</strong> (iter1.hasNext()) {curr1 = iter1.next();}<br/><strong>            else</strong> {curr1 = <strong>null</strong>;}<br/>        }<br/><strong>        else</strong><br/>        {   result.addLast(curr2);<br/><strong>            if</strong> (iter2.hasNext()) {curr2 = iter2.next();}<br/><strong>            else</strong> {curr2 = <strong>null</strong>;}<br/>        }<br/>    }<br/><strong><br/>    while</strong> (iter1.hasNext()) result.addLast(iter1.next());<br/><strong>    while</strong> (iter2.hasNext()) result.addLast(iter2.next());<br/><strong>    return</strong> result;<br/>}</pre>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18N.2.HL.TZ0.16",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Consider the following algorithm.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAd0AAAH+CAYAAADZBdT2AAAgAElEQVR4nOzdf1RTd74v/HewT0/ppU6H2tUmxdbxeoOdhRWFeLgHXFDqQJlpT1ihtkePggP0x6mgd7wGBJ1OpwWrhEefCp2xFRjBjp2Okzxw23ksVBk4wj3WgKD1OZIcl2M1hvaoaYs8pvUc2M8fEORX+JnsvZO8X2uxliab7E+Me7/z/e7v/n4VgiAIICIiIq8LkroAIiKiQMHQJSIiEsldUhdA5KusVuuIv1+7dg1fffXVhL+zcOFC3HvvvUN/v/feexEWFuaV+ohIfhi6ROOw2Wy4desWzp07h97eXpw7dw4AYDAYvLZPjUaDhIQEAEBMTAwAICIiAvPmzUNoaKjX9ktE4lFwIBUFMqfTiStXruDcuXPo6urCX//6V1RUVIzZTq/XAxgIwZCQEABjW61TCUdXmLvcunULFy9eBADY7XZcvnwZX3/9tdsaHn30UajVaixYsABqtXr6b5iIJMXQpYDicDhw/vx5dHR04NixY6irqxt6TqvVQq1WIyYmBg899BAefPBBzJ8/H8HBwZLVev36dVy6dAlffvklzp07h6amJpjN5qFtsrOzsXLlSjzxxBMIDw+XrFYimhqGLvm9zs5OnD17FiaTaUTI6vV6xMTEICIiQtJwnS6Hw4HLly/j7NmzOHHixIhWcXZ2NlJSUrBixQpeKyaSIYYu+aXOzk60tLSgpqZmqGXoCqSIiAi/65p1fbEYHsJarRY6nQ6JiYkMYCKZYOiS33A4HPj4449RXl4+FLTFxcWIj4/H8uXLfaYlO1sOhwNtbW04cuTIiAB+9dVXsXLlyoD5dyCSI4Yu+bzOzk4cPnx4aGSxXq+HVqsNqKB1xxXAv/nNb4a61svKypCamsrWL5EEGLrks1pbW2EwGFBXVweNRoOcnBw888wzvL3GDavVioaGBuTm5gIY+HKSnZ3td13tRHLGGanI51itVqSmpiIuLg52ux1GoxHNzc1IT09n4E5ArVYjJycHN27cQHV1NZqamhAeHo68vDzYbDapyyMKCGzpks9wOBzYtWsXDAYDNBoNtm3bBp1OJ3VZPsvpdOLo0aNIS0sDAFRXV2P16tUB3yVP5E0MXfIJDQ0N2LFjB8xmM8PBwxwOB/bv34/t27dDo9HgvffeQ2RkpNRlEfkldi+TrDmdTuTl5SE5ORlLly7FlStXkJ6ezsD1oNDQUBQWFqKjowMqlQrLli1DTU2N1GUR+SW2dEm2bDYbdDodzGYzjEYju5JF4HQ6sXfvXmzfvh3Z2dnYt28fv+AQeRBDl2Sps7MTL730EgDg/fff5whbkTU0NCA5ORkajQYmk4m3FxF5CLuXSXY6OzuxbNkyqFQqmEwmBq4EkpKS0NHRAQDQ6XQc3UzkIWzpkqy4upSXLl3Krk0ZcH0eKpUKH3zwAT8Polli6JJsOJ1OxMfHAwC7NGXE1fOg1+tRUlIidTlEPo2L2JNs7N27F2azGRaLhYErI5GRkaivr0dycjJiYmI4oI1oFnhNl2TBarVi+/btqK6uluga7newVj0PhUIxzk8y8qsaYe3t995+VYVo7PHG63tGUlIS9Ho9du3aBYfDIXU5RD6LoUuyUFFRAY1Gg9WrV0tcyWpUWpwQBGHop8/+Gh5u3oyEzbW46vFcvAfqzD9CsO9E4lx5H47btm2D2WzG4cOHpS6FyGfJ+yingGCz2WAwGFBUVCTLgTpByv+OrIxngao/oP7Cd1KXI5nQ0FCUlZWhpqYGTqdT6nKIfBJDlyTX2NgIAFi5cqXElUxCuQgLHr77zt/7u9FenY8nXd3QT+ajqtGK3hG/1APrp3uRoRrYRpVRij9V5eNJRTTyG69jTPdyTyPyVSokH/gUp4a9tipjLz619oj7fscRFxcHs9mM06dPS10KkU9i6JLkTpw4Ab1eL8tWLgD0d/8LKn/fA31tLhJcXcD9X8D0YhKebXwUu+3fQxD6cPO3P0bzujRsNn2BgV7o27hqKkRCxjnEN34LQeiDteBBfLSjBE0T7rEbDS+Vwnjfz/GRIEDos+H9Rz5B0suVaPfKdeWpi4yMhEajGbqHl4imh6FLkquoqEBMTIzUZQw6gqzw4BEDqeao4rCl6t/wpe1b3AIA9KOn6V3kVD2Oou1ZWKG8G0AQQhavR9n7z+Jozrto6ukHelqwL6cZKeWvYcPiucO2KYBykiqUefnYrluMEAAIUiJ6VRSUTf+CM/bb3n37U5CQkIDLly9LXQaRT2LoEo0wdiCVcPM8jHlASdpW7G//BoADbfUN6B7d3YwghIQtwpLuBtS3XUdP2zEc6n4csREPDTvQBreZVk2u37kIi6138s1F8PXXX0tdApFPYugSTSZkMVKzXkAS/ow9fzyNoSur3W/hqR/MGdkqDs9Cg5S1iuDRRx/FtWvXpC6DyCcxdImmIOjhBYgc3SecVAlLnzCyVSwIEIQ27E6cJ0mdYrh8+TLnwyaaIYYuSU6r1aKrq0vqMibU/+UldHZHYX3yE5iLUEQnJ0H5+Wmc6x5+jbUfve178aTieVRZb2Nu9CqsV47uEu5Hr+0CPhf7DXhQU1MTHn30UanLIPJJDF2S3KpVq1BbWyt1Ge71dqG28kM0JPwjnl8RCiAIcxNeRnlKM3IKD+BU920A/ei11uLNre8Ahq14Xn0PMDcOm8pjcOjNPTBZe4a2KX7zILolfkszZbPZYDab2dIlmiGGLknOde9na2ur1KVgvNHLivs247PwXLQd3oiokMFDJugx6N424v34y8hX/Q0Uijm4L6EOD+Z+iMNbVgyMOsbdeES3E02FD6Iu4QdQKOZAXfwFEnM3IUnCdzgbrnuqo6OjJa6EyDdxlSGShdTUVACQd4vXQ/qtVUhJuID8riLZT/04nGsVqNTUVBQWFkpdDpFP8p0jnvyaXq9HXV0dGhr8aOxvTyPyVUuQUXVuaJaq/u5WvF38G9zekooVPhS4AFBZWQmz2YznnntO6lKIfJZvHfXkt2JjY5GdnY0dO3b4zyo2c+PwPz76NZY0r8F9rluKot5Dn/bdYV3QvqGzsxO5ubkoKyvj9VyiWWD3MsmGzWbD/PnzkZ2djX379sl2WshAY7PZoNPpoFKp8MEHH/BzIZoFLmJPshEWFoaWlhbExcUBAA4cOCBxReR0OvHrX/8aAFBeXs7AJZolhi7JSmxsLIxGI9LS0gCALV4JORwO5Ofno6KiAh0dHQgLC5O6JCKfx9Al2dHpdAxeibm6lM1mM1paWhAZGSl1SUR+gQOpSJZ0Oh1aWlpQUVGB+Ph4WK1WqUsKGA0NDZg/fz4A4MqVK4iNjZW4IiL/wdAl2YqNjYXFYgEAhIeHo6amBk6nU+Kq/JfD4UBeXh6Sk5ORnZ2NTz75hF3KRB7G0CVZU6vVaG5uRnFxMTIyMrBmzRp0dnZKXZbfaWhowNNPPw2DwYDq6mocOHAAoaGhUpdF5HcYuiR7wcHBKCwsREdHB+x2O5YtW4a8vDx2OXtAZ2cnUlNTkZycjKVLl8JisSA9PV3qsoj8Fu/TJZ/idDpx9OjRoUFWer0e2dnZnLBhmlpbW1FXVweDwQCNRoOioiIkJfnqjNBEvoOhSz7J4XDg8OHDyM3NBTAQvlqtloN+JuB0OnH69GkYDAbU1dVBo9Fg27ZtSElJ4ehwIpEwdMmnORwONDU1YdeuXTCbzdBqtdDpdHjmmWd4TXKQzWZDbW0tampqhv6NXn31VaxcuZJhSyQyhi75BVcr7uDBg6ioqAAAZGdnY/Xq1YiOjg64ALbZbGhsbITJZEJdXR0AoLi4GD/96U95zy2RhBi65HccDgc+/vjjEYGTnZ2NlStXIiYmxi+v/zqdTlgsFrS0tODYsWMj3negfvEgkiOGLvk1m82GU6dO4ejRo0MtYGDgGnBMTAwiIiIwf/58n+tmdYXs2bNnceLEiTHvbdWqVQxaIhli6FLAcHVBd3R0jGgNAgMtwh/96EdYvHgxIiIiMG/ePNkEls1mw/Xr13Hx4kV0dXXh1KlTY2pfuXIlnnjiCYSHh/vcFwiiQMLQpYDlai1evHgRJ0+eRFNTE8xm84htsrOz8cMf/hAREREICQnBQw89hAcffBAAPNJCdjgcuH79OgDg0qVL6O3thd1ux+XLl2G1WkeEKwBotVqsWLFi6MuBL7bSiQIZQ5doGKfTiStXrgwF4MmTJwEABoNhSr+v1+uH/uz6HddjX3/99YhuYHc0Gg0SEhJw//33Y/HixUNB74/XookCDUOXaBpsNhtu3boF4E7LdDhXSANjQxcAYmJiRmw/vOUspy5tIvIOhi6RlygUCgAADzEicuHcy0RERCJh6BIREYmEoUtERCQShi4REZFIGLpEREQiYegSERGJhKFLREQkEoYuERGRSBi6REREImHoEhERiYShS0REJBKGLhERkUgYukRERCJh6BIREYmEoUtERCQShi4REZFIGLpEREQiYegSERGJhKFLREQkEoYuERGRSBi6RLNks9mgUCjG/LiMfnzFihUSVktEUmLoEs1SWFgYiouLp7z9tm3bvFgNEcmZQhAEQeoiiHydw+HAAw88MOl2Wq0WtbW1IlRERHLEli6RB4SGhsJoNE66XUlJiQjVEJFcMXSJPCQlJQVardbt83q9Hmq1WsSKiEhu2L1M5EGtra2Ii4sb97kbN24gNDRU5IqISE7Y0iXyoNjYWOj1+jGPV1dXM3CJiC1dIk+zWq0IDw8f+rtGo0FzczOCg4MlrIqI5IAtXSIPU6vVKCsrG/p7UVERA5eIALClS+QVTqcT8fHxWLp0KQ4cOCB1OUQkEwxdkoXhMziR/PG0QTQz7F4mIiISyV1SF0A0HFtQ8sYeCaLZYUuXiIhIJAxdIiIikTB0iYiIRMLQJSIiEglDl4iISCQMXSIiIpEwdImIiETC0CUiIhIJQ5eIiEgkDF0KYN/BWvU8FIrnUWX9btRz/ejtqkaGSgFVRjW6evu9XMttXDXlQKUqRGPP4L56GpGvUkChcPMzfFsi8gkMXaIx+tHbdQgbEwtweb0RTe+sx+IQ7x4q/Vc/xms576B7+INzE7HbLkAQhv/04WbbHiTgZzB8lIfEuTyEiXwJ514mGmFY4G6oxuGin0Dp7VzrPYeDhf8nLoZHAZbJtm3D/q3vAIbDeCXqfi8XRkSexq/JREMkCFx8g/b927DjrldQuHbhFLZ9A3rL8/jlS9EI8XZpRORxDF0iANIEbj9623+HrXsWoPwNLR6bM8nWVxvxmz1fILP8ZSSwW5nIJ/HIJcJ/wHH2d9iYuAE13ZNvPeAKTBmL3A9yGvxZVNqO/3T3Er1t2L/1n/Gzj4qge+TuSfb3HS7U/wFVeBb/uGo+D1wiH8Vjlwi10K8uxuUNH+GzIzpY3tqCHbVfYOJxwfOhq74wapDT2J8LW6PGHzjR/wVMm1/Fn39WMLVrs/2X0PLhaSRsScUKtnKJfBaPXiIokVBQjcNFz2CFrgDvF6hQlfMGDnb1eGl/t3G11oCci/+I0lemdm22/8L/xocNy7H+75/gtVwiH8bQJUIc1m9YOXANN+gRJBa+CUP4UWT9UyXa3d6fO4vu5f6LqH/XhO6mLYi+b87gtsEIzzoCdL+Fp34QhuSqrmEt7e9woeUTNCgXYcHDk3VDE5GcMXSJRguJxiuleiQ0bcGzm2txddzcnUX3ctBiZNbbR23rhKVyNaAswPFvbajPXHzn4Oy/hJYPW6BcvwrR7Fom8mk8gonGCEJIVBZ+W5kJVP0Kr016fdfLeu2wfA4sCVexa5nIxzF0icY1F4s3vIbyTKAqbSP2tH8jdUFE5AcUgiAIUhdBpFAoAAD87yhv/JyIZoctXSIiIpEwdImIiETC0CUiIhIJQ5eIiEgkDF0iIiKRMHSJiIhEwtAlIiISCUOXiIhIJAxdClw9jchXKaB4cu/4Cxv0NCJfpRq1+IC3fIP20megym/E6LWN+rtPoTo/+c5CCk/mo6quHd2Szk1JRDPB0CVqMmDr/jb0SlZAD7qq30Lh/3167FP9X6B2x6s4iHVos38PQfge9t2PovmfXsb/1XRd/FKJaFYYuhTglPi7hB/Bon8D+yWYX3mgFVuIjx//e7wwzmoG/ReO492qhViftRpRyrsB3A3liixsL1qIQ/Vnx7SKiUjeGLoU8ELWFmBf5hfQb/3dBOvnekF/Fw5ueAOW5AJsiX5gvA3Qa7uAz8eso3s3Hl6wCDh0DG097GMm8iUMXaI5i5D6xq+RaRG5mzkoDCkH/4CdiY+4ORBv48tLF9Dt7ve7L+DSl7e9Vh4ReR5DlwhA0CPP4I1y3fS6mbtNyHANbnL78yRK293FeAiUyolWyO2FzXJxum+FiGSMoUsEALgbj6TqUT6dbmalDtWCAGHCn79gaxSXnieiAQxdIpegx6TpZnYrBGHhC6Uugog8iKFLNMyIbubPrk288ay7lyczMGBK6e7pMQOsiEju7pK6ACJ5cXUzP4O01/cjAYDbWBvsXq72Wi1BCAlbhCXdnwwMmJp7z+DjgwOsljyNsBB+bybyJTxiiUZzdTP/tQlNbocOi1TKoqfwcuZ57Cj+EF29/QBuo/tUJYp3XERe/t9DzSOYyKfwkCUax0A380b3XbuiFTIfSflvo+jhw3j8vjlQKP4GqtRTWFL+Lv5HwjypqyOiaVIIgiBIXQSRQqEAAPC/o7zxcyKaHbZ0iYiIRMLQJSIiEglDl4iISCQMXSIiIpEwdImIiETC0CUiIhIJQ5eIiEgkDF0iIiKRMHSJiIhEwtAlIiISCUOXiIhIJAxdIiIikXA9XZIV14T6RET+iC1dIiIikbClS7Lgj0vFcRk8IhqNLV0iIiKRMHSJiIhEwtAlIiISCUOXiIhIJAxdIiIikTB0iYiIRMLQJSIiEglDl4iISCQMXSIiIpEwdImIiETC0CUiIhIJQ5eIiEgkDF0iIiKRMHSJiIhEwtAlIiISCUOXiIhIJAxdIiIikTB0iYiIRMLQJSIiEglDl4iISCQMXSIiIpEwdImIiETC0CUiIhIJQ5eIiEgkDF0iIiKRMHSJiIhEwtAlIiISCUOXiIhIJAxdIiIikTB0iYiIRMLQJSIiEglDl4iISCQMXSIiIpEwdIkkZrVasXPnTqnLICIRKARBEKQugsgfKRQKAIC7Q8zpdKKyshK5ubkTbkdE/oMtXSIJmEwmxMfHDwUuEQWGu6QugCiQdHZ24vXXX0ddXZ3UpRCRBBi6RCKw2WzYt28fDAaD1KUQkYQYukReVlNTg4yMjEm3c10DJiLpeWuMBQdSEXkJQ5TId3krGtnSJfIyi8WCvLy8Sa/j8vsvkfS8/WWZo5eJvEytVqO2thZGoxEajUbqcohIQuxeJvKS8e7THX1v7nA8FImkN9n99bN+fYYukXdMdPCON5qZhyKR9Bi6RD5qKgfv8Pt2eSgSSY+hS+SjpnrwOp1OnDhxAklJSWKURUQTYOgS+ShvH7xE5HnePm45epmIiEgkDF0iIiKRMHSJiIhEwtAlIiISCUOXiIhIJAxdIiIikTB0iYiIRMLQJSIiEglDl4iISCQMXSIiIpEwdIlmyWazQaFQjPlxGf34ihUrJKyWiKTE0CWapbCwMJSVlU15+6KiIi9WQ0RyxtAl8oC1a9dCo9FMup1Wq+VqQkQBjKsMEXmIyWRCWlrahNtYLBao1WqRKiKi6eIqQ0Q+IiUlBVqt1u3zxcXFDFyiAMeWLpEHdXZ2YtmyZeM+d+PGDYSGhopcERFNB1u6RD4kMjISer1+zONGo5GBS0Rs6RJ5ms1mw/z584f+rtFo0NzcjODgYAmrIqKpYEuXyMeMvoVo7969DFwiAsCWLpFXOJ1OxMfHY+nSpThw4IDU5RDRFLGlS+SDTp8+jXvuuQd//etf0draKnU5RCQTDF0iD2ptbUVqairi4uIQGhqKkJAQxMXFITU1leFLRAxdIk8YHrYA0NLSgtraWtTW1qKlpQUAGL5ExNAlmg13YRsbGzu0TWxsLMOXiAAwdIlmZCphOxrDl4gYukTTYLVa8eKLLyIuLg52ux1Go3HSsB2N4UsUuBi6RFNgtVqRl5eH8PBwnDlzBkajEc3NzdDpdDN+TYYvUeBh6BJNYHjYNjU1jQhbT014wfAlChwMXaJxiBG2o7nC12g0wm63D4Wv1Wr1yv6ISHwMXaJhpAjb0XQ6HZqbm4fCNzw8HHl5eQxfIj/A0CWCPMJ2uODg4BHh29TUxPAl8gMMXQpocgvb0Ri+RP6FCx5QQHI4HNi/fz+2b98OACgrK0NWVpYsgnYiTqcTR48exa5du2A2m6HX65GdnQ21Wi11aUR+wdsLHjB0KaA4HA4cPnwYubm5AAbCdu3atT63wDzDl8g7GLpEHuAvYTsaw5fIsxi6RLPgr2E7GsOXyDMYukQzEChhOxrDl2h2GLpE0xCoYTua0+nEkSNHkJGRAQDQ6/XYtGkTwsLCJK6MSN4YukRTwLAdH/9diKaHoUs0AVd3alpaGgC26Nxh+BJNDUOXaBy8djkzDF+iiXk7dDkjlej60du+F08qVHiy9BR6x93mOhrzo6FQ5cB09TbQ04h8lQIKxQQ/yVWw9rt+vwfWxg9RmrFk2DbJyK8yodHaI9o79Qan0wmTyYT4+HikpaUhISEBFosFJSUlDNwpCA0NRU5ODm7cuIGysjLk5ubigQceQHl5ORwOh9TleVA/ehoLoXJ7nLmefx5V1u9GPdcDa6MJVfnJo46fj9HefVuc8sl/CSSBr4U2w88E4GeCoe3rUc/1CTfb9ggJiBAyjZeEPkEQhG+PC3lKCMq848K3k77294LNuFFQKtOFPZ/ZB35fEAThpkVoMKQLSmWmUHl+8leRm1u3bglGo1HQaDQCAEGv1wsWi0XqsnzejRs3hLKyMgGAAEAoKysTbty4IXVZHtAnfHu8QFACbo+zgedXC5UW57CHbcLxgiQBynTB0GARbroetrcJRkO6oESSUHDcdue4Ir/jOha89vpee2Wa2M3PBEOCUkDCHqHtZt+Yx5WZRsHmeng6ofvtcSFPqRSSKs+PPTH0nRcqk5RTDG95YNiKw//C1xWqUUJCwn8de5yNG7qDX4aVGwWj7ftxXnOy58kfMHT9lqtFqxQSDJ8NfqMePKhHnyCmEbp9lkohCW5C14cwbKXhP+F7J1Tfa6gRMpXDj7ORzw+F7kRfWF2m1etEvsjboctrupIJQkjUz1FqWI4m/RvY3+5Ab/vvsFUPGEp/jqiQmX00QYuewsuZD6AhazV+XnoYpkarm+vG8sRrttLyx2u+cx7T4o1yHSz6N7C//Rs3W/Wjp+0YDnWrELlgnvvBLnOfQPL6KHQfOoa2nn53WxG5xdCV1P2I2vIOjJlfQL81Fxu3vgMYXsMrUfePu3V3yVP4wbgDqaKR33h9YKOgx6B724gGw3J8qv9HpD0VjvsUCiiezEeVqQnWXvmeKFpbW4fCVqVSoaOjg2ErkeHhW1xc7OPhezceSdWjPPML6Lf+Du3jHgO38eWlC+jGQoSHhUz+kt0XcOlLDqqi6WPoSi3oMaS+8WtkWg6jBhtR+ko03B3yyrzj+HbgksConzbsTpx3Z8MQNX6ytRr2Pjvaao0wHjEg3VKCrLQnEa5+EVVd8hrB3NraitTUVMTFxUGlUqGlpQW1tbWIjIyUurSAFxoaisLCQlgsFuj1euTm5uLpp5+GyWSC0+mUurypGzrODNi6v82nen/IvzB0ZSDovvsxD4ByxVL8txl2K4//wkpEaXXQPbcV1fY+3LQYkRd+FFmb/zTs9iLpDA9bAENhGxsbK3FlNJparUZJSQksFgsSEhKQlpaG+Ph4nwrfoEeemaCb+W48vGARlLgIi20KkaxchAUP3+2VOsm/MXT9ynewVj0PhaoQjWOuNwUhRP1TZK2PAxo+QcuF0fcmiodh67t8O3yHdzNX4bNv+oY9F4S50auwXmlH56XrcPudtOcs6g+1Q7l+FaLn8vRJ08f/NX7lHiyKexpJ3Q2ob5vgulvS04hbdI94ZQ1i2PoPnw3foW7mPXi9rGXkc3OX4/kty9Gwoxy1V8e7XvsN2t/bgxJsRPmmOMwVpWDyNwxdPxOkfg5vVy7FoXX/E6WmdnS7vrL3d6O9+ld4OesWDDt1UIv4yTNs/Zcvhq+rm/mvTf8b3SOeuR9RW97F8Q3/hjTNiyj99M7I//7udphKN+NZ/X+g4P0CpD7CrmWaGYauD3E/elkBxdB0dnOxOPO3aP9oNUI/K4RqzuDzc5Kw79py/NJyGFvdjI72NIZt4PCt8HV1M0eMfSroESTu/Aj2j1Yj9FjuwMh/hQJzVIX4LHQ1PrJ/hJ2Jj/DESTPntTuAKWBZLBYhOztbACBoNBrBaDTO7gUHZ9JCUqVgGTNrwbfC+cpMQYkIIb3y82GTH0hs0okWXJMzjJ60YfTzo6Yp9GyRgqVhj5CuHJgMYPTUh7NhsVgEvV4/4v/ArVu3PPDKRN4FTo5BvsJqtSIvLw/h4eE4c+YMjEYjmpubodPpvLTHHnRV/QKJWTasNx7BO5kRbm+3kq/uwclR3E3a4C23cdVUiISd16Bt+haC0IebTVpc2/lTPOt2IY6p862WL5F4GLo0a8PDtqmpaUTYBgcHe2mvdwJ3w/EqvKVb7IOBCwBRSEjommDSBi/pacG+nJNY/8st0Knn4s7o9uVo2lOLUx6abWl4+C5dupThSwGPoUszJk3YAqMDt2iya2w9jchXqZBc+ifUl2ZApVBAoVDhyfxDaO++Duune5ExuHSiKuMdnBqxfFsPrI1VyH9SNWyJt8ZRM3vdRne76c5SiqoMlB49jS+n9F4WYm3hrzw/aUN/N9pNpcjIMo0aLDRobiJ220dNquJFarUaBw4cQEtLC1Qq1VD4tra2irJ/Irlg6NK0SRe2wLQDd0g3GvQH0LiwAFZBQJ+9Gv/9VD6iVU+i+NwK7LIJEBT+TEYAACAASURBVG5+jiLsR+qOj3G1/86+EtY14+Hd7egTBPTZX8PDzZsR/uzbgy3TfvS2v4O10e/imvYIbgoCBGsBFp7+FDXjpt1YU5sbeIp6rWj8UykywpKw7+JCbCp6Bsop/eJtdJ+qRPGO88gsfxkJXroHNTY2FrW1tUPhGxcXh9TUVIYvBQ6vXS0mvyPZ4JihgVRvCUfeyxxcI3Ua65qOuzLMNeF4XtSowVlOwVK5WoCyQDj+bd/g7w1b13jE67kGSQ28zphVZ6Y8kGpwoFTfJcGYGTFshalpDqS6aRGOV+YJCUgS8iqPC5abU19jamBlqoHBI8r0cuEzu3jL1rW0tAharVYAIGi1WqGlpUW0fRONBxxIJV/j37rjvz/h4eEwGAzQarX45JNPRGrZDtNQgNUv2bCh4Z9xJNOOt9a95WYSA09wrTrzOGIjHhrZmg5RIXwJ8LnFjt6es6g/ZMeScNXIa8ohKoQvuXfqu5vp3MC9VjRW5eNJdTFOz03BuzePYndmItTTmE40SJ2JekGA0GfD+4/8L/xt1BaYvPbvOpKr5VtWVoa6ujrExcVJ/v9cqh8KDAxdmra6ujqJJr1PQsHxKhT9ZCV0RXtQEG5CTuEH6JrSACTl2GCckGvVGfe6Oy/Bbr+Ezil2I09m4rmBx/rP9lIsui8cT+2Yg192/Q5bn0uYVtiOUwASt+UjDya8W3/R/VSIHuS6VJGbmwuNRiPCHomkxdD1AGHclX/892f4SNQ1a9aIdz0u6QVsSBi4hhukfAqFpXqE1xTgn7yyaoxrAnz3lJELoFItQOTULppOaZ/u5wYe666orbDa21Bb1Ic3f5AyzgCvmeoeaMV74JXccTcuQOr/21L8UGBh6NK0DR+JCkCiwTBBCIn6OUoNy9Gkz8Jm0xcebpm5JsA/j9ZzX4187V47LJ9joOU89wkkr1eNDaleOyyf35rBbieYG3i8zZVR0Gbuxl9uliEZ9Xj5vqXIKDWhvXvi7uF+axWSh6/DPEIU1ic/4ZW5haUdhEckPYYuzdjwkajAnfC1Wq0iVXA/ol7Zhcp0oCrH4Pnru3Oj8fOiFTia8xrePtU9ELy951CVuxkl4XrsfF6NIMxDwqZCpBzajNyqcwPB29sFU/FulMyw29n93MATCFEjMXM3/nLTiIwFF7EvKmrC8A1S67DT8DAOVf/5Tvd8/1U07tqNQz/Jxc9XhM6seDcYtkQDGLo0a67wra+vh91uR3h4OPLy8sQJ35AIbNj5a2TiHaSte8fDE0zMxeLMvWh6Px5f5kdhjkIBxX3/E5b4t2H5aDOiBq+fBj2SirebSrGkec3AXL33bcZnmkwYkqYxkGqECeYGnkyIGonPbUW1rQGbFl7Evh0fuwnu+xG15QA+0l7DW+o5A4N55mSiflE+mt5Zj8UeWteZYUs0kkLgRYUZc4045D/hHU6nE0ePHsWuXbtgNpuh1+uRnZ0NtVotdWkkIofDgcOHDyM3NxcAUFZWhrVr1yI01LMtaH/A84i8ePvzYOjOAg8W90aHb3FxMV555RWedP0cw3b6eB6RF4aujPFgmRxPwoGBn/PM8TwiLwxdGePBMnXjnZSzsrJ4Xc/HMWxnj+cReWHoyhgPlulzOBzYv38/tm/fDo1Gg23btiElJYXh62MYtp7D84i8MHRljAfLzFmtVlRUVMBgMDB8fQjD1vN4HpEXhq6M8WCZvdHhW1RUhKSkJKnLolEYtt7D84i8ePvz4H26JCnXIucdHR1QqVRITk7mUm8y4nQ6YTKZ8PTTTyM3Nxd6vR4WiwU5OTkMXKIZYOiSLERGRo47uxXDVxqusI2Pj0daWhoSEhJgsVhQUlLCe66JZoGhS7Iy3tSSos1uRQxbIi9j6JIsxcbG4oMPPoDRaERTU5O4U0sGIIYtkTgYuiRbwcHB0Ol0aG5uHhO+NptN6vL8AsOWSFwcvTwLHHUoLqfTiSNHjiAjIwMAR9DOBufIlg+eR+SFtwzJGA8WafD2ldkxmUwMWxnheUReGLoyxoNFWqPD12g0coKNCbS2tsJgMKCurg5arRZ6vR6xsbFSlxXweB6RF96nS+RGaGgocnJycOXKFej1eqSlpSE+Ph4mkwlOp1Pq8mSjtbUVqampiIuLAwC0tLSgtraWgUskAYYu+bywsDCUlJTAYrEgISFhRPgGMoYtkfwwdMlvuGa3slgsWLp0KdLS0gJygg2GLZF8MXTJ76jVahw4cCDgZrdi2BLJH0OX/NZ4s1ulpqb63QQbDFsi38HQJb/nCl+j0Qi73e43s1t1dnYOha3dbmfYEvkAhi4FDHezW/la+FqtVuTl5WHZsmWw2+0wGo1obm5m2BL5AIYuBRR3U0uWl5fD4XBIXd6EXGEbHh6OpqamobDV6XS8N5nIR3ByjFngTe2+zxdmt7JaraioqIDBYIBGo8G2bds4CYgf4XlEXjgjlYzxYPEf44VvVlaWpMHGsA0MPI/IC2ekIhKBa3arGzduQK/XIzc3V7LZrdiNTOS/GLpEw4SGhrqd3crb4cuwJfJ/7F6eBXYL+b/RXbx79+71+Chhh8OBXbt2wWAwAODCDYGG5xF5YfcykYRcU0t2dHRApVJ5dHYrh8OB8vJyPPDAAzAYDCgrK8ONGzfYsiXyYwxdoimIjIwcd3armYTv8LDNzc0dCtucnBxZjZomIs9j9/IssFsocLW2tuIXv/jFtBaC94Xbk0h8PI/IC28ZkjEeLIHN6XTi6NGj2LVr14Thy7ClifA8Ii8MXRnjwULA+OG7bds2AGDY0qR4HpEXhq4MOBwOXL9+fczj4eHhAACLxTLmucm6Gsn/OJ1OHDlyBBkZGSMeZ9gSANhsNty6dWvM4+7OI/feey/CwsJEqY3uYOjKgNVqHTowpqK4uBiFhYVerIjkzNWdDIBhS0MaGhqQnJw85e2NRiN0Op0XK6LxMHRlory8fKibcDI3btzgiZaIxkhNTUVdXd2k22m1WtTW1opQEY3G0JUJh8OBp59+GmazecLt+O2UiNyZaq9ZR0cHIiMjRaiIRuPkGDIRGho6NDjGHY1Gg5SUFJEqIiJfo1arodfrJ9xGr9czcP0YW7rT4HQ6sWbNGrfdQy0tLVxInIgm5HA48MADD7h9/sqVKxxAJSG2dGUkODjY7bfU7OxsBi4RTSo0NBTV1dXjPldWVsbA9XNs6c5AXl7e0OT0LhaLhbcJEdGUOJ1OxMfHjxgjotFo0NzczHm3JcaWrgxt2rRpxN/LysoYuEQ0ZcHBwdi7d++Ix4qKihi4AYChOwNhYWEoKysDMPDtdO3atRJXRES+JjY2FtnZ2QAGbhFKSkqSuCISgyy6l51OJ65cuSJ1GdPy3XffYd26dXjppZcC6mDhLDkkR62trYiLi5O6DJoCuU8e5O3u5bu88qrTdPr0aZ89YKY6YYa/KCsrQ05OjtRlEI3Q0dEBYOA+eV/T1dWFxYsXS12G19ntduTm5iI+Pl7qUiQli9Btbm6GRqPB+++/L3Up5MalS5eQnJyMZcuWSV0K0RjHjh1DcXExJ6aRMZPJBAB4/PHHJa5EWrII3draWqSmpnIwkoydO3cOAA8Ykh+bzYa6ujq8+uqrUpdCEzh58iS0Wm3AT5Er+UAqm80Gs9mM6OhoqUuhCRw9ehTZ2dkBf8CQ/Pzrv/4rAODHP/6xxJXQRAwGA1atWiV1GZKTPHRdBwxDV76cTicqKiqwcuVKqUshGqOtrQ0ajYYD/GSss7MTAHx27I4nSR66x44dY5eDzLnW+XziiSckroRorNraWqSnp0tdBk3g7NmzADCtJVL9leShyy4H+eMBQ3JltVphNps5wE/mTpw4gezsbE7+AYlD12q1AgAPGJkzmUzQ6/U8YEh2OMBP/nh5aiRJQ/fkyZMAgOXLl0tZBk3A4XCgrq4OMTExUpdCNAZHxMrf6dOnAYDnkEGShi67HOTv/PnzAICIiAiJKyEayel0wmAw8N5cmXNNXMJbQgdIFrrscvANHR0d0Gg0PGBIdjjAzzccO3bM7ZKogUiy0OUB4xtqamqQmpoqdRlEY3CAn/y5Lk9xsOwdkoVuS0sLACAyMlKqEmgSnLiE5IyXp+Svra0NACcuGU6y0GWXg/xxph+SK9flqZSUFKlLoQlw4pKxJAldjoj1DW1tbdBqtTxgSHZcI2I5wE/eXPPq0x2ShK5rROyKFSuk2D1N0fbt23kthmSJI2Llz3V5KtCX8htNktB1jYhlC0q+OHEJyZlrKT+SL9flKU5cMpIkocsRsfLHmX5IrlyXpzjAT944r/74RA9djoj1DSdPnuRSfiRLHBHrGziv/vhED10u5Sd/rpl+OHEJyRFHxMqf6/IUl/IbS/TQdY2IZQtKvjhxCcmZx5by67Wi0XQA+U+qoFAoBn6ezEdVXTu6+2fygv3otdajtPAwrDP6/Un0WvFpaTGqrd+5r8BahWSFCslVXRhTQu85VGUsgUKVhaquHi8UeIdrXn1OXDKW6KHrnRGxPbA2mlCVn3zn4FEkI7/qY7R3357B6wX2wcOZfkiuXJenZjvAr7/7UxQ+m4B1db1Y9W4XBEGAIHwPe+nfwmHaANVTv0bjtM8dDpyq3A59u/vjehYVo+fUQWToz6BvJr/eew5VG9cg6/KzMDbtRebiuZ4ucAROXOKeqKHrlRGx/VfRWLga4evq4FhVhpuCAEEQ0Gffib91HMGzqmdR2Hh1bHBNKLAPnhMnTnApP5KlU6dOAZjlAL/eU9izNgMHF5bD/Ltf4Cdq1zF0N5RROmx9pxIGvIt1Oz7GVW986Rbb0DnjORw//Dp0au8GLufVn5iooesaEeu5pfy+Qfuel/HUwf8Go/kAtv5EjZDBZ4KUUdBtfRsfGf4PvLXuLdRenUmLV2ZEOHgcDgcqKio4cQnJ0uyX8utHz6la7GmKQ1H+T/HIeGfAkGi89MsNQNVO7Gu6PvA7jYVQKaKR33h92IbX0ZgfDYWqEI09/47G/KfxVEk70JCF8DnRyG/898Hfex4HGj/F3owld3rhqk/d6cLuaUS+SgFVfiPu9FsN3+e/o6dxBxY/9Ra6cQRZ4cGjtp3AiHNGARKVd8/w323qXJeneA4Zn6ihe/ToUc92OfScxh/3nEZSUQ5SHxnvP9P9iHppC/LwDnL2taCHB8+kuJQfydnsR8Q60FbfgG7lIix42N0xFIS50auwXtmOQ/Vnp3Z8Yh4Sd3+C43lRQFIlLH1t2J04b/C5I3hp3fvAxgb0CX24aXkZOLgWa/ecQu+UXjsIcxOL0HW8AEqsRqXFCfvuREz6lVuCwAXuzKvPiUvGJ1roer7LoR89bcdwqFuFyAXz3L+RuU8geX0Uug8dQ1vPVPqKAvvg4VJ+JFednZ0AZjkitv86LnXagSWLEBYy+emvu/MSvpx1F3MEMsvfwOYVSgQhCCHqVGz/5fOw7KnFqSmdk2bAYUblxjXIqjnnndefAOfVn5hooev5EbG38eWlC+jGQoSHhUy+efcFXPpytl3M/n/wHDt2jBOXkCz57gC/xxEb8dCwk20QQsIWYUl3A+rbHF7YXzca9Ol46fJzaPjs98i0vC7a9WnOqz850ULXdw+Y4fz74LHZbJzph2TLIyNig+ZhQaQK+PwCbL2TH0jKyAV42GtnSTs6L12f5iDPKUp4HccPF+AnK55D0fuvI7zqVyg8eG6KPXIzx3n1Jyda6JpMJg+PiL0bDy9YBCUuwmKbwn+lCa/hzJZ/HDxcyo/kynOXp0IRnZwE5YQ9X65LV1FYn/zE5Jd/ZmySS2MzpkTS+heQoLwbwN1QJm5GqeEx1GRtw/72bzy+t+Gam5s5cckkRAld73Q5uAY7TBJ4PWdRf6gdyvWrED3XW2/XPw4ezvRDcuVaym/255AgzF2Rii0JLdix+/8Zv9eotw3vvXkQyCzEpoR5GOrRmvE+RzcM+tFru4DPlUlIjg4FQlQIX6Kc8atP7n5EvfIaDAmnoX92B0xevJODS/lNTpTQ9dqI2LnL8fyW5WjYUe7mlqBv0P7eHpRgI8o3xWEuD54JeWymHyIP8+hSfiErsOVwNTZczIHm53vxqdU1Pvk2uttNKN2YBT1exvtFzwzdUhS06O/wQpIdh6r/jK7efgA9sJr24M2S9uEvjLDwhYN//g69vf85+Od2lLy5ByZrD4B+9HYdQu66j5BS/jIS5gYBQQsQ90Icug/9Hn/qGtzGWoviNw+ie+i1h5+7+nGr99b0etZCovHKb99COt5BzmveuUTFefWnRpTQ9d6I2PsRteVdHN/wb0jTvIjST61D3a793e0wlW7Gs/r/QMH7BUO3FPHgGZ/VavXITD9E3uDpEbFByp9g5/F2fKQLwbGXFw/eAvg3UG39DKG6g7Af/9XIuwSC1Hi+7HfYglI8ft8cKBSrUXkzCb98b/WwV70Hi1JeRMHtHQifsxBpf7wwOAFOEvLWP46LxbFQKObgvsRGLKk24m3dY4Mn4Hugfr4IDVv+Ezse/wEUijA8W/n/Ie2X25E0vOZFycgv+BZZ4f8F/yXtD7gwrWM/CCGL12Bn+UagKgfrpnzHxdRxXv0pEkSg0WiE4uJiL+7he8He9pFQmZckABj8SRLyKj8S2uzfj9q2T7hp+UQwpEfc2e7gX4Tj760WoCwQjn/bN7CVvUEoSFAKgFJIqvx/BcfxAkGJJCGvsubO7yrTBUODRbg5/OVvWoQGQ7qgBARAKSTk1QifHS8XkhAl5B2/NliCTTheMFhrUqVg6Rv7jvoslUISlEJS5Xlh7NPfCzbjRkEJpZBg+Gzk/mfIaDQKAIQbN2544NWIPOfGjRsCAKG+vl7qUqapT/j2eIGgxGqh0uKUuhiv0+v1glarlbqMWXNliLcoBnfiNTabDfPnz0d9fT2SkpIm/wVZ6h+c1OICiiw1yFTfI3VBHpeXlwer1Yra2lqpSyEaoaGhAcnJybhy5YqPjTfw//PGcAqFAmVlZcjJyZG6lFlRKBQAAG9Fo9e7lzkiVv5cS/npdDqpSyEagwP85M8r8+r7qbu8vQPXUn48YOSLS/mRnPnuiNggzE3cCbtX+xLlwbWUn+fm1fdfXm/pemcpP7G5Dp4/+mUXkX9MXEL+yDUiNj4+XupSaAJcym/qvBq67HLwDTxgSK48spQfeRWX8pser4auayk/HjDy5VrKLyUlRepSiMaY/VJ+5G28PDU9Xg1d11J+PGDki0v5kZzNfik/8jbXUn6RkZESV+IbvBa67HLwDR6d6YfIg1yXp2a1lB95HZfymx6vhS67HHzDsWPHUFxcLHUZRGO4RsRygJ98cSm/6fNa6HJErPxxKT+SMw7wkz8u5Td9XgvdEydOeHgpP/I0TlxCcsXLU77BNa8+52GYOq+ErmtELLsc5I0z/ZBcuS5P8RwibzU1NT46cYl0vBK6HBHrG7iUH8mVa0QsB/jJF5fymxmvhK73lvIjT3EdMJy4hOSII2Llj0v5zYxXQvfYsWPscpA5zvRDcuUaEcv7c+XNNa8+52GYHo+HLkfE+gbO9ENy5bo8xQF+8uYf8+qLz+OhyxGxvoFL+ZFcNTc3c4CfzHFe/ZnzeOhyKT/56+zsBMCJS0iefHcpv8DhmlefS/lNn8dDt7a2ll0OMseJS0iuOCLWN7jm1ec8DNPn0dC1Wq0cEesDONMPyRVHxMofJy6ZHY+GLpfykz/XAcOl/EiOjh07xgF+Msd59WfnLk++GEfEyt/p06cBcOISkieDwQCtVguTySR1KeQGF6KYHYUgCIInXsjpdOLee++FRqNBQkKCJ15S9gwGAwD41E38VqsVdXV18NDHTuQxVquVJ3IfodfrUVJSInUZXqFQKADAa+dIj7V0b9y44VPhE6jUajWqq6ulLoNoDLVaHXBfBr19gif58VhLNxDxgCGi2eA5RH68/Zl4bWk/IiIiGomhS0REJBKGLhERkUgYukRERCJh6BIREYmEoUtERCQShi4REZFIGLpEREQiYegSERGJhKFLREQkEoYuERGRSBi6REREImHoEhERiYShS0REJBKGLhERkUgYukRERCJh6BIREYmEoUtERCQShi4REZFIGLpEREQiuUvqAnyB1WpFRUWF2+fz8vJG/P3RRx9FTk6Ot8siIh/R2dmJw4cPu32e55DAoRAEQZC6CF+QmpqKurq6KW3b0dGByMhIL1dERL5kOucQi8UCtVrt5YpoPAqFAgDgrWhk9/IUlZSUTGk7vV7PwCWiMV5//fUpbafX6xm4foyhO0VqtRp6vX7S7TZt2iRCNUTkayIjI6d0Dtm2bZsI1ZBU2L08DQ6HAw888IDb56urq5Geni5iRUTkS2w2G+bPn+/2eaPRCJ1OJ2JFNBq7l2UkNDQURqNx3Oc0Gg1Wr14tckVE5EvCwsJQXV097nMajQYpKSkiV0RiY0t3mpxOJ+Lj42E2m0c8Xl9fj6SkJImqIiJf4e4c0tLSgtjYWImqIhe2dGUmODgYe/fuHfGYVqtl4BLRlAQHB6OoqGjEY9nZ2QzcAMGW7gy9+OKLQ/fucng/EU3X8FuIeA6RD7Z0Zco1CpHD+4loJly3IRYXF/McEkDY0p0mq9U69Of9+/fjH/7hH3D//fcDAObNm4fQ0FCpSiMiH7Nz50688sorPG/IiLdbugEfularFbdu3cLFixcBAF1dXfjmm28AAAaDYdavn52djR/+8IcAgIiICISEhOChhx7Cgw8+yJAmmgbXyZDkz5djhaHrIVarFZcuXcKXX36Jc+fOoampaczoQWD8kBwuIiLC7T6uXbuGr776asRjw0N8on3+6Ec/wuLFi7Fw4UI8+uijDGOiURi6vsOXY4WhOwMOhwPnz59HR0cHzpw5M2axAr1ej0cffRQqlWooRMW8puJwOHD9+nVcunQJvb29OHnyJKxW64h5WTUaDRISEhATE4OIiAhe86GA5+2TIc2eP3xGDN0pcDqdOH36NDo6OnDs2LER4aXX6xETE+MzLUhXi9xqtY77XlatWoUf//jHCAsLk7BKIvH5wwnd3/nDZ8TQdcMVtM3Nzdi+ffvQ48XFxYiOjvabYHI6nbBYLGhpaRkRwlqtFjqdDomJiX7xPokm4w8ndH/nD58RQ3eU1tbWEUGr1WqxatUqxMXFBcTqPg6HA21tbThy5MhQt7krgJ955hnZt+SJZsofTuj+zh8+I4YuBoLm448/Rnl5OcxmMzQaDdLT05GUlBTQ1zrHC2C9Xo+1a9cGxBcQCiz+cEL3d/7wGQV06DocDhw+fBi5ubkABgJFq9VyurRx2Gw21NbWoqamBmazGVqtFnq9nv9W5Df84YTu7/zhMwrI0B0dtmVlZUhNTeW1yylwOp04ceIEfvOb36Curo7hS37DH07o/s4fPqOACl2n04mjR48iLS0NwEDYrl27ltcpZ6i1tRUGgwF1dXXIzs7mlJXk0/zhhO7v/OEzCpi5lzs7O7FmzRqkpaVBr9fjxo0byMnJYeDOQmxsLGpra2E0GnHmzBmEh4ejpqYGTqdT6tKIiAKS5KHrdDpRXl6OZcuWwW63o6OjAyUlJQxbD9LpdGhubkZxcTEyMjKwZs0a2Gw2qcsiIgo4koauw+HApk2bkJubi+LiYjQ3N3PUrZcEBwejsLAQHR0dsNvtmD9/PlpbW6Uui4gooEh2Tddms0Gn08FsNqOlpYUDfUTkcDiQn5+PiooKlJWVIScnR+qSiCblD9cL/Z0/fEbefg93eeVVJ+EKXAC4cuUKRyWLLDQ0FPv27cPSpUuHRogzeImIvE/00B0euCaTiYErkeDg4KGgZfASEYlD1NB1Op1DJ3YGrjwMD95ly5axm5+IyItEvaa7c+dObN++HR0dHRwwJSNOpxObNm3CmTNn+GWIZMsfrhf6O3/4jPzmPl2r1Yrt27ejurpafoHb04h8lQIKxQQ/yVWw9ntiPyokV3Vhti/lScHBwdi9ezcAYN++fRJXQ0Tkv0QL3by8PGi1WqxevVqsXU6bMu84vhUECOP91GdCLfldzd4TGhqKbdu2wWAwwGq1Sl0OEZFfEiVGrFYr6urqoNfrERwcLMYuaQZSUlKg0WiGViwiIiLPEiV0GxoaoNFosHz5cjF2512uLuIDn+JUdT6eHOx+VmXsxafWnmEb3kZ3uwmlGUsGuqdVGSg9ehpfSlb45Fwjmg0GAxwOh9TlEBH5HVFCt6amBqmpqX7Uyu1Gw0ulMN73c3wkCBD6bHj/kU+Q9HIl2nv7AfSjt/0drI1+F9e0R3BTECBYC7Dw9Keo6Za69oklJiYCAM6fPy9xJURE/sfroetwOGA2mxEdHe3tXc1ad8lT+MG4A6mikd94fcS2yrx8bNctRggABCkRvSoKyqZ/wRn7bQAOnPrj72EZvk3IYui25yNPKf77mg7XyOWvvvpK4kqIZq/fWoVkhZvBi73nUJWxBApVFqq6esb79Vn4Dtaq56FQPI8q63ejq0JvVzUyVAqoMqrR1TuNYZWjBmMOvL+x56fp6YG1sQr5T6qGznlje+7IU7weutevD/xnWLBggbd3NWvuB1K1YXfivAl+MwghYYuwBBdhsfUCPWdRf8iOJeGqgcB1CVEhfMm9Xn4Xs6fX63Hy5EmpyyDynt5zqNq4BlmXn4WxaS8yF88Vacf96O06hI2JBbi83oimd9ZjccjMT8NB6kzUT3p+mshtXDUVImFdMx7e3Y4+QYDQZ0dtZCcyEgphunp7xrXR+Px4PK50+r+8hE6ZdyMTBayhwH0Oxw+/Dp1agsDdUI3Db+mgnkXgeqaki6h/1wSsz0DWCuVAIAQpsWJzAYqWmJCzrwVs73oWQ9cLgh5egEiZdyNP5Ouvv5a6BCLvGBG4BUhU3i3SjkcFbtFPoJz07Dv5YMzh3csDfx6nO7unEfkqN13QQYuRWW+HfXcixvvq0d15CV/KaVIBP+D10J0/fz4A4Ny5c97elXzMfQLJsJEUVwAACSNJREFU61X43GJH7/DHe+2wfH5LqqqmrKKiAjExMVKXQeRZPhW40x+MGbTo7/BC0ml82HJp2PXrfvS0HcMhJCE5erprlN+LpBf+DovYNPMor/9zBgcHQ6vVoqury9u7kpF5SNhUiJRDm5FbdW4geHu7YCrejRKZdzt3dnYCABYuXChxJUQe5DCjcuMaZNVM48t/twkZE81Sp1BAoXgSpe29E7zIf8Bx9nfYmLhhmncuzGAwZtACxL2wHA2HjqJjaHCWA231DcD6VYieO9XT/W1crS3Hjs+fxsvJC9kd6mGi/HvqdDps374dTqdTjN3NmPvRywo3oxDdC3okFW83lWJJ8xrcp1BAcd9mfKbJhCFJ3gOpWlpaAADh4eESV0LkKd1o0KfjpcvPoeGz3yPT8jrW7fgYVyfrNlXqUO1uhrqhn79ga1TIBC9SC/3qYlze8BE+O6KD5a0t2FH7xeTTwM5oMOY9WJT8D8i0/B5/PDVwn33/1X/G7w89jC3PLx+3+3isfvR2fYDCnH/DhvcLkPqIWL0BAUQQwZUrVwQAgtFoFGN3NEO3bt0SAAhlZWVSl0I0BgBhuqesPkulkAQISHhdOG7/XhCE7wX78deFBEQI6ZWfCze9U6ogCE7BUrlaAJRCQkGDYO8TBKHPJhwvSBKgzBQqz387hbqVQlLleaFvxBPnhcqk/zr0+MB2UULe8WuDG1wTjudFCcq848K3rhqSKgVL39h9jLNX4eb5g0K6cub/NjP5jOTG2+9BlJZuWFgY9Ho9du3axZmOZKyyshIAsHbtWokrIfIkJZLWv4AE5d0A7oYycTNKDY+hJmsb9rd/4/7XPNK9HIf1G1YOXMMNegSJhW/CEH4UWf/kmkhnfDMfjBmK6OQk4NAxtH1zES0fnp7iddnb6D71W2xMLAWKPsA7mRGYqP1OMydad/2mTZtgNpuxa9cusXZJ02C1WpGbm4uysjKEhk53wAWRL7kfUa+8BkPCaeif3eH+XlSPdC+PEhKNV0r1SGjagmc317rv4p7xYMwgzI1ehfVowJ8r/oAPG5bjhbgFE5/o+6+isfBZqP72f+Hh8iMMXC8TLXTDwsJgNBphMBhgMpnE2i1NgdPpxLp166DRaJCVlSV1OUTeFxKNV377FtLxDnJem8L1XY8JQkhUFn5bmQlU/Qqvub2+O4vBmHOX4/ktD2OP/k00JD2NuEX3TLDxN2jf8zKeesuOTON7eMs1aIu8RtSBaTqdDnq9HmlpaWhtbRVz1+SGawF7s9kMk8nkR/NjE00kCCGL12Bn+UagKgfr9pzCRJ3EnjUXize8hvJMoCptI/a46eKe+WDM+7Hs73VIQgQyX35qwq7lfqsJhfo/AziHqrQFmDO6+1xViMYe3qjrSYrBC8eicZ3kKyoq0NLSgtjYWDF3T8PwsyBfolAoAAAin7JoGvzhM/L2e7jLK686geDgYOzbtw8AEBcXh/r6eiQlJYldRsBzOBzIz89n4BIRiUj00AVGBm9ycjLKysqQlZXFrk2RWK1WrFu3DmazmYFLRCQiySYbcQVvWVkZcnNzsWnTJthsNqnKCQhOpxM1NTVDE19cuXKFgUtEJCJJZ/gKDg5GTk4OWlpacObMGcyfPx81NTWyn7nKF1mtVqxZswYZGRkoLi5Gc3Pz0Nq5REQkDllMqxkbG4tPPvkEer0eGRkZiI+P5+hmD3E4HNi5cyfCw8Nht9vR0tKCwsJCduUTEUlAFqELAKGhoSgpKUFHRwdUKhXi4uKQmprK8J0hh8OB8vJyPPDAA9i+fTvKysrQ3NzM7mQiIgmJfsvQVDU0NGDHjh0wm83QarXQ6/VYvnw5W2iTsFqtaGhoQG5uLgCgrKwMa9eu5SxT5PP84XYUf+cPn5G334NsQ9eltbUVBoMBdXV10Gg0SE9PR2pqKq9HDuN0OnH69GkcPHgQFRUVABi25H/84YTu7/zhMwr40HXp7OzE4cOHYTAYAADZ2dlYvXo1oqOjAzZYOjs70dLSMtSq1Wq1SE9PR0JCQsD+m5D/8ocTur/zh8+IoTuKw+FAU1MTampqUFdXB2AggFNSUrBixQq/bgG7WrTNzc2ora2F2WwGMNCqjYuLQ2RkpMQVEnmPP5zQ/Z0/fEYM3QnYbDY0NjbixIkTQ92qGo0GqampiI6Oxo9//GOfDmGn0wmLxYKzZ8+OeY/p6elYtmwZr3NTwPCHE7q/84fPiKE7RQ6HA+fPnx/TCgQAvV6PmJgYPPTQQ3jsscdkGcROpxNXrlzBuXPn0NXVhVOnTg215IGB1vzKlSsRExMDtVotYaVE0vCHE7q/84fPiKE7QzabDV988QU6Ojpw5syZoVaiS3Z2Nn74wx8iJiYGISEhWLBgAQB4NdAcDgeuX7+Oa9eu4auvvoLd/v+3d8eqiUVRGEa3kEpSWCiCoLXFbQVLX8r3s8wDWIilxbSmsHcqkziTGQjM/OrJWpV2Rw6cz33Re3/U4XCozWZz9SVhsVjUarWq5XJZXdfVdDo1zfLttXCgt66FPRLdf2i/378F7+XlpY7H428x/mi9Xl+977qunp///rTJ3W5Xr6/vj+ra7/dXE+tHl7jOZrOaTCbVdV0Nh0M/goJPtHCgt66FPRLdgMul3aqq7XZbVVWn0+nt9cWvE+lnLhP0xWAwqPl8XlVV4/G4RqNR9fv9u7zEDfeshQO9dS3skegCVBsHeuta2KP//Rnu5jaQANA60QWAENEFgBDRBYAQ0QWAENEFgJCnWy8A4Csuf+mAR2TSBYAQky7wEB75hgt/0sLNJPgaky4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEiC4AhIguAISILgCEPN16AQDfXa/Xu/USCDHpAkCISRfgRs7n862XQJhJFwBCRBcAQkQXAEJEFwBCRBcAQkQXAEJEFwBCRBcAQkQXAEJ+AqW4hThxMJmFAAAAAElFTkSuQmCC\"/></p>\n<p style=\"text-align:left;\">Determine the outputs that will be produced by this algorithm.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>47, 23, 11<br/><em>Award <strong>[1]</strong> per output</em>.<br/><em><strong>Note</strong>: Allow FT if either of the first two outputs are wrong</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.8",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Copy and complete the following truth table where:</p>\n<p style=\"text-align:center;\">X = A XOR B<br/>Y = A NOR C<br/>Z = X OR NOT Y</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for all 8 input values correct;</em><br/><em>Award <strong>[1]</strong> for correct X column;</em><br/><em>Award <strong>[1]</strong> for correct Y column;</em><br/><em>Award <strong>[1]</strong> for correct Z column;</em><br/><em>Allow follow through from incorrect columns X or Y</em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Generally, well answered, with many full or near full marks awarded. Some candidates lost marks by not giving a sufficient number of input combinations, so that instead of there being 8 possible input combinations, only 6 or 7 were supplied. Other candidates, due to not systematically filling the input columns, but using a more random approach, may have provided 8 input combinations, but at least one was a repeat of another.</p>\n</div>",
    "question_id": "19N.1.SL.TZ0.5",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A bus company provides services within a city. The basic fare depends on the travel distance between the departure station and the destination station.</p>\n<p>The cost per kilometre is € 0.20.</p>\n<p>Children under the age of 5 can travel for free.</p>\n<p>Children between the ages of 5 and 15 <strong>inclusive</strong> can travel with a child age ticket, which gives a 50 % discount off the kilometre fare.</p>\n<p>The senior discount (age 65+) offers 30 % off the kilometre fare.</p>\n<p>The sub-program <code>costperkm(AGE)</code> accepts an integer <code>AGE</code> (the age of a passenger), then calculates and returns the cost per kilometre.</p>\n<p>For example,</p>\n<p><code>costperkm(10)</code> returns 0.1,<br/><code>costperkm(20)</code> returns 0.2,<br/><code>costperkm(80)</code> returns 0.14</p>\n</div><div class=\"specification\">\n<p>Passengers can find the distance between any two bus stations.</p>\n<p><strong>Figure 1</strong> shows the one-dimensional array <code>NAMES</code>. It is used to store the names of all bus stations on the route from Oppox to Dovely.</p>\n<p><strong>Figure 2</strong> shows the one-dimensional array <code>DISTANCES</code>. It is used to store the distances (in kilometres) on the route from Oppox to Dovely.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\"><code>DISTANCES[K]</code> holds the distance between the bus stations <code>NAMES[K-1]</code> and <code>NAMES[K]</code>.</p>\n<p style=\"text-align: left;\">For example, the distance between Kronos (<code>NAMES[7]</code>) and Longlines (<code>NAMES[8]</code>) is 1.2 km and can be found in <code>DISTANCES[8]</code>.</p>\n</div><div class=\"specification\">\n<p>The sub-program <code>calcdistance(P1,P2)</code> accepts the indexes of the names of two bus stations in the array <code>NAMES</code>, where index <code>P1</code> is always less than index <code>P2</code>, and returns the distance between them.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm in pseudocode for the sub-program <code>costperkm(AGE)</code>.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the distance between Kiko and Endsley.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the distance between Oppox and Brinkley.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how the distance between the two bus stations can be calculated in this sub‑program.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>An algorithm is needed that inputs the names of the two bus stations and the age of the passenger. It then calculates and outputs the price of a ticket.</p>\n<p>If any of the inputted names are not found, the algorithm outputs an appropriate message. <strong>Figure 3</strong> shows three examples of inputs and outputs.</p>\n<p style=\"text-align:center;\"><strong>Figure 3: Three examples of inputs and outputs from the algorithm</strong></p>\n<p style=\"text-align:center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align:left;\">Construct an algorithm as described. You should call sub-programs <code>costperkm()</code> and <code>calcdistance()</code> in your algorithm.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em></p>\n<p><em><strong>Award [1]</strong></em> <em>mark for correct condition (in if statement) and correct calculation of the cost per km for each type of tickets, <strong>x4</strong></em>.</p>\n<p><span style=\"text-decoration:underline;\">Example 1</span>:</p>\n<pre>costperkm(AGE)<br/>         if AGE&lt;5<br/>           then COST=0.0<br/>         else<br/>             if AGE&lt;=15<br/>                 then COST=0.20*0.50          // COST=0.20*50/100<br/>                 else<br/>                    if AGE&lt;65                  //accept AGE&lt;=65<br/>                         then COST=0.20<br/>                         else COST=0.20*0.70     // COST=0.20*70/100<br/>                    endif<br/>             endif<br/>         endif<br/>         return COST<br/>end costperkm</pre>\n<p><span style=\"text-decoration:underline;\">Example 2</span>:</p>\n<pre>costperkm(AGE)<br/>         if AGE&lt;5<br/>           then COST=0.0<br/>         endif<br/>         if AGE&gt;=5 and AGE&lt;=15<br/>            then COST=0.20*0.50        // COST=0.20*50/100<br/>         endif<br/>         if AGE&gt;=15 and AGE&lt;65<br/>                        //accept AGE&gt;=15 and AGE&lt;=65<br/>             then COST=0.20<br/>         endif<br/>         if AGE&gt;=65<br/>                     //if condition in the previous if statement is<br/>                     //AGE&gt;=15 and AGE&lt;=65, then this condition<br/>                     //should be AGE&gt;65<br/>             then COST=0.20*0.70 // COST=0.20*(100-30)/100<br/>          endif<br/>          return COST<br/>end costperkm</pre>\n<p><em><strong>Note</strong>: The method heading and the return statement may not appear in the candidates’ responses</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>1.3;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>1.2 + 1.0 or 2.2;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3] </strong></em><br/>The sum of elements;<br/>in the array <code>DISTANCES</code> from <code>P1+1</code>;<br/>to <code>P2</code>;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em><br/><em>Award <strong>[1]</strong> for all variables correctly declared and initialized;</em><br/><em>Award <strong>[1]</strong> for any correct loop through the array NAMES;</em><br/><em>Award <strong>[1]</strong> for to determine positions of <strong>both</strong> names in the array;</em><br/><em>Award <strong>[1]</strong> for outputting a message if one <strong>or</strong> other not present;</em><br/><em>Award <strong>[1]</strong> for comparison of positions to find smallest/largest;</em><br/><em>Award <strong>[1]</strong> for correct calculation and output of the price;</em><br/><em>Award <strong>[1]</strong> for correct invocation of methods</em> <code>calcdistance()</code> and <code>costperkm()</code></p>\n<p><span style=\"text-decoration:underline;\">Example 1</span>:</p>\n<pre>NAME1=input()<br/>NAME2=input()<br/>AGE= input()<br/>POS1=-1<br/>POS2=-1<br/>K=0<br/>loop while K&lt;=9 and (POS1==-1 or POS2==-1)<br/>      if NAMES[K].equals(NAME1) //<strong>accept</strong> <strong>'</strong>==<strong>' instead of</strong> equals()<br/>        then POS1=K<br/>      end if<br/>      if NAMES[K].equals(NAME2)<br/>         then POS2=K<br/>      end if<br/>      K=K+1<br/>end loop<br/>if POS1==-1 OR POS2==-1<br/>   then<br/>      output('stations are not found')<br/>   else<br/>      if POS1 &gt; POS2<br/>         then<br/>           output( calcdistance(POS2,POS1)* costperkm(AGE))<br/>         else<br/>           output( calcdistance(POS1,POS2)* costperkm(AGE))<br/>      end if<br/>end if</pre>\n<p><span style=\"text-decoration:underline;\">Example 2</span>:</p>\n<pre>ST1=input()<br/>ST2=input()<br/>AGE= input()<br/>PS1=-1<br/>PS2=-1<br/>loop K from 0 to 9<br/>    if NAMES[K].equals(ST1) //accept '==' instead of equals()<br/>      then PS1=K<br/>    end if<br/>    if NAMES [K].equals(ST2)<br/>       then PS2=K<br/>    end if<br/>end loop<br/><br/><br/>if PS1!=-1 AND PS2!=-1<br/>  then<br/>         if PS1 &lt; PS2 <br/>               then T=PS1 <br/>                    PS1=PS2 <br/>                    PS2=T <br/>         end if<br/>         PRICE= calcdistance(POS1,POS2)* costperkm(AGE)<br/>         output(PRICE)<br/>   else<br/>         output('stations not found')<br/>end if</pre>\n<p><span style=\"text-decoration:underline;\">Example 3</span>:</p>\n<p><strong>Note</strong>: <em>Award marks for the algorithm written in Java/Python/Javascript/any other programming language. The following example is the solution written in Javascript</em>.</p>\n<pre>function findStation(station)<br/>{ var found = false;<br/>  var i = 0;<br/>  do<br/>  { found = (ROUTE_X_NAMES[i] == station);<br/>    if (!found) i = i + 1;<br/>  } while (!found &amp;&amp; i &lt; 10);<br/>  if (found) return i;<br/>  else<br/>  { output(\"No such bus station as \"+station);<br/>    return -1;<br/>  }<br/>}<br/><br/>var station1 = input();<br/>  var P1 = findStation(station1);<br/>  var station2 = input();<br/>  var P2 = findStation(station2);<br/>  var AGE = input();<br/>  if (P1 &gt;= 0 &amp;&amp; P2 &gt;=0)<br/>  { if (P1 &lt; P2)<br/>      output(\"Cost = \"+costperkm(AGE)*calcdist(P1, P2));<br/>    else<br/>      output(\"Cost = \"+costperkm(AGE)*calcdist(P2, P1));<br/>}</pre>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "",
    "question_id": "21N.1.SL.TZ0.11",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"question\">\n<p>State the hexadecimal representation of the binary number 10001010.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max]</strong></em></p>\n<p>8A;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates correctly stated the hexadecimal representation of the binary number 10001010.</p>\n</div>",
    "question_id": "21N.1.HL.TZ0.2",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>For an identified application, explain why a binary search would be preferred to a linear search.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>For example, <strong>[2 max]</strong> if no application given</em>;<br/>Searching through a database of names;<br/>That contains a large amount of data;<br/>That is already sorted;<br/>And needs to be searched in the least amount of time;<br/>Is faster because binary search divides and searches smaller blocks of data/does not have to compare each element in the list;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.SL.TZ0.9",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A medical centre uses a computer system to manage both patients’ data and appointments. This system, which is used by the doctors, nurses and secretaries, has two unordered files: a patients’ file and an appointments’ file, both of which can only be accessed sequentially.</p>\n<p>Every evening the following processing takes place:</p>\n<ul>\n<li>a list of appointments for the next day is printed out</li>\n<li>reminders are sent by SMS text messages to the patients’ mobile devices.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the pseudocode that the processing must follow when the system sends out the text reminders.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> different methods that the medical centre could use that would allow data to be restored should it be lost for any reason.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The medical centre is concerned about the privacy of the data it is storing and has to make decisions concerning:</p>\n<ul>\n<li>access to the data stored on this system</li>\n<li>storing the data locally or through the use of a cloud service.</li>\n</ul>\n<p>Discuss the issues that should be considered before making these decisions.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Award marks for a response which indicates the logical steps that have to be followed.</p>\n<p><em>Award <strong>[5 max]</strong> as follows</em>:<br/>Iterate through the appointments file;<br/>Check for correct day;<br/>Repeat for each appointment on that day;<br/>Using the patient ID for that appointment;<br/>Iterate through the patients file until record for that ID found;<br/>Retrieve phone number and send out SMS;</p>\n<p><em><span style=\"text-decoration:underline;\">Example</span>:</em></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em><strong>Note</strong>: Candidates are not requested to construct the (algorithm in) pseudocode</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for method and <strong>[1]</strong> for description – only accept TWO methods.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Backup;<br/>Data files on a regular basis;</p>\n<p>Printed copies;<br/>Printouts can be kept of transactions;</p>\n<p>Transaction Log file;<br/>Written for each transaction can be used to restore;</p>\n<p><em>Accept any reasonable methods described including second server and cloud use</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>There are 2 possible issues here: who has what level of access to the data in the hospital and whether storing in the cloud is safer than storing locally</em>.</p>\n<p><em>For each of these 2 issues award <strong>[3 max]</strong> as follows</em>:<br/><em>Award <strong>[1]</strong> for identifying the issue.</em><br/><em>Award <strong>[1]</strong> for some valid development of the issue.</em><br/><em>Award <strong>[1]</strong> for a suitable discussion</em>.</p>\n<p><em>Example answers <strong>could</strong> include reference to the following but this is not an exclusive list. Award marks for any two reasonable issues discussed – one of which is access and the other security</em>.</p>\n<p><em><strong>Official access to the data [3 max]</strong></em>:<br/>Access to this sensitive data must be restricted.<br/>Only those directly concerned can be able to access it.<br/>Even less people should be able to edit it.<br/>Therefore access levels should be set up, with strong levels of authentication.<br/>Physical access to servers should be controlled if using the local system;</p>\n<p><em><strong>Data security [3 max]</strong></em>:<br/>Is the data safer stored locally or on the cloud?<br/>Cloud service providers are professionals – they should have stronger security than a hospital system.<br/>What track record / reputation does the cloud service provider have?<br/>If patient data could be sold/inspected, then both the patient and hospital could suffer serious consequences.<br/>Is the cloud governed by appropriate privacy laws?<br/>Is it located internationally or is it governed by the laws of the country in question?<br/>Could be intercepted in transmission;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18M.1.SL.TZ0.10",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming",
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "1-1-systems-in-organizations",
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain why abstraction is required in the design of algorithms.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Abstraction allows us to create a general idea of what the problem is and how to solve it;<br/>Abstraction removes all specific detail, and any patterns that will not help in solving a problem. This helps in forming a “model” (If designers don’t abstract they may end up with the wrong solution to the problem they are trying to solve);<br/>Abstraction is widely used because there exist a number of “patterns” in programming that keeps repeating in every application/program;<br/>The pattern corresponding to an issue can be found, then the abstract solution to it can be found and implemented, and the problem is solved;<br/>Most programming languages provide some built-in abstract patterns, which are easy to use (some API provides more advanced patterns);</p>\n<p>Abstraction is the process of taking away or removing characteristics from something in order to reduce it to a set of essential characteristics;<br/>In object-oriented programming, abstraction is one of three central principles (along with encapsulation and inheritance);<br/>Through the process of abstraction, a programmer hides all but the relevant data about an object in order to reduce complexity and increase efficiency;<br/>The resulting object itself can be referred to as an abstraction, meaning a named entity made up of selected attributes and behavior specific to a particular usage of the originating entity. Abstraction is related to both encapsulation and data hiding;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates generally found this question more difficult, with very few responses scoring more than 1 mark. Candidates who did gain marks recognised that abstraction enables a programmer to represent the main features of a problem, in order to solve it, without including irrelevant detail.</p>\n</div>",
    "question_id": "19N.1.SL.TZ0.6",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-1-general-principles"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>one</strong> method of inputting data that can improve the accessibility of a computer system for some users.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award up to <strong>[1 max]</strong></em>.<br/>Text-to-speech;<br/>Voice recognition;<br/>Braille keyboards;<br/>Touch screen;<br/>Input from scanner;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.4",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by a sorting algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> difference between a bubble sort algorithm and a selection sort algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em><br/>A sorting algorithm is a method for reorganizing a number of items;<br/>into a specific order (such as alphabetical order, highest-to-lowest value);</p>\n<p>Sorting algorithm performs specific operations on the input list/array;<br/>In order to deliver ordered list/array as output;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p>Bubble sort swaps adjacent items;<br/>Selection sort finds the next smallest (each time it goes through the list);</p>\n<p>Bubble sort can exit early/ is faster if already the list is sorted;<br/>Selection sort will need to complete the procedure for the entire list every time;</p>\n<p>(The efficiency of Bubble and selection sort is different when applied on <strong>already sorted</strong> list)<br/>in the best-case bubble sort takes an order of N time;<br/>whereas selection sort consumes an order of N<sup>2</sup> time (where N is the number of items on the list));</p>\n<p><em><strong>Note</strong>: To award marks for such an answer it should be evident that the list is already sorted OR the term 'the best-case' should appear because the worst-case /average-case complexity/efficiency is same in both algorithms (O(N<sup>2</sup>))</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>In this question both parts, (a) and (b), were well answered by the majority of candidates. Many candidates scored full or nearly full marks for this question.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In this question both parts, (a) and (b), were well answered by the majority of candidates. A few candidates confused Bubble Sort with Selection Sort. Many candidates scored full or nearly full marks for this question.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "21N.1.HL.TZ0.3",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a trace table for the following algorithm.</p>\n<pre>K = 1<br/>N = 1<br/>M = 2<br/>loop while K &lt; 5<br/>   output(N,M)<br/>   K = K + 1<br/>   N = N + 2<br/>   M = M * 2<br/>end loop</pre>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[5 max]</strong> as follows</em>:<br/><em>Award <strong>[1]</strong> for a trace table with at least three columns (headings K, N, M, K&lt;5 and output)</em>;<br/><em>Award <strong>[1]</strong> for each correct output up to <strong>[4 max]</strong></em></p>\n<p><em><strong><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></strong></em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>This question was well answered with the vast majority of candidates gaining some marks, many of whom<br/>achieved full or nearly full marks.</p>\n</div>",
    "question_id": "19N.1.SL.TZ0.7",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p><strong>Copy</strong> and complete the following truth table.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1]</strong> for all correct input values, <strong>[1]</strong> for a correct A NOR B column and <strong>[1]</strong> for a correct (A NOR B) OR A column</em>.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.5",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An organization is implementing a new computer system.</p>\n</div><div class=\"specification\">\n<p>The management considered phased conversion and direct changeover as methods of implementation.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> organizational issues related to the implementation of the new system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate these <strong>two</strong> methods of implementation.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> type of testing that involves users.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> consequences of inadequate testing.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss the social and ethical issues associated with the introduction of a new computer system.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>User roles / the organization restructure their workflow;<br/>Technology issues / issues of software compatibility / hardware compatibility;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em><br/><em><strong>Direct changeover</strong></em>:<br/>is the cheapest and quickest/the old system is completely switched for the new one;<br/>this is straight forward but also the most risky / nothing to fall back on;<br/>no need to keep data duplicates;<br/>it allows the organization to change the system when most convenient;<br/>the employees have very little time in order get use to the new system as the change is instantaneous;<br/>there is a period of time when neither systems are operational;</p>\n<p><em><strong>Phased conversion</strong></em>:<br/>method where the old system is still in use but parts of the new system or modules are introduced, involves bringing in the new system one step at a time;<br/>less risky than direct changeover; less risky that the whole system will go wrong/if something happens, it will only affect the specific part;<br/>takes a lot of time;<br/>employees have enough time for training/to get use to the new system / are introduced to the changes in small stages;<br/>Employees/users could ask for changes which then hold up the installation of the next phase which helps improving the system;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>User acceptance test;<br/>Beta testing;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em><br/>Can lead to software which is not appropriate for the purpose it was intended/can lead to the system not meeting user requirements;<br/>Can lead to (undiscovered) bugs in software/errors in the system;<br/>Can lead to end user dissatisfaction;<br/>Can lead to reduced (employee) productivity;<br/>Can lead to decreased reliability of the organization;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/>Personal/professional development of all employees must be considered;<br/>Physical safety (of all users);<br/>Ergonomic standards (human-computer components);<br/>Human dignity of all users;<br/>The new system might be designed to replace some staff;<br/>Code of ethics (system resources should not be used without approval);</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were usually able to identify at least one organizational issue related to user roles or technology, when implementing a new system. In many cases, candidates got both.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates were able to correctly evaluate the two given methods of implementation, direct changeover and phased conversion, with very comprehensive contextual responses seen. In some cases, it wasn't quite clear whether the candidate was describing phased conversion or pilot implementation. However, these cases were rare.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates correctly stated beta testing, as a type of testing that involves users.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally demonstrated their understanding of the consequences of inadequate testing with many full or nearly full mark responses seen. Some candidates unfortunately gave good descriptions of the consequences that concentrated on the same marking point, for example, they described the software not meeting user requirements or being fit for the intended purpose in multiple ways, therefore limiting their mark.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates recognised the more obvious social and ethical issues associated with the introduction of a new computer system, such as the need to replace some staff, or issues related to the implementation of a code of ethics. However, many candidates concentrated too much on matters related to poor data protection issues. In addition, the issues related to ergonomics, physical safety at work or professional development were rarely seen.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.8",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations",
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Assume X = 5, Y = 3 and A = TRUE.</p>\n<p>Determine the value of the following expression:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mo>(</mo><mo>(</mo><mi mathvariant=\"normal\">X</mi><mo> </mo><mo>&gt;</mo><mo> </mo><mn>5</mn><mo>)</mo><mo> </mo><mi>XOR</mi><mo> </mo><mi mathvariant=\"normal\">A</mi><mo>)</mo><mo> </mo><mi>AND</mi><mo> </mo><mo>(</mo><mi mathvariant=\"normal\">Y</mi><mo> </mo><mo>+</mo><mo> </mo><mn>2</mn><mo> </mo><mo>&gt;</mo><mo> </mo><mn>4</mn><mo>)</mo></math></p>\n<p>Show all your working.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max] </strong></em><br/><em>Award <strong>[1]</strong> for showing the working out</em><br/><em>Award <strong>[1]</strong> for the correct result (TRUE)</em></p>\n<p>((5&gt;5) <strong>XOR</strong> TRUE) <strong>AND</strong> (3+2&gt;4) = (FALSE <strong>XOR</strong> TRUE) AND TRUE=<br/>=TRUE <strong>AND</strong> TRUE;<br/>=TRUE; </p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Many candidates correctly determined the value of the expression and gained full marks.</p>\n</div>",
    "question_id": "21N.1.HL.TZ0.4",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A company has expanded its office space into nearby rooms and has decided to set up a local area network (LAN) to support its operations.</p>\n<p>The LAN will connect the room where the server is installed to new computers in the additional office space. The network engineer produced the following Gantt chart for this task.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>concurrent processing</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> tasks that will be carried out concurrently.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> tasks that will be carried out sequentially.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>After 5 years the company decided to replace the LAN with a wireless local area network (WLAN).</p>\n<p>Outline <strong>two</strong> advantages, to this company, of installing a WLAN.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A WLAN will introduce additional security issues for the company.</p>\n<p>Discuss any <strong>two</strong> of these issues and the ways in which the company might resolve them.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The company is considering expanding their network to allow employees to connect from anywhere in the world. The expanded network would need to provide security and allow the employees full functionality of the internal network.</p>\n<p>Explain how setting up a virtual private network (VPN) would provide a suitable solution.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Processes/task are carried out simultaneously/at the same time;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Install connectors on wall of server room AND Install connector on wall of new office space;</p>\n<p><em><strong>OR</strong></em></p>\n<p>Test the cabling AND Connect the new computers with the cabling;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Any pair of tasks that are NOT a correct answer to part (b)</em>;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating an advantage, <strong>[1]</strong> for expansion/example in context.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em></p>\n<p>Answers may include:<br/>Use on the move;<br/>More versatile staff encouraged to collaborate etc.;</p>\n<p>Allows BYOD:<br/>Which could lead to greater productivity (as familiarity with device);</p>\n<p>No extra equipment is needed for expansion after initial set-up;<br/>Which will save the company time and money;</p>\n<p>Reduces wiring;<br/>Therefore improved safety for employees;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>The data can be intercepted as it goes through “the air”;<br/>Can be resolved by strong encryption/protocols;<br/>WPA-2 / a description of WPA-2;<br/>Use of trusted MAC addresses;<br/>Regular changes of <span style=\"text-decoration:underline;\">router</span> password; <strong>[2 max]</strong></p>\n<p>BYOD issues leading to insecure devices;<br/>Clear company policy regarding use;<br/>Use of sand-box;<br/>Only approved devices allowed;<br/>MAC addresses – only adding clean and tested devices brought in by staff;<br/>Installation of MDM services;<br/>Authentication (user ID + password on all devices including BYOD);<br/>Security features added by company; <strong>[2 max]</strong></p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A VPN/tunneling allows the employee’s device to appear to be part of / a node of the internal company network;<br/>Thus affording him/her full access to the network resources;</p>\n<p>Data that passes through a VPN can be encrypted;<br/>So any unauthorized access will not be able to understand the data;</p>\n<p>Tunnelling allows the company’s own protocols to be used/IPsec/TSL ensure security;<br/>Even though the data is passing over an outside network;</p>\n<p>Multiple exit nodes / hidden IP addresses/encrypted connections;<br/>Make it hard to distinguish where the data was generated;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "18M.1.SL.TZ0.11",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming",
      "topic-3-networks"
    ],
    "subtopics": [
      "4-1-general-principles",
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a logic diagram for the Boolean expression</p>\n<p style=\"text-align:center;\">NOT A OR B AND C.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1]</strong> for each correctly placed gate, up to <strong>[3 max]</strong></em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.6",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> difference between a binary tree and a non-binary tree.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Given the following binary search tree (BST), draw the resulting BST after the deletion of the root node.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max] </strong></em><br/>Binary tree (is a tree) in which every node has (no, one or) at most two children whilst<br/>a non binary tree can have nodes with more than 2 children (non binary trees do not have an upper limit on number of children nodes);</p>\n<p>Each node in a binary tree can have at most two subtrees (left and right subtree), a node in a non binary tree can have any number of subtrees;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p><em>Award <strong>[1]</strong> for the root (1)</em><br/><em>Award <strong>[1]</strong> for the correct right subtree</em></p>\n<p><em><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKAAAACsCAYAAAD8DadWAAARfUlEQVR4nO2dcWgcV37Hv07vCitEaWwJp6plK/ZhOWHPih2NEXiFFTXQOPg6i0wLNdSldNKWxDakZBJI/sofS3OWocXrK2296XXV4DQHKyRIQ1YYn4xGILR2dq2aRLucZaHZ7iVYuz6uYhZ6V0//0L3JSNqVVtp582Z2fx8wJLI877fsZ9/befP7/d4u0zRNEIQgnhIdANHckICEUEhAQigkICEUEpAQCglICOU7bg2Uy+WwuLiIlZUVFAoFLC0tVfy9/fv3o6OjA62trejq6sLhw4fdCpEQwC5e+4CZTAZzc3OYmppCLBZb83eSJGFgYKDiv5ucnEQqlVrzM0VR0N/fj6NHj+KFF17gES4hCEcFzOfzGBsbw8jIiCWRqqro6+vDwYMH0dbWhn379tV8reXlZSwsLGBmZgbDw8MAVuU9f/48wuFwzdciPIzpAJqmmaqqmgBMAGYkEjE1TTMNw3Di8qZpmqZhGKamaWYkErHGUVXVzGazjo1BuE9dAmazWUs8SZLMRCLhqHTVKBaLZiKRMCVJIhF9zo4ENAzDjMfjrotXKQ67iPF4XEgcxM7ZtoC6rpuyLFtLrRfecMMwrKVZlmVT13XRIRE1si0BNU2zZr10Os0rph2TTqdNSZJMSZJMTdNEh0PUQM0CMvkURfH0DKPruqkoigmAJPQBNQlol88LS+5WGIZBEvqELQXMZrO+ko9hl5DukL3LphvRpVIJr7zyCgDg9u3bCAQCvLYjuVAul3Hq1CkA/oy/KdjMTrbH5+cZhM3gkUhEdChEBaoKmE6nrb01v8P2LP38QWpUqi7B4XAYhUKhIZYuthR3dHRgbGxMdDiEnUpWstkvkUi4+3HgSCKRoFnQg1RMSNU0DQBw+vRpFz8KfGGvZWJiQnAkhJ2KAo6MjCAajfp+6bUTCAQQiUQwMjIiOhTCxgYBc7kcUqkUjh07xn3wJz+/i9Erf47vX7mLX3MfDTh16hRSqRRyuZwLoxG1sEHAxcVFAMBzzz3HeehlTP7DX+OsOoIy55EYBw4cAPDtayTEs0HAlZUVAMDu3bs5D92GwR9O4s5w5dR8HrAMavYaCfFsELBQKECSJBGxEE3IBgGXlpbQ09MjIhaiCdkgYDAY3FDFRhC82CBga2uriDiIJmWDgHv37gWAhtyqYK/p4MGDgiMhGBsEdG+rYgV3r/wAveokHqi9OOLCXiB7Tfv37+c8ElErFZMRwuEw2tvbcf36dRExceO1117Do0ePKCHBQ1R8FDc0NIRYLIZSqeR2PNwolUqIxWIYGhoSHQpho6KAZ86cAQDcuHHD1WB4wl4Le22ER6iWJhONRk0AZrFYdC83hxO6rpsAzGg0KjoUYh1V+wOeO3cOAPDBBx+49FHgx9WrVwF8+5oID7GZnSyJM5lMuvWBcBz2GhopubaR2LIs08+ljSyzW1EU0aEQVdiyP6C9tHF0dNQ3Pfny+bx1x9sIdS2NypY9ogOBAEZHRwGsbs/k83nuQdVLLpez5BsdHSX5PExNTcr37dtnSdjZ2Ynp6WmuQdXD9PQ0uru7Afhrxm5atrNeF4tF6zthNBr1VKsOe4s2RVGqbh/puu6ZtnLEDvoDGoZh7RHKsuyJNm2apllNKrf6YNhvTEhC8ey4RW86nbYaVYpqkWtvEbydD4Ou66YkSSShB6irR/T6FrmKojjenLzSmJqmWV8FdtoimElIHVXF4kiXfCYimxElSTKj0ahjMjLpotGoJbssy3X3pmYSSpJEEgrC8YNqMpkMNE1bc1aIoijo6elBR0cHgsEggNW76fXbI+VyGbquAwDu37+PQqGAe/fuWSUC7IyQUCjk2IE19v1Cumt2H24nJQGr+3H379/HzMxMxROQtoKdqNTX14dgMMjt2K58Po/3338f9+7dIwldhquA61k/w1VisxmSd2yXLl3CvXv38NFHH9EZdS7hqoBeh0kYi8WgaRpOnjwpOqSGx7XTMv1AIBCwUrdCoRBJ6AIk4DqYhE8//TRJ6AIkYAUCgQAuX76M/fv3k4ScoRPTN+HChQuIRqMIhUK4du2a6HAaEpoBt+DChQsAgIsXL675//Vcu3aNzjDeASRgDdQiIeu8Wk1QojK0BNfIhQsXkEwmcfHixYrL8fnz53Hz5k0Bkfkb2gfcJtPT0wiFQohEInjzzTetzfJMJoNjx45B13Uuy3Aul4NhGFhYWACw2mTTvpnf19dn/XcwGERLS4svvg7QErxNTp48CU3TEAqF8PDhQ1y9ehWBQMDKwv7yyy/rfuPL5TKy2Sw0TVvzLHw9qqoCAB4/foyzZ89W/R3ejzLrQlQWhN+pdIKoqqp1VeBpmmZldbM/qqqaiUTCTKfTNeVcGoZhZrNZU9M0Mx6PW2lrsGUpeSnzhwSsA5aJzSRMJpMmgG2liK3PqWSSOJlpztLZ7HKz3E3RkIB1Ys+uZgcj1vrGVkrm5c363E1ZloXWfJOANWKvJWHJtqzwyZ7Yihp60GSzWUsAJq4I7LU0kUhESB8gEnAbJJNJMxqNWvKwGSQSiViFWmwZrQZrFSJJkieWQMMwrNNEJUlyvciMBNwhxWKx4hd99mf9rGYYhlVApaqq54qh7LOym310SECHYHefiUTCunO1/x2T1MvnL9s/JG61siMBOWOXzwtLbi2wrxNuSEgCcsZv8jGYhLyXYxKQI+zLvV97E7LlmOeNCT0L5kQul0N3dzcikQjeffdd0eHsCHtrPl4t7khAToTDYRQKBd/3JmRJFvF4HOfPn3d+AG5zaxPDnhP7ubWxHfYIj8dGNc2AHAiHwwDQMAfilEol7NmzB9Fo1PGEW0pIdZhcLofx8XG8/vrrokNxjN27d0NVVSvr20lIQIeZmZkBAPT39wuOxFlkWUYqlUImk3H0uiSgw0xNTUFRFBduPJZx651e7Nq1C7t29eKdW8tcRzt+/DgAYG5uztHrUka0w8RiMSQSCe7jPMn9J37S/RH+zzziyiwSCASgKErVnj47hWZAB2EnCLAzl/mxjMkPo/jnv/xj/MWVJHIrTziPt8qzzz7r+DnSJKCDGIYBAGhvb+c8UhsGfziL/8lewff/6y0MvPHvmHdBwiNHjmB8fNzRa5KAvuUptB7+Q7z1ow/xt0v/gh/P+vNoXRLQ77QexR/92ffw9S/KoiPZESSgg7S0tAAAHj165PLIATzzu/wf983Pz0OWZUevSQI6CKsHfvDggUsjPsHK/CeIfNKNPzmxm/toDx8+dLy2mAR0GEVRMDU1xXeQX97COx29eOmlHvzgx0/h0r+9gRdb+b6V5XIZsVjMaqHsGI4/XW5yWA5gI5w0b4clWDidG0gzoMMMDg4CAO7cuSM4EmcZHx+HJEmOHY/BoGwYDjRaNkw+n0dnZyeXnECaATmgqirGx8cxMTEhOhRHYI3bz5w54/i1aQbkBGVE1wYJyAlWE6KqKi5fviw6nB3hRk0I3QVzxO9VcayklGfvGhKQM6y00a91wbzrWkhAzlBnhM0hAV3AT71hWJzUG6bBMAzDKm9UVdVzT0qy2azVK5C6YzUw9v6AXqgbpv6ATYiu69ZSJ6pF7vre1JFIREjPQhJQIMlk0hJAlmVT0zTuEhSLxQ29qalHdBPDuuuz7qT2LvlOyci6udq75Kuq6om7cnoS4iEymQw+++wzvPfee9bPFEVBf38/Dh06hPb2dnR2dm76RKJUKmF5eRmLi4vI5XK4efOmVUgkyzKGhoYwODjomVOUSEDB5PN5jIyMrDn2C1iVcW5uDlNTU1VPStoKSZIwMDCAvr4+nDhxwjPS2SEBBZLP5zE0NAQA+Pzzz7F7d/W0+nw+D8MwtiwMb21tRVdXF9ra2ja9nlcgAQXBDj1UFMU6b64ZoXxAAZB830ICusy1a9cQCoUQjUZx/fr1ppYPAKVjOclW2xpuPuT3CySgQ7Cz5Cpt6voxI8YtaAl2CE3TIEnShsLtcrmMS5cuIRaLQdM0nDx5UlCE3oT6AzrEyMjIhpoJ+zaLruue3IcTDc2ADpDJZJBKpRAKhayf2eUbHR0l+apAAjoAW35Z0fb09DQ6OzvR09OD27dvk3ybQAI6gH35pT2+7UFPQuqEdQ1Ip9PQNA0XL17kcp5Go0IzYJ3cunULkiRZ8sXj8TXylctl5HI5TExMoFz2ZxNJntAMWCfhcNhKd0omk+jq6sL9+/cxPz+P2dnZNT2Vi8WiLxIE3IQErAO2/FZCVVUEg0EcOnQIBw4coBuRKjTNPmC5XIau63j06BG++eabqr938OBBtLS0bJn4CawuvwDJVg8NOwPm83nMzs5iZmYGk5OTSKVS276GLMs4ceIEent78fzzz28Qq1Qq0ZJaJw0lYD6fx9jYGEZGRizhZFnGyy+/jI6ODgSDQbS0tGw6Q+VyORiGgYWFBczPz2NsbGzNtYaGhnDmzBkSzylEPYR2Ek3TrKIe/KbEUNM0x4q/dV03k8mklVCA3xT1iKwmaxR8LaBdPFmWzWQy6UpZYzwet8oaScT68KWAxWLR6jrF6mndZn1hdzQaFVLY7Xd8J6C9mDuRSAh/0+09XyRJotlwm/hKQJZRrCiKqeu66HDWkE6nrQ8GJZ3Wji8EXN82TPSsVw0R7c38jucF9EtvPYZhGFT7sQ08LyB7M/3WZ9mtFrd+x9MCsl56fpxJ7DM33ZhUx7NPQtiDfkVRcP36ddHh7Ah2zEFHRwc+/vhjSk6thOhPQDVUVTUlSfJcK9vtwso1/fD9VQSeFJCdzOi3733VYJvmfv8w8cCTGdHsZMbTp0+LDsURFEUBAHz66aeCI/EenvsOyL77JRIJq6yxEXj77bcxOTmJ2dlZ0aF4Cs/NgOwNGhgYEByJs8iyjFQqhUwmIzoUT+E5AWdmZqAoikv5dk+wcvfv8dL3ruDur/mOdPz4cQDA3Nwc34F8hucEHB4eRn9/vzuDPdEx8Y//ikkXhgoEAlAUZcsOp82GpwQslUoAgGeeecaF0f4X/z32MXLP/QEOuTAaAPT09GBy0g3d/YOnBFxeXgYAdHV18R/slxp+dOcE/uol9wqIOjo6dlSb0sh4SkD3+AXufjiL3jdC+B3RoTQ5TSjgE6zc/Q8kDvwpwr//26KDaXo8JWBLSwsAYHFxkeMoBrI//QR/d7YLv7VrF77bq+LBAxW93/0bjP6c761woVDgen0/4ikBWbnk119/zXGUVrz41k9hrj6GxK/uDOPQoWHc+dU/Yej3+NbpLy0tQVVVrmP4DU8JCKw+tpqamhIdBheGh4cRDAZFh+EpPCdgf38/YrGYa52kvvPiW/jZz97Ci5yblLAnIEePHuU7kM/wnICDg4MA0HCzoKZpAIDu7m7BkXgLzyUjAKstzwBgbGxMcCTOUCqVsGfPHmpcWQHPzYDAarep8fFxTE9Piw7FEW7cuAEAOHfunOBIvIcnZ0BgdRYsFAq4ffu2r1PZc7kcuru7afarhrhc2M3JZrNWoyG/YhiGKctyQ5QW8MKzApqmacbjcV+XNrKWHdQpoTqeFtA0Td+esUbF6bXheQH9eNAfyVc7nhfQNNdK6OXlmNpybB9fCGiaayWMRCKea1Ck6zo1JtoBvhGQwW5MJEky0+m06HBM0/y2hYgkSb75muAVfCegaa5u0bDWvCJb5NpbBKuq6rmehX7AlwKa5sYWuaqqujIjGoaxoTc1zXo7x7cCMtaLKMuyGY/HHZ+N0um0GY1G14xD4tWPZx/FbZdyuYwvvvgC4+PjGB4eBgBIkoRwOIwjR44gGAyira2tpnrjfD6P5eVlLCwsYGZmxroeAEQiEbz66qvW2cBEfTSMgHZKpRK++uorpNNp3Lx5c82BgYz1mcmPHz9GLBbb8HuKoqCnpwehUAjd3d2+fi7tRRpSwPWwc+IWFxexsrKClZWVigXifX19AIC9e/eivb0dhw8fdjvUpqMpBCS8iyfzAYnmgQQkhEICEkIhAQmhkICEUEhAQigkICGU/weAbhr/r+RzcAAAAABJRU5ErkJggg==\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></em></p>\n<p>Alternative answer<em><br/></em></p>\n<p><em>Award <strong>[1]</strong> for the root (4)<br/>Award <strong>[1]</strong> for the correct right subtree</em></p>\n<p style=\"text-align:center;\"><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>In this question both parts, (a) and (b) were well answered by the majority of candidates.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In this question both parts, (a) and (b) were well answered by the majority of candidates. Part(b) Only a few candidates did not draw the resulting binary tree after deleting the root node.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "21N.1.HL.TZ0.5",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following algorithm, where <code>N</code> is a positive integer. </p>\n<pre>loop for K from 1 to N<br/>  loop for J from 1 to N<br/>    if K = J then<br/>      output K<br/>    end if<br/>  end loop<br/>end loop</pre>\n</div><div class=\"question\">\n<p>Construct the algorithm which performs the same task using a single <code>while</code> loop, instead of nested <code>for</code> loops.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award marks as follows up to <strong>[4 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for correct initialization.</em><br/><em>Award <strong>[1]</strong> for correct condition.</em><br/><em>Award <strong>[1]</strong> for correct output.</em><br/><em>Award <strong>[1]</strong> for changing the value of controlling variable.</em></p>\n<p><em><strong>Note</strong>: The question explicitly requires one <code>while</code> loop</em>.<br/><em>Do no not accept a <code>for</code> loop. Do not accept two <code>while</code> loops</em>.</p>\n<p><em><strong>Example answer</strong></em></p>\n<pre>K = 1<br/>loop while K &lt;= N<br/>    output K<br/>    K = K + 1<br/>end loop</pre>\n<p><em><strong>Example answer (including J variable)</strong></em></p>\n<pre>K = 1<br/>J = 1<br/>loop while K = J AND K &lt;= N<br/>    output K<br/>    K = K + 1<br/>    J = J + 1<br/>end loop</pre>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.7",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A group of programmers are involved in creating a new software product. They create many new sub-programs but also use existing sub-programs within the product.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:left;\">Outline why a sub-program is considered an example of abstraction.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of designing and developing different parts of software products concurrently.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> way in which users can be informed of software updates.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p>A sub-program is a named section of code that performs a specific task (in a program) / can be called by name / referred by the identifier when needed;<br/>without knowing the details (of code and data structures) as these are wrapped / hidden within the sub-program;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] </strong></em></p>\n<p>different stages (of programming) (<em>Accept examples!</em>) run simultaneously (rather than consecutively);<br/>this decreases product development time / decreases the time to market;<br/>leading to improved productivity/reduces costs;<br/>however, it requires more resources/more software developers;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong></em></p>\n<p>A message can be sent to the user (When the software is installed and registered, the user provides an email address / phone number);<br/>With a link to the update;</p>\n<p>notifications/alerts are sent to the computer (a cookie is placed on the user's computer which communicates with the software developer);<br/>about automatic updates;</p>\n<p>(when the program is run) it queries a URL the program developer has built in to check whether the current version matches the latest version;<br/>if not, notifications/alerts are sent;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates provided vague or too general answers.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates correctly answered this part.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "21N.1.HL.TZ0.7",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming",
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "4-1-general-principles",
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>One-to-7</em> is an international organization that works with teachers and other educators. One department within this organization provides an online forum for teachers to discuss ideas for lessons and to share resources.</p>\n<p>In order to access this forum teachers are required to submit the following information, which will be stored in a table in the database.</p>\n<p style=\"text-align: center;\"><strong>Figure 1: Online form to register personal details to the <em>One-to-7</em> forum</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"specification\">\n<p>Once the submit button on the online form has been selected, the personal data is input into the database.</p>\n</div><div class=\"specification\">\n<p>Once the teacher is registered they can post comments on the forum.</p>\n</div><div class=\"specification\">\n<p>The <em>One-to-7</em> database in managed by the database administrator (DBA).</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> reason why the teacher’s name has been split into two fields.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason why there may be concerns about the amount of personal information that is requested.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why the transaction needs to be atomic in the context of this scenario.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how transactions are managed to ensure isolation when registered teachers add comments to a discussion thread on the forum.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> tasks that are carried out by the database administrator (DBA).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The DBA is considering using the email address as the primary key, but is concerned that many of the 250 000 educators who are registering for this online forum may have more than one email address. Users may create duplicate accounts, deliberately or accidently, by using different email addresses as usernames.</p>\n<p>Explain the factors that would need to be considered in using a composite primary key instead of only using the email address.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>atomicity;<br/>sorting;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Not all of this information may be necessary for the purposes it is being collected for / asks for too much information;<br/>Teachers may be concerned about issues of privacy;<br/>If the information is shared with third parties it could be used / aggregated to identify the teacher / identity theft;<br/>Teachers may be put off either by the excessive time required to complete the form;<br/>This may lead to some teachers refusing to complete the application form / not as many teachers will sign up;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Atomicity in transactions ensure that the indivisible series of database operations either all occur, or nothing occurs;<br/>This prevents updates to the database occurring only partially / this maintains data integrity / consistency;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Isolation specifies the sequence that changes is processed / specifies that any parallel processing must produce the same result as if the processes were carried out sequentially;<br/>Each post to the thread takes place independently of others;<br/>One post will be completed before another starts / a post will not become visible until completed;<br/>Data and the transaction (row for the thread) is locked for that moment when the transaction is carried out;<br/>The addition of one post to the thread must not displace another’s, regardless of the order in which they finally appear;<br/>A transaction log is created prior to the transaction to allow rollback;<br/>This means that should an error occur part of the way through the transaction it will be rolled back and the database will return to its original state;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Data configuring / applying patches or upgrades;<br/>Setting permissions / passwords / access rights / ensuring security;<br/>Back up / recovery / archiving;<br/>Data cleansing / consistency checks on data / remove data errors;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>The large number of educators is critical to the number of fields included in any composite key;<br/>The composite key must be unique;<br/>Composite key could be made up of several fields;<br/>Allow suitable example (e.g. combinations of first name, last name, email, phone number etc.);<br/>Allow example that would <span style=\"text-decoration:underline;\">not</span> be suitable;<br/>Part of the primary key could include a random element in case of duplicated name and dates of birth;<br/>This would require an understanding of the nature of the educators, for example, are they of a particular age which would reduce the possible number of dates of birth, are they from one particular country so certain names would be more likely to occur;<br/>This would influence the number of fields that would be required to reduce the possibility of a duplicate entry occurring;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was poorly answered. Few candidates connected the question and scenario to atomicity or sorting purposes, while the majority claimed the reason for splitting the name into two fields was to avoid any confusion.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Generally, well done, but many partial marks were achieved on this question due to incomplete answers.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Was not well answered; Candidates were not able to outline the need for atomicity in transactions and many tended to confuse the question with networking environments, and failures in data transmission.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Was not well answered with many candidates giving generic answers for isolation in the scenario, but not knowing what happens when a transaction takes place with respect to this property.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was poorly answered. Responses were either vague or gave incorrect tasks of a DBA.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates were unable to clearly explain this question as they did not show understanding of the concept of a <em>composite primary key</em>. Some candidates, however, used correct examples to demonstrate its use and implications in the proposed scenario.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.1",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model",
      "a-3-further-aspects-of-database-management",
      "a-1-basic-concepts"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The machine instruction cycle is the process by which a program instruction is fetched, decoded, executed and the results are stored.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State where all instructions and data are stored.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the role of the data bus and address bus in this process.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Primary memory / RAM</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em><strong>Note</strong>: there must be explicit reference to both address and data bus</em></p>\n<p><em><strong>Example 1</strong></em><br/>Buses are used as physical connections to carry information to the CPU;<br/>The data bus transports data from/to CPU, whereas the address bus the memory address where the data is supposed to go/be.</p>\n<p><em><strong>Example 2</strong></em><br/>Data bus is a physical connection to transport data from-to CPU to be processed;<br/>Address bus is a physical connection to transport an address of memory storage where data (transported in the data bus) should be read/written;</p>\n<p><em><strong>Note</strong>: Award <strong>[1]</strong> mark, for responses that show some understanding of use of buses in CPU, for address location and data transport without using specialist terminology</em></p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "17N.1.SL.TZ0.8",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> advantages of a school using a computer network.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the purpose of the following hardware component of a network: Router</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the purpose of the following hardware component of a network: Network interface card (NIC)</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why protocols are necessary.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>data encryption</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of trusted media access control (MAC) addresses as <strong>one</strong> method of network security.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/>File sharing/resource sharing;<br/>instead of using a disk or USB key to carry files from one computer to another files can be shared directly using a network/all computers in the network can share resources such as printers, scanners;</p>\n<p>Communication;<br/>students/teachers can communicate with people around the world via the network;</p>\n<p>Interactive teamwork;<br/>software (like Microsoft Office) enables many users to contribute to a document concurrently;</p>\n<p>Flexible access;<br/>network allows students to access files from different computers (throughout the network) (one can begin work on a project on one computer and finish up on another);</p>\n<p>Software cost;<br/>software products are available for networks at a substantial savings in comparison to buying individually licensed software;</p>\n<p>Software management;<br/>load software on the server saves time compared to installing and tracking files on independent computers/upgrades are also easier because changes only have to be done once on the file server instead of on individual computers;</p>\n<p>Improved network security;<br/>if the school has its own network, it can monitor network traffic / can create a security culture (everyone who has a username and password is responsible for keeping data secure);</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>A network router is a hardware device that is connected to multiple channels for different networks;<br/>through an interface that is situated on each network;</p>\n<p>Router acts as a processing unit for information packets;<br/>it duplicates information packets for use during transmission from one network to another;</p>\n<p>The router uses a protocol or set of rules;<br/>to determine which information packets are to be routed to certain interfaces within the network;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Network interface cards are used to connect each computer to the network;<br/>so they can communicate with the network router to receive information packets;</p>\n<p>Interface cards determine the infrastructure of a local area network (LAN);<br/>and allow all of the computers to connect to the network;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Protocols define the rules that govern network communication (for example, packet format, type and size, what happens when an error occurs, and which part of the network is supposed to handle the error and how);</p>\n<p>Computer networks consists of various types of equipment (such as routers, switches, hubs and network interface cards) and the equipment comes from different vendors, but they must all work together or the network does not operate correctly;</p>\n<p>Protocols work in layers (the highest being what the user sees, and the lowest being the wire that the information is transferred along) and these layers communicate with each other according to the rules (allowing communication to occur accurately and efficiently);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>Data encryption refers to calculations/algorithms that transform plain text into a form that is non-readable to unauthorized parties (authorized recipient of an encrypted text uses a key and the algorithm to decrypt the data/ to transform it to the original plain text version);</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/>Each (wireless network) adapter has a unique label called a MAC address;</p>\n<p>Routers uses these addresses to identify/authenticate computers (routers include an option to whitelist or blacklist certain devices based on MAC addresses, so access could be restricted to any device which is not in the whitelist);</p>\n<p>One disadvantage is that the whitelist should be amended any time a new device is purchased / when access to guests should be granted;</p>\n<p>Also this method is useless against hackers who use programs which intercept data passing through network and report the MAC address of any device communicating on the network;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were mostly able to outline at least one advantage of a school using a computer network. Answers were usually based around file or resource sharing. However, in many cases, the second advantage outlined was also related to file or resource sharing. Very few of the other possible answers were seen.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally found this question to be difficult and very few correct answers were seen, concerning the purpose of a router.</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates who achieved a mark for this question generally recognised that a network interface card was used to connect a computer to the network. Unfortunately, candidates who scored, generally only achieved a single mark, as they did not expand on their answers.</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally recognised that protocols define rules that govern network communication, but generally did not expand beyond that point, so rarely achieved two marks.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A mixed set of responses was seen for this question, in which candidates were required to define the term encryption. Candidates recognised that it was a security method but were often unable to provide sufficient detail to define clearly what it is.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally demonstrated a good understanding of what MAC addresses are and how they can be used in network security. Most candidates scored some marks. However, responses often lacked sufficient distinct points to allow all of the marks to be awarded.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.9",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Define the term <em>bit</em>.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Binary digit;<br/>(Minimal) unit of storage that can be set to 0 or 1;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.9",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain how an operating system manages peripherals.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max] </strong></em></p>\n<p>OS keeps tracks of all peripheral devices (the I/O controller) / decides which process gets the device when and for how much time / allocates and de-allocates devices;<br/>OS works with device drivers and the basic input/output system (BIOS) to perform hardware tasks<br/>/ the necessary drivers (for every peripheral) are built into the OS and/or when a new peripheral is added software / device driver provided by a hardware maker is installed into the operating system (to tell the computer's OS how to work with the peripheral/hardware) (because without a device driver the OS would not be able to communicate with this peripheral device);<br/>A device driver translates the OS's instructions into a language (analogue signals) that the device can understand;<br/>There are various types of device drivers for peripherals (such as keyboards, mice, disk drives, controllers, printers, graphics cards, ports, etc.);<br/>Device drivers run in the OS kernel space (in the part of the OS that directly interacts with the physical structure of the system) (and implement functions such as open, close, read, write);<br/>The running application/user's program can make a call to device driver functions which provide an interface between user space and kernel space;</p>\n<p><span style=\"text-decoration:underline;\">Example answer</span>:<br/>An OS manages peripherals via their respective drivers;<br/>For example, sound card drivers;<br/>are necessary so the OS knows exactly how to translate the 1s and 0s (that comprise the MP3 file) into audio signals;<br/>that the sound card can output to headphones / speakers;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A well answered question. Many comprehensive answers were seen.</p>\n</div>",
    "question_id": "21N.1.HL.TZ0.8",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A transport authority is investigating how many people use a certain direct train route, which is used every day of the week.</p>\n<p>At the end of each day, the total number of passengers who travelled on this route is stored in a collection, <code>PASSENGERS</code>.</p>\n<p>The first item was written to the collection on Monday 1st January 2018.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>The next items, collected on Tuesday and Wednesday, were added like this:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:left;\">The collection <code>DATA</code> contains the following data:</p>\n<p style=\"text-align:center;\">2, 4, 1, −2, −4, 1, 0</p>\n<p style=\"text-align:left;\">Consider the following pseudocode:</p>\n<pre style=\"text-align:left;\">COUNTER = 0<br/>SUM = 0<br/>DATA.resetNext()<br/>loop for X from 0 to 6<br/>  if DATA.getNext() &gt; 0<br/>    ARRAY[X] = DATA.getNext()<br/>    COUNTER = COUNTER + 1<br/>    SUM = SUM + ARRAY[X]<br/>  end if<br/>end loop<br/>output SUM/COUNTER</pre>\n<p style=\"text-align:left;\">Trace the pseudocode using the table below:</p>\n<p style=\"text-align:left;\"><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Assuming that the first item read from the collection is from Monday 1st January 2018, construct pseudocode that will read <code>PASSENGERS</code> into an array, <code>P_ARRAY</code>.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using <code>P_ARRAY</code>, construct pseudocode to output the day of the week with the highest average number of passengers. Use the sub procedure <code>convert()</code> which converts the numbers 0 to 6 into days of the week, for example <code>convert(1)</code> will return “Tuesday”.</p>\n<p><strong>Note</strong>: you should <span style=\"text-decoration:underline;\">not</span> assume that data for an exact number of weeks is stored.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award <strong>[1]</strong> for each correct column (excepting the first). If the first row is not completed, mark as follows:</em><br/><em>All 4 correct except for first row: <strong>[3]</strong></em><br/><em>3 correct except for first row: <strong>[2]</strong></em><br/><em>2 correct except for first row: <strong>[1]</strong></em><br/><em>1 correct except for first row: <strong>[0]</strong></em>.</p>\n<p><em>Follow through for an output that is correct from their incorrect <code>COUNTER</code></em> <em>or <code>SUM</code></em> <em>columns.</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre>P_ARRAY // array declaration<br/>X = 0<br/>(PASSENGERS.resetNext())<br/>while PASSENGERS.hasNext()<br/>  P_ARRAY[X] = PASSENGERS.getNext()<br/>  X = X + 1<br/>end while</pre>\n<p><em>Award <strong>[1]</strong> for evidence of the idea of a loop (for or while etc.).</em><br/><em>Award <strong>[1]</strong> for correct while/until loop with correct condition.</em><br/><em>Award <strong>[1]</strong> for correct reading of collection into array.</em><br/><em>Award <strong>[1]</strong> for declaring and incrementing X</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The following are two alternative approaches. Most solutions should fit one or the other markscheme. Do not penalise those who introduce separate variables instead of arrays.</p>\n<p><span style=\"text-decoration:underline;\"><em>Example 1:</em></span></p>\n<pre>MAXAV = 0<br/>    X = (P_ARRAY.length) − 1<br/>    MAXDAY<br/>    loop for DAY from 0 to 6<br/>      SUM = 0<br/>      COUNTER = 0<br/>      loop for C from DAY to X //loop while C &lt; X<br/>        SUM = SUM + P_ARRAY[C]<br/>        COUNTER = COUNTER + 1   <br/>        C = C + 6 // C = C + 7 or allow step 7 in the loop<br/>      end loop<br/>      if SUM/COUNTER &gt; MAXAV<br/>        MAXAV = SUM/COUNTER<br/>        MAXDAY = DAY<br/>      end if<br/>    end loop<br/>output convert(MAXDAY)</pre>\n<p><em>Award <strong>[1]</strong> for correct initialization of numerical variables (e.g. <code>MAXAV</code></em>, <em><code>COUNTER</code></em>, <em><code>SUM</code>)</em>.<br/><em>Award <strong>[1]</strong> for correct loop through each day</em>.<br/><em>Award <strong>[1]</strong> for correct for loop through array</em>.<br/><em>Award <strong>[1]</strong> for incrementing array position by 7 each time</em>.<br/><em>Award <strong>[1]</strong> for calculation of average (<code>SUM/COUNTER</code>)</em>.<br/><em>Award <strong>[1]</strong> for comparing this with <code>MAXAV</code></em>.<br/><em>Award <strong>[1]</strong> for adjusting <code>MAXAV</code> and <code>MAXDAY</code> if necessary</em>.<br/><em>Award <strong>[1]</strong> for correct use of <code>CONVERT</code> sub-procedure</em>.</p>\n<p> </p>\n<p><span style=\"text-decoration:underline;\"><em>Example 2:</em></span></p>\n<pre>MAXDAY = 0<br/>DAYSUM ARRAY<br/>WEEKS ARRAY<br/>COUNT = 0<br/><br/>while COUNT &lt; P_ARRAY.length //loop through p.array<br/>  DAY = COUNT MOD 7<br/>  WEEKS[DAY] = WEEKS[DAY] + 1 //add number of weeks for this day<br/>  DAYSUM[DAY] = DAYSUM[day] + P_ARRAY[COUNT]<br/>  COUNT = COUNT + 1<br/>end while<br/>COUNT = 0 //reinitialize<br/>while COUNT &lt; DAYSUM.length //loop through dayArray can also be<br/>                            //a for loop as there are 7 days.<br/>  if DAYSUM[COUNT] / WEEKS[COUNT] &gt; MAXDAY //find maxDay<br/>    MAXDAY = COUNT<br/>  end if<br/>  COUNT = COUNT + 1<br/>end while<br/>output convert(MAXDAY)</pre>\n<p><em>Award </em><strong><em>[1] </em></strong><em>for correct initialisation of variables/arrays. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>for correct loop through array. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>mark for incrementing day. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>mark for incrementing sum for each day. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>mark for incrementing number of weeks for each day. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>mark for correct calculation of average for each day. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>mark for finding MAXDAY. <br/></em><em>Award </em><strong><em>[1] </em></strong><em>mark for correct use of CONVERT procedure.</em></p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18M.1.SL.TZ0.12",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline what is meant by beta testing.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Involves sending sample software to the intended audience;<br/>(Selected audience do not pay for this software);<br/>To try/use the software product;<br/>And give the feedback to the authors (which help in correcting bugs);</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.SL.TZ0.10",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An application package used in an office includes a word processor. A secretary uses the word processor to create a text file.</p>\n</div><div class=\"specification\">\n<p>The text file is automatically saved at regular periods while being edited.</p>\n</div><div class=\"specification\">\n<p>All files created in this office contain information important to the business.</p>\n</div><div class=\"specification\">\n<p>The office manager decides to buy and install new software and hardware.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a spellchecker checks whether a word in a text file is correctly spelt or not.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> advantage of this feature.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> additional features of a word processing package that could be useful for this office.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the purpose of <strong>one</strong> application software package other than a word processing package that could be used in this office.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the security measures that should be taken to prevent data loss.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> problem that may arise from the installation of new hardware and software in the office.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The changeover to the new system can be achieved by either direct changeover or phased conversion.</p>\n<p>Compare direct changeover and phased conversion.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Each word in the text file is compared with words in a <span style=\"text-decoration:underline;\">dictionary</span> (held in memory/online);<br/>If the word is found in the dictionary it is correctly spelt / if the word is not found in the dictionary, spellchecker will recognize that it is incorrectly spelt;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>If power goes off, only the text typed after last (automatic) save is lost;</p>\n<p><em><strong>NOTE</strong>: accept responses that express evidence of just a partial loss of the file</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Apply styles, effects;<br/>Insert (tables/pictures/graphs/formulas/...);<br/>Mail merge;<br/>Macros;<br/>Print;<br/>etc.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating an application software package and <strong>[1]</strong> for stating its use in the office, up to <strong>[2 max]</strong></em>.</p>\n<p>Example answers<br/>Spreadsheet;<br/>For graphically presenting various data;</p>\n<p>Database software;<br/>For holding employees/customers data;</p>\n<p>Web page creators/editors;<br/>To create/manage the office’s web pages;<br/>etc.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Off site data storage;<br/>Make backups regularly/periodically/frequently;</p>\n<p>Prevent physical damage to the computers / Keep equipment in safe and dust-free places / Protect equipment from static electricity that can erase data or damage components / Protection during lightning and electrical storms;</p>\n<p><em><strong>Note</strong>: Accept any reasonable examples, but there should be more than one. The focus of the question is on data loss, and not in relation to security/hacking</em>.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating a problem and <strong>[1]</strong> for an elaboration, up to <strong>[2 max]</strong></em>.<br/>Users/employees might be afraid of these changes (for various reasons);<br/>And not willing to help in this change;</p>\n<p>Data migration problems;<br/>For example, different file formats so conversion must be performed;</p>\n<p>Employee efficiency may drop;<br/>As they learn to use the new system;</p>\n<p>Issue of compatibility with legacy software/hardware;<br/>So features of new software/hardware may not work correctly;</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for outlining what is meant by direct changeover, <strong>[1]</strong> for outlining what is meant by phased conversion, and then <strong>[1]</strong> for an advantage or disadvantage <strong>of each</strong>, up to <strong>[2]</strong></em>.</p>\n<p><strong>Example answer</strong>:<br/>Direct changeover, the old software and hardware is completely replaced, in one move, by the new software and hardware;<br/>Phased conversion involves selecting one section in the office for the direct changeover and other sections will be switched when the first section selected is running satisfactorily. Eventually the whole office has been changed;<br/>A phased conversion is less risky than a direct changeover as any problems that might arise will be isolated in only one section in the office;<br/>Direct changeover means everyone in the organization has same software/hardware and so there are no compatibility issues;</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">g.</div>\n</div>",
    "question_id": "17N.1.SL.TZ0.11",
    "topics": [
      "topic-2-computer-organization",
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "2-1-computer-organization",
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A rail transport company uses a global positioning system (GPS) to determine a train’s position.</p>\n</div><div class=\"specification\">\n<p>The GPS data is used to provide real-time arrival data on video displays in train stations.</p>\n</div><div class=\"specification\">\n<p>The GPS data is made publicly available so that software developers can use it to build apps.</p>\n<p>The mobile application <em>ATrainAway</em> uses the real-time train GPS data as well as the GPS data from the user’s smartphone.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how GPS works.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> data item concerning the arrival of a train that may be provided on the video display.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how sensors can be used in combination with GPS to provide more accurate arrival data.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> pieces of information that the <em>ATrainAway</em> application could provide to the user.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss the advantages <strong>and</strong> disadvantages of using GPS in transportation systems.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] </strong></em><br/>The GPS receiver in a train takes the information from the satellite/ picks up the signals from (at least) 3 satellites;<br/>The signals transmitted are: time of transmission, coordinates of the satellite;<br/>The difference between the (atomic) time of transmission and (atomic) time of receiving the signal is used to calculate the distance;<br/>The method of trilateration is used to determine position from the distance to satellites/ is used to determine the train’s exact position/ is used to calculate position of the train through equation resolution on a sphere;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Train number/ route / direction;<br/>Status (on time / delayed / cancelled);<br/>Time train / <strong>next</strong> train are due;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Motion sensors/vibration sensors (can be placed on track rail);<br/>(<em>Accept any sensors which detect trains that pass them!</em>)</p>\n<p>(Sensing devices are placed) at a distance from each side of a train station/junction<br/>OR<br/>at some fixed points where obstacles (like tunnels) prevent GPS from working properly;</p>\n<p>Data from each sensor can be logged continuously and used in calculations/determinations (of expected arrival times/ delays / rerouting during disruption, etc.);</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Real-time information (train number/ route / direction and status (on time / delayed / cancelled);<br/>relating to the arrival / departure of a particular train;</p>\n<p>The time next train is due (the next 2/3 times a train will arrive at the station);<br/>helps passenger to spend less time waiting on trains;</p>\n<p>Should save user's (passenger) most frequented stops and routes;<br/>for quick access to get the next rail trip;</p>\n<p>Alerts (set);<br/>to let you know when a train is nearing the station;</p>\n<p>Trip planning (conducted);<br/>through Google trip planner within the app;</p>\n<p>Information on the nearest station, distance to station/ next train arrival/ next train departure;<br/>to help passenger to spend less time travelling/ save time;</p>\n<p><em>Mark as 2 and 2</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Safer transit;<br/>Trains are tracked and in case of emergency the location can be reached at the earliest and emergency services provided;</p>\n<p>More favourable view of transit/helps passenger to get punctuality status of the train/;<br/>GPS can provide worldwide three-dimensional positions (24 hours a day) (in any type of weather);</p>\n<p>Sometimes it is too difficult to ensure reliable positioning;<br/>Objects, such as buildings, overpasses, and other obstructions (that shield the antenna from a satellite) can potentially weaken a satellite's signal;</p>\n<p>Privacy issues;<br/>Knowing the absolute position of anything, anytime, anywhere;</p>\n<p><em>Mark as 2 and 2</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates knew that a GPS works by communication with satellites. Some candidates failed to achieve full marks as result of incomplete and vague descriptions. A few candidates, for some unknown reasons, wrote that the satellite calculates the train's position. </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Generally, well answered, with many full or near full marks awarded.</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Generally, well answered, with many full or near full marks awarded.</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was well answered with the vast majority of candidates gaining some marks, some of whom achieved full or nearly full marks.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were usually able to identify the advantages and disadvantages of using GPS in transportation systems. Some marks were lost because of vague discussions.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "21N.1.HL.TZ0.11",
    "topics": [
      "topic-7-control"
    ],
    "subtopics": [
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Define the term <em>recursion</em>.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p>Process/method/ procedure/ subroutine/ function/ algorithm that calls itself;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.HL.TZ0.7",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Taxonomy is the part of science that focuses on naming and classifying or grouping organisms. Swedish naturalist, Carl Linnaeus, developed the current hierarchical classification system of taxonomic classification in the 1700s. This classification is hierarchical so one class has many orders, and one order has many families.</p>\n<p>Birds can be classified under the Linnaean hierarchical taxonomy as “Aves”. In one system of classification, Aves has 23 orders, one of which is the order Ciconiiformes, which holds six families within it.</p>\n</div><div class=\"specification\">\n<p>Spoonbills constitute one of the families within the order Ciconiiformes. Data can be collected about spoonbills, such as length of the bill or beak, and is included in the database.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an entity-relationship-diagram that shows the relationships between the class, order and family.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline, using an example, how data validation can ensure that data is entered into the birds database correctly.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline, using an example, how data verification can ensure that data is entered into the birds database correctly.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between data and information when entering the length of a spoonbill’s beak in the database.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics of a conceptual schema.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the use of data modelling is critical to the success of a database, such as the one used in this scenario.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain, using the example described in the scenario, why referential integrity is important in databases.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each correct relationship with labels</em>.<br/><em>Award <strong>[1]</strong> for a top-down diagram showing the relationships</em>.</p>\n<p>One CLASS contains many ORDER;<br/>One ORDER contains many FAMILY;</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em><span style=\"text-decoration:underline;\">Note: original MS did not make any sense</span></em><br/>Length check;<br/>Could ensure only a value within an acceptable range is entered (allow example);<br/>Type/format check on bill;<br/>Only numerical (int/float etc.);</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Double entry;<br/>Will only allow data entry if both match;<br/>Proof reading;<br/>Will give a visual check that the data is correct;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>When the length of the bill / beak of the spoonbill is measured it is simply a decontextualized figure such as 10 cm;<br/>When it is included in the database and interrogated it will have a meaning, so it then becomes information;</p>\n<p>The database will contain only the figure “10”;<br/>the “cm” will not be included and is part of the meaning of the attribute/field.</p>\n<p><span style=\"text-decoration:underline;\">Note: the “information” mark must reference the database in some way</span></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>A high-level / overall / general view of the database;<br/>Shows the main concepts;<br/>Shows the main relationships,<br/>Can be described by a Data Structure Diagram;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Data modelling gives a visual representation of the proposed system;<br/>So that all stakeholders have the same understanding of the system;<br/>Shows basic relationships / establishes naming conventions etc.;<br/>Some that developers are able to develop the actual database;<br/>Avoiding issues such as redundancy/lack of integrity/lack of consistency;</p>\n<p>The data model will take the information from the conceptual schema and logical schema to informs the physical model for the development of the database;<br/>A lack of modelling may result in a structurally deficient implementation with poor data integrity / redundant data;<br/>This may mean developing prototypes of the database to test whether the proposed structure of the database will function as intended;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><span style=\"text-decoration:underline;\">Note: only award full marks if there is a valid reference to the scenario</span>.<br/>Referential integrity requires that a value used as a foreign key corresponds to a value of a primary key / validates the database through relationships;<br/>Therefore if the Ciconiiformes/order of the Spoonbill is not in the database, you will not be able to add it because of referential integrity;<br/>Referential integrity constraints;<br/>Ensure that if a record is updated or deleted, these changes will made in related cells and the possibility of update anomalies is reduced / eliminated;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most of the candidates clearly identified the main entities but failed to establish and correctly label the relationships.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates showed a good understanding of <em>data validation</em> and <em>data verification</em> with appropriate use of examples related to the scenario. However, many candidates provided answers that were generic, which showed confusion with both concepts.</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates showed a good understanding of <em>data validation</em> and <em>data verification</em> with appropriate use of examples related to the scenario. However, many candidates provided answers that were generic, which showed confusion with both concepts.</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates had no problem identifying what <em>data</em> was, but were not able to clearly distinguish the concept of information within the scenario.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A few candidates were able to correctly identify the characteristics of a conceptual schema.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was not well answered. Many candidates provided vague responses and just a few referred to <em>data modeling</em> as being critical process for avoiding issues such as redundancy, lack of integrity or lack of consistency.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most of candidates could not explain <em>referential integrity</em> with respect to the scenario.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.2",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model",
      "a-1-basic-concepts"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Describe the characteristics of a queue.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award</em> <strong><em>[2]</em></strong>.<br/>FIFO data structure;<br/>Items can be added only to one end (rear/tail) and removed from the other end (front/head);</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.HL.TZ0.8",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline the function of an interrupt.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>An interrupt is a signal to the CPU sent by hardware or software;<br/>The function of an interrupt is to alert the CPU to suspend execution of the current program;<br/>And to transfer the control to the interrupt handler (which saves the state of the current program to the interrupt stack, services the interrupt and resumes the normal CPU activity);</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18M.1.HL.TZ0.10",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><code>NUMBERS</code> is a collection that holds only positive integers.</p>\n<p>A three-digit number has three digits: a hundreds digit, a tens digit and a units digit. <br/>For example, for 406, its hundreds digit is 4, its tens digit is 0 and its units digit is 6.</p>\n<p>An algorithm is needed to copy each three-digit number from the collection <code>NUMBERS</code>, where the hundreds digit is smaller than its tens digit and its tens digit is smaller than its units digit, into a one-dimensinal array named <code>THREE</code>. If there are no such numbers in the collection then an appropriate message should be displayed.</p>\n<p>For example:</p>\n<p>If <code>NUMBERS </code>= <code>{9, 3456, 12, 237, 45679, 368, 296}</code></p>\n<p>then the contents of the array, <code>THREE</code>, is:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">If <code>NUMBERS</code> = <code>{1234, 56, 90, 324, 876}</code></p>\n<p style=\"text-align: left;\">then the array <code>THREE</code> is empty and a message such as “No such numbers”, should be outputted.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Consider the following algorithm.</p>\n<pre>N = 372<br/>X = N DIV 100<br/>Y = X + 10 * (N MOD 100 DIV 10)<br/>Z = Y + (N MOD 10) * 100<br/><br/>Determine the values of variables X, Y, and <code>Z</code> after execution of this algorithm. Show your working.</pre>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct this algorithm. You may assume that the array <code>THREE</code> is initialized with a sufficient number of elements.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a selection sort algorithm could be used to sort the array <code>THREE</code> in ascending order.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em><br/>X = 372 div 100 = 3;</p>\n<p>Y = 3 + 10 * (372 mod 100 div 10) = 3 + 10 * ((372 mod 100) div 10) = 3 + 10 * (72 div 10) = 3 + 10 * 7 = 73;<br/>Z = 73 + (372 mod 10) * 100 = 73 + 2 * 100 = 273;</p>\n<p><em>Award FT marks if working is shown</em>.<br/><em>Award 1 mark only for each correct value if no working shown, up to 3</em>.</p>\n<p>X = 3;</p>\n<p>Y = 73;</p>\n<p>Z = 273;</p>\n<p><em>Allow FT marks</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[8 max]</strong></em><br/><em>Award <strong>[1]</strong> for reset/starting from the first item in the collection and for initialization and correct increasing of the array index (K)</em><br/><em>Award <strong>[1]</strong> for the while loop through the collection</em><br/><em>Award <strong>[1]</strong> for retrieving a number from the collection</em><br/><em>Award <strong>[1]</strong> for if statement- checking whether the number retrieved is a three-digit number</em><br/><em>Award <strong>[1]</strong> for correctly calculated digits in retreived number,</em><br/><em>Award <strong>[1]</strong> for each</em><br/><em>Award <strong>[1]</strong> for if statement which compares the three digits of the retrieved number</em><br/><em>Award <strong>[1]</strong> for correctly placing ITEM into the array THREE</em><br/><em>Award <strong>[1]</strong> for the correct output message</em></p>\n<p><em>Example answer</em>:</p>\n<pre>K=-1<br/>NUMBERS.resetNext()<br/>loop while NUMBERS.hasNext()<br/>   ITEM=NUMBERS.getNext()<br/>   if ITEM&gt;99 and ITEM&lt;1000 then<br/>      F=ITEM DIV 100<br/>      S=ITEM MOD 100 DIV 10 //or S= ITEM DIV 10 MOD 10<br/>      T=ITEM MOD 10<br/>      if F&lt;S and S&lt;T then<br/>         K=K+1<br/>         THREE[K]=ITEM<br/>      end if<br/>   end if<br/>end loop<br/>if K==-1 then<br/>   output('No such numbers')<br/>endif</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/>The selection sort algorithm starts by finding the <strong>minimum/smallest</strong> value in the array THREE (containing K elements);<br/>and moving it to the beginning of the array THREE (element at the first position is THREE[0]) / exchanges it with the element in the first position;<br/>the correct entry is in the first place in the array and the process is repeated on the remaining entries / this step is then repeated for the second lowest value, then the third, and so on;<br/>once this has been repeated K-1 times;<br/>the K-1 smallest entries are in the first K-1 places which leaves the largest element in the last place (the array is sorted in ascending order);</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally found this question difficult. They were required to determine the values of a number of variables using the formulae given. These formulae included program code for integer division and the remainder of integer division. Unfortunately, candidates often ignored this and simply gave answers as though regular division was used, therefore the answers given were incorrect. However, some candidates achieved full marks, and others achieved marks for partially correct responses.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A wide range of marks were awarded for this question, in which an algorithm was written. The algorithm involved copying data from a collection, processing it and then copying some of the data that meets a test into an array. Candidates achieved credit for the correct elements of their algorithms. Some common errors included candidates using an array for the initial data retrieval, rather than a collection, and not correctly using a loop, to ensure all the data elements had been read from the collection.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates achieved high marks for this question, as they were able to correctly describe how a selection sort could be used to sort the resulting array from part b in ascending order. A few candidates lost marks because they used the wrong type of sorting algorithm, for example, a bubble sort or an insertion sort. Another error seen involved candidates sorting the data into descending order.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19N.1.SL.TZ0.10",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The names of students attending a science fair were recorded in a stack data structure as each one arrived.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">The first item stored in the stack was “<code>Sophie</code>”.</p>\n<p style=\"text-align: left;\">Note that “<code>Troy</code>” is currently in position 0 in the stack.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the pseudocode that will search the stack for a specific name, and output its position in the stack. You may assume that all names in the stack are unique.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the benefits of using a binary search tree, compared to a stack, when searching for a specific item.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>If the tree is populated with the data from the stack, the first item popped off will become the root. For each subsequent item popped from the stack, a recursive procedure is followed until the item is correctly placed in the tree.</p>\n<p>Without writing code, describe this recursive procedure.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>By considering only the data visible in the stack shown above, sketch the binary search tree that has been created from the items removed from the stack.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong> as follows:</em></p>\n<p><span style=\"text-decoration:underline;\"><em>Example answer 1</em>:</span></p>\n<pre>searchName(NAME, STACK)<br/>  DEPTH = 0<br/>  NAMEDEPTH = -1<br/>  while not STACK.isEmpty() do<br/>    X = STACK.pop()<br/>    if X == NAME<br/>      then NAMEDEPTH=DEPTH<br/>    DEPTH = DEPTH + 1<br/>  end while<br/>  if NAMEDEPTH == -1<br/>    then output(NAME + ’not found’)<br/>    else output(NAMEDEPTH)<br/>  endif<br/>end searchName</pre>\n<p><em>Award <strong>[1]</strong> for initialization.</em><br/><em>Award <strong>[1]</strong> for a correct while/until loop.</em><br/><em>Award <strong>[1]</strong> for correct comparison and assignment.</em><br/><em>Award <strong>[1]</strong> for updating and outputting correct position of <code>NAME</code> on the stack(<code>NAMEDEPTH</code>)</em><br/><em>Award <strong>[1]</strong> for use of stack methods (<code>pop()</code> and <code>isEmpty()</code>)</em></p>\n<p><span style=\"text-decoration:underline;\"><em>Example answer 2:</em></span></p>\n<pre>NAME=input()<br/>CSTK = STACK.copy() // must make a real copy,<br/>//any attempt to make a copy of stack is acceptable<br/>DEPTH = 0<br/>SEARCHING = true<br/>while SEARCHING and not CSTK.isEmpty() do<br/>  if NAME == CSTK.pop()<br/>  then<br/>    SEARCHING = false<br/>  else<br/>    DEPTH = DEPTH + 1<br/>  end if<br/>endwhile<br/>if not SEARCHING<br/>  then output(DEPTH)<br/>  else output NAME + \" not found\"<br/>end if</pre>\n<p><em>Award <strong>[1]</strong> for initialization.<br/>Award <strong>[1]</strong> for a correct while/until loop (even if flag is missing).<br/>Award <strong>[1]</strong> for correct comparison and assignment.<br/>Award <strong>[1]</strong> for incrementing and outputting correct position of <code>NAME (DEPTH)</code>.<br/>Award <strong>[1]</strong> for use of stack methods <code>pop()</code> and <code>isEmpty()</code>).</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong>.</em></p>\n<p><em><span style=\"text-decoration:underline;\">Example answer 1 (time efficiency)</span></em>:<br/>The data in the binary search tree(BST) is ordered;<br/>Such that as each node is checked for the item, half of the remaining nodes are ignored;<br/>But each element in the stack has to be checked;<br/>Which, for large data sets, may be inefficient compared to the BST;</p>\n<p><span style=\"text-decoration:underline;\"><em>Example answer 2 (memory efficiency)</em></span>:<br/>Each element on the stack has to be popped off/removed from the stack to be checked for the searched item;<br/>When the item is found the stack will be empty/stack contents will be changed;<br/>When an element in the BST is checked for the item it is not removed from the BST;<br/>So there is no need to create an additional copy of the BST (but a real copy of the stack must be created which for large data sets may be inefficient);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p><span style=\"text-decoration:underline;\"><em>Example answer</em></span>:<br/>The (next) stack item is (placed into a new node and) compared (alphabetically) to the root;<br/>If the root is empty it would be placed here (and this recursive procedure terminates);<br/>Else, depending upon the comparison, it would look to the node to the left or right and the (recursive) procedure calls itself again (with the new root);<br/>If it is lower than the root, then the left child of the root becomes the new root;<br/>If it is higher than the root, then the right child of the root becomes the new root;</p>\n<p><em><strong>Note</strong>: Marks should also be awarded for an algorithm constructed in pseudocode.</em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3]</strong></em>.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n<p><em>Award marks as follows</em>:<br/>Clearly a binary tree;<br/>Correct root;<br/>All values correct;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18M.1.HL.TZ0.13",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following circular linked list:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">where <em>head</em> is an external pointer that points to the first node in the circular linked list.</p>\n<p style=\"text-align: left;\">Three operations are performed on this circular linked list in the following order:<br/><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>Arrays and linked lists are used to store linear data.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Sketch a diagram showing the resulting circular linked list.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the last node of the circular linked list is identified.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the steps required to calculate the sum of all numbers held in this circular linked list.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare the use of arrays and linked lists.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A linked list can be used to implement a data structure queue.</p>\n<p>Identify <strong>two</strong> applications of a queue data structure.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for the node containing number 20 is pointed to by the pointer 'head';</em><br/><em>Award <strong>[1]</strong> for the diagram showing only two nodes and all the correct links;</em><br/><em>Award <strong>[1]</strong> for the last node containing number 5;</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.</p>\n<p>The last node is identified by its next/link pointer;<br/>which contains the address of the node at the beginning of the list / is equal to the pointer “head “;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p>Initialize (a temporary pointer with the head and) a variable sum with 0 (zero);<br/>Loop from the beginning to the end of the circular linked list / until all the nodes get traversed);<br/>Add the value (of the data field) (of the current node) to the sum;<br/>Change the temporary pointer so it points the next node of circular linked list;</p>\n<p><em><strong>Note</strong>: Answers written in pseudocode are acceptable</em>.<br/><span style=\"text-decoration:underline;\"><em>For example</em></span>,</p>\n<pre>sum = 0<br/>curr = head<br/>loop<br/>  sum = sum + curr.data<br/>  curr = curr.next<br/>until curr == head</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p>In an array, memory is assigned during compile time(predetermined) whilst in a linked list it is allocated during execution/runtime;<br/>Arrays are of fixed size whilst linked lists are flexible and can expand and contract its size;<br/>Array requires less memory (due to actual data being stored within the index in the array) whilst there is a need for more memory in linked list (due to storage of additional next and previous pointers/references);<br/>Elements are stored consecutively in array whereas elements are stored randomly in linked lists;<br/>Memory utilization is inefficient in the array whilst memory utilization is efficient in the linked list;<br/>Accessing an element in an array is direct (fast) while accessing an element in linked list is sequential/linear (slower) /to get into the nth element only the array name with index within the square bracket should be written whilst a linked list (to get the nth element) should be traversed starting from the beginning of the list, and traversing through the list until found/ ;<br/>Operations like insertion and deletion in arrays consume a lot of time whilst the performance of these operations in linked lists is fast;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.</p>\n<p>Print queues (a number of print jobs on a queue instead of waiting for each one to finish before specifying the next one);<br/>Queue is used for synchronization when data is transferred asynchronously between two processes (for example IO Buffers, file IO);<br/>Queues are used in CPU scheduling algorithms;<br/>Handling of interrupts in real-time systems (the interrupts are handled in the same order as they arrive, first come first served);<br/>Computer modelling of physical queues (supermarket checkouts) (call centre phone systems use queues to hold people calling them in an order, until a service representative is free);</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to sketch a diagram showing the resulting circular linked list.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A mixed set of responses was seen for this question. Most candidates knew that the last node in a circular linked list contains a pointer to the first node of the list. A few candidates incorrectly wrote that the next of the last node point to the null.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were required to write the main steps involved in an algorithm to calculate the sum of all numbers held in the circular linked list. Many candidates were well prepared, some candidates even wrote their answers in algorithmic form and they generally achieved better results than those who simply described the steps. The full range of marks, from zero to four were achieved for this question.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Was reasonably well answered with many gaining at least two of the marking points. Some candidates described two data structures individually without performing any comparison and thus losing marks.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Was well answered.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "21N.1.HL.TZ0.12",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Sketch a double linked list that holds the following sequence of names: Anne, Lana, Mary.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em><br/><em>Award <strong>[1]</strong> for showing that every node in a double linked list contains three fields (data and 2 pointers);</em><br/><em>Award <strong>[1]</strong> for showing every node has link to its previous node and next node/can be traversed forward by using next field and can be traversed backward by using previous field;</em><br/><em>Award <strong>[1]</strong> for showing that the first node must be always pointed by an external pointer (for example head) (and the last node could be pointed to by an external pointer (for example rear));</em><br/><em>Award <strong>[1]</strong> for showing that the previous field of the first node must be NULL and the next field of the last node must be NULL</em>.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display:block;margin-left:auto;margin-right:auto;\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates sketched a doubly linked list and answered this question reasonably well.</p>\n<p>Only a few candidates did not attempt this question.</p>\n</div>",
    "question_id": "19N.1.HL.TZ0.5",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A number of devices in and around the home can be operated by control systems.</p>\n<p>A home owner wishes to install automatic lights to illuminate a water fountain in her garden. These lights will automatically turn on at sunset and turn off at sunrise.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> hardware components that would be an essential part of this control system.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the concept of feedback, with respect to computer control systems in general.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The home owner has also installed a control system that waters the flowerbeds in the garden. This system aims to maintain the water content of the flowerbeds between a minimum and a maximum value. However, the system is only activated when the light intensity is below a certain level.</p>\n<p>Outline the algorithm involved in controlling the watering system described above.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying the hardware component and <strong>[1]</strong> for describing its purpose within the control system.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Sensor;<br/>To detect the presence/absence of the light;</p>\n<p>Microprocessor;<br/>To carry out any processing;</p>\n<p>Output transducer / Actuator / Activator;<br/>To turn the lights on or off;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The output value is (continuously) compared to the desired value; <br/>To produce an error value/difference between observed and measured; <br/>The controller uses the error value/difference between observed and measured;<br/>To determine the new input to the system;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong> for the following</em>:<br/><em><strong>[1] </strong>the system is turned on (when the light intensity is below a given value);</em><br/><em><strong>[1] </strong>for light loop/while input light intensity is below the given value the system continues being on;</em><br/><em><strong>[1] </strong>for comparison and activating watering / checking the water content of the flower beds and turning on water if the water content is not in a given range;</em><br/><em><strong>[1] </strong>for water loop / watering until maximum value;</em><br/><em><strong>[1] </strong>for turning off water when the water content of the flower beds is filled to maximum;</em><br/><em><strong>[1] </strong>for turning off system when the light intensity increases the given value;</em></p>\n<p><em><strong>Note</strong>: Candidates are not requested to construct an algorithm in pseudocode</em>.</p>\n<p><span style=\"text-decoration:underline;\"><em>Example answer</em></span>:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18M.1.HL.TZ0.14",
    "topics": [
      "topic-7-control"
    ],
    "subtopics": [
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A wireless local area network (WLAN) is used to extend access to a school’s wired local area network.</p>\n</div><div class=\"specification\">\n<p>The advantages of this WLAN are user-mobility and economical access points.</p>\n</div><div class=\"specification\">\n<p>The concept of packet data transmission is used within this network.<br/><strong>Figure 1</strong> shows the simplified structure of a data packet.</p>\n<p style=\"text-align: center;\"><strong>Figure 1: The structure of a data packet</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> hardware component of the WLAN, other than computers.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> disadvantages of this WLAN.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> ways in which the network administrator can reduce the risk of unauthorized access to confidential data.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>protocol</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to <strong>Figure 1</strong>, explain how data is transferred by packet switching.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[1 max]</strong></em>.<br/>Wireless router/modem;<br/>Access points;<br/>Switch;<br/>Wireless repeater/extender/booster;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying a disadvantage and <strong>[1]</strong> for an expansion, for <strong>two</strong> disadvantages up to <strong>[4 max]</strong></em>.</p>\n<p>Data transfer will decrease (compared with a wired LAN);<br/>Because the number of computers using the network increases;<br/>(and because) WLAN has lower bandwidth than a wired LAN;</p>\n<p>Less data security;<br/>As devices from outside the school can access the network/intercept transmissions;</p>\n<p>More easily open to misuse;<br/>As teacher/administrator cannot directly monitor a specific student/teacher/ machine;</p>\n<p>Intermittent connectivity due to physical barriers (walls);<br/>Results in low transfer/speed and may hinder operations.</p>\n<p><em><strong>Note</strong>: Accept any reasonable points, provided they are appropriately elaborated</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Give each user appropriate login details/passwords;<br/>Different access rights for students, teachers, school administrators (file-level and share-level security);<br/>All passwords and files should be encrypted;<br/>Use the latest WiFi protocol/WPA2;<br/>Require MAC address authentication;<br/>Password protect the documents;</p>\n<p><em><strong>Note</strong>: the focus of the question is on confidential data (firewalls not accepted)</em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Set of rules for data transmission;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6]</strong> as follows</em>:</p>\n<p><em>Award up to <strong>[3 max]</strong> for a general description of how data is transferred by packet switching. ([1 max] if this general description is very simplistic and refers to just the Header/Data/Trailer already shown in the question paper)</em>.</p>\n<p><em>Award up to <strong>[3 max]</strong> for added detail that references the contents of the given data packet in the answer</em>.</p>\n<p><em><strong>Example of general description</strong></em></p>\n<p>Data is organized in specially formatted units (data packets) which are routed from source to destination using network switches and routers;</p>\n<p>Network switches and routers determine how best to transfer the packet between a number of intermediate devices (routers and switches) on the path to its destination (rather than flowing directly over a single wire on the path to its destination);</p>\n<p>Data packets are reassembled at the destination;</p>\n<p><strong>Example of referencing content</strong></p>\n<p><em><strong>Addresses</strong></em> have to be in a standard format so that each switch/routing station recognizes the address;<br/><em><strong>Address of sender</strong></em> identifies the sending computer, so that any packets not received can be re-requested;<br/><em><strong>Address of receiver</strong></em> identifies intended recipient so it can be forwarded on correctly;<br/>The <em><strong>protocol</strong></em> used must be identified so that the correct rules are followed;<br/><em><strong>Size of packet / size of fields in packet</strong></em> – All packets/fields must have the same size so that the data can be reassembled;<br/><em><strong>Sequence number</strong></em> so that packets can be reassembled in correct order;<br/><em><strong>Transmission codes</strong></em> to show whether the data packet is transmitted or re-transmitted;<br/><em><strong>Control bits</strong></em>, to maintain the integrity of the data by ensuring that the data received is the same as the data sent;<br/><em><strong>Error checking code</strong></em> – when an error is detected, an algorithm either corrects the error or requests that the packet is resent;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "17N.1.SL.TZ0.12",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline what is meant by <em>virtual memory</em>. </p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Virtual memory is a memory management capability of an OS;<br/>that (uses hardware and software to) allow a computer to compensate for physical memory shortages by temporarily transferring data from random access memory (RAM) to disk storage;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates provided the generally correct answers. Only a few were not able to outline the meaning of \"virtual memory\".</p>\n</div>",
    "question_id": "19N.1.HL.TZ0.7",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A washing machine manufacturer has created its website to be viewed on standard desktop computers as well as mobile devices. The mobile browsing experience differs from desktop browsing.</p>\n</div><div class=\"specification\">\n<p>Different devices such as desktop computers and mobile devices have different operating systems.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>screen resolution</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> issues resulting from the website being viewed on various devices, such as desktops and smartphones.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the role of the operating system (OS) in terms of managing the hardware resources.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A washing machine uses a control system.</p>\n<p>The microprocessor controls the washing machine and its actions. To complete the wash and rinse process the user selects the program, loads the washing machine and pushes the start button.</p>\n<p>Describe the interaction between the sensors, microprocessors and output transducers in this situation.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>Screen resolution is the number of pixels a screen can display (horizontally and vertically). (For example, the screen can show 1024 pixels horizontally and 768 vertically);</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/>Screens that are different sizes (for example, 3–6 diagonal inches for a phone, and 9–12 diagonal inches for a tablet, size of 13–17 inches for a notebook screen, and a desktop screen size of 20–30 inches);<br/>can still have the same screen resolution (for example, a laptop could have a 13-inch screen with a resolution of 1280 x 800 and a desktop computer could have a 17 inch monitor with the same 1280 x 800 resolution);<br/>but physically smaller screen will not show less of the website;<br/>the screen with the higher resolution will be able to show you more of the website because that screen has more pixels and the image will be sharper;<br/>but elements on the screen (icons and text) will look smaller;</p>\n<p>Most mobile displays currently have screens with fewer pixels than desktop displays and are physically smaller;<br/>typing on small on-screen keyboards is difficult;<br/>less precision (clicking a 12-pixel-high text link with a mouse is no problem but tapping the same link with fingers could be difficult);<br/>there is no mouse pointer so there is no concept of \"hovering\" over a page element/ remaining in an uncertain state;<br/>most modern touch screens allow zooming;<br/>allow the user to perform gestures using one or more fingers, such as swiping/pinching;</p>\n<p><em><strong>Note</strong>: Award marks not only for issues for a viewer but also for issues for a creator of the interface</em>.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/>OS controls all the activities of computer system and acts as an interface between user and hardware;</p>\n<p><em>Thus the role of OS is</em><br/>to keep track of who is using which resource;<br/>to grant resource requests;<br/>to mediate conflicting requests from different users/programs;<br/>to allocate time to different programs or different users/ each one gets their turn to use that resource (for example printer);<br/>to allocate space for different users, each one gets a part of the resource (for example sharing main memory/ hard disk);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em><br/><em>Award <strong>[2 max]</strong> for evidence that</em>:<br/>Sensors (input devices) detect/measure the water level;<br/>sensors detect/measure the temperature (of the water);<br/>sensors detect/measure the dampness/moisture level of the clothes;<br/>sensors also detect movement of the machine's drum and other associated actions;</p>\n<p>Sensors continuously take readings/measurements (in the context of above) and send these readings to the processor;</p>\n<p><em>Award <strong>[2 max]</strong> for evidence that</em>:<br/>processor controls sensors, valves and actuators responsible for controlling the parts that clean clothes;<br/>processor determines what actions the machine should take next;<br/>the washing machine has been programmed/ it goes through a process of running its internal programs;</p>\n<p>processor compares readings with pre-set values (in the context of the various sensors);<br/>if the readings fall outside of the specified range, the processor sends a message to the output transducer to switch on/off ... (in the context of part of the washing machine);</p>\n<p><em>Award <strong>[2 max]</strong> for evidence that</em>:<br/>output transducers are used for turning on and off devices that control the rest of the machine;<br/>such as the motors that spin the tub;<br/>or the water pump;</p>\n<p>Example:<br/>once the start button is pushed the washing machine begins to dump water into the drum, a sensor will detect that the water level has been reached;<br/>based on the setting, the processor will allow the water to flow only to a predetermined level;<br/>it will send the signal to output transducers to shut off the water;<br/>and begin the agitation process;<br/>once the timer tells that it is time, processor sends signal to output transducer to stop agitating;<br/>it then begins the spin process/ removing the dirty water from the machine at the end of the spin cycle, the washing machine's processor sends signal to turn on the water pump and sucking it out of the machine;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly a well answered question.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly a well answered question.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly gave good responses for this question.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A wide variety of answers and quality of answers was seen for this question covering the full mark range. Candidates who were able to fully describe the interaction between the sensors, microprocessors and output transducers and the steps involved generally scored quite highly.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19N.1.HL.TZ0.11",
    "topics": [
      "topic-6-resource-management",
      "topic-7-control"
    ],
    "subtopics": [
      "6-1-resource-management",
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Trucking On</em>, a logistics and haulage company based in Marseille, France, keeps track of its lorries and operations in a database.</p>\n<p>Lorries are made up of the truck and a trailer. Trucks are usually coupled with the same trailer but sometimes trailers are moved to a different truck for part of the year.</p>\n<p>Each driver only uses one truck.</p>\n</div><div class=\"specification\">\n<p>The following LORRY table shows information about the lorries.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Trailer space is measured in cubic metres.</p>\n<p>The table can also be represented as:</p>\n<pre>LORRY (TruckID, Truckmake, Energysource, Driver, Tel, TrailerID,<br/>Trailerspace, Coupled_from, Coupled_to)</pre>\n</div><div class=\"specification\">\n<p>The table has been split into two tables following the rules of normalization. The resulting two tables are shown with the primary key underlined:</p>\n<pre>LORRY (<span style=\"text-decoration: underline;\">TruckID</span>, Truckmake, Energysource, Driver, Tel)<br/><br/>TRAILER (<span style=\"text-decoration: underline;\">TrailerID</span>, Trailerspace, Coupled_from, Coupled_to, TruckID)</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> characteristics that make a database unnormalized.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why data redundancy may be a problem in the LORRY table.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the steps to create a query to find the names and telephone numbers of the drivers who drive lorries that have more than 60 cubic metres of trailer space.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Normalize the database to 3NF. Use the same format as shown, ensuring that primary keys are clearly indicated by underlining them.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Note: Allow answers that refer to any stage of normalisation</em>.<br/>Each data item hasn’t be broken down any further / contains repeating data;<br/>Each row is not unique / does not contain a primary key;<br/>Each field/column does not have a unique name / does not have atomic values;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>This would mean that data is duplicated within a table / allow an example of duplicated data in this table;<br/>However, when it is updated, it may not be updated in all cases;<br/>Which could lead to anomalies within the data and the incorrect information being used;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for all relevant tables selected (Lorry and Trailer);</em><br/><em>Award <strong>[1]</strong> for all relevant fields selected (Driver and Tel);</em><br/><em>Award <strong>[1]</strong> for correct condition;</em><br/><em>Award <strong>[1]</strong> for correct link between tables;</em></p>\n<p>SELECT Driver, Tel<br/>FROM Lorry INNER JOIN Trailer<br/>ON Trailer.TrailerID = Lorry.TrailerID<br/>WHERE TRAILERSPACE &gt; 60;</p>\n<p><em>Accept logically equivalent answers written in English or a Data Manipulation Language</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[7 max]</strong></em>. <br/><em>Award marks as follows</em>:<br/>Separate, correct DRIVER table;<br/>FK links Driver to TRUCK table (driver always drives same truck);<br/>New “journey” table created — called JOURNEY in examples below;<br/>JOURNEY PK either a new JourneyID field, or a composite key as shown below;<br/>Correct FK links to TRUCK and TRAILER tables in JOURNEY table;<br/>Coupled.to and Coupled_from in correct table;<br/>TRAILER table + PK correct;</p>\n<p>DRIVER (<span style=\"text-decoration:underline;\">Driver</span>, Tel)<br/>TRUCK (<span style=\"text-decoration:underline;\">TruckID</span>, Truckmake, Energysource, Driver)<br/>JOURNEY (<span style=\"text-decoration:underline;\">TruckID, TrailerID, Coupled_from</span>, Coupled_to)<br/>TRAILER (<span style=\"text-decoration:underline;\">TrailerID</span>, Trailerspace)</p>\n<p>DRIVER (<span style=\"text-decoration:underline;\">Driver</span>, Tel)<br/>TRUCK (<span style=\"text-decoration:underline;\">TruckID</span>, Truckmake, Energysource, Driver)<br/>JOURNEY Journey <span style=\"text-decoration:underline;\">ID</span>, TruckID, TrailerID, Coupled_from, Coupled_to)<br/>TRAILER (<span style=\"text-decoration:underline;\">TrailerID</span>, Trailerspace)</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a memory recall question, where many candidates were able to identify at least one characteristic of unnormalized databases. However, many others were not prepared for this question.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was connected to a given scenario. Successful candidates were able to provide examples of redundant data affecting the given table, but just few candidates were able to outline consequences.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to get the basic structure of the query though some of them could not set the two tables and their relationship properly while writing the query. Also, many candidates provided generic answers failing to provide the necessary details (fields, tables, conditions) to answer the query.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates seemed unprepared for this question, most of them were not able to correctly split the table, identify the relationships and in many cases did not identify primary keys (this being explicitly required in the wording of the question).</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.3",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A bus company provides services within a city. Passengers can look up the distance between any two bus stations on any of its routes.</p>\n<p>For each route, a one-dimensional string array is used to store the names of all bus stations on the route and a two-dimensional array is used to store the distances between the bus stations (in kilometres). Only the lower triangle of the two-dimensional array is used to store the distances.</p>\n<p><strong>Figure 1</strong> shows data about Route X, a bus route between Oppox and Dovely.</p>\n<p style=\"text-align: center;\"><strong>Figure 1: One-dimensional string array, <code>ROUTE_X_NAMES</code>, and</strong><br/><strong>two-dimensional array, <code>ROUTE_X_DISTANCES</code>, for Route X</strong></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p>For example, the distance between Kingsley and Kronos (2.0 kilometres) can be found in <code>ROUTE_X_DISTANCES [7][5]</code>.</p>\n</div><div class=\"specification\">\n<p>The two-dimensional array <code>ROUTE_X_DISTANCES</code> is valid if all the entries on and above the main diagonal are zero and all the entries below the main diagonal are greater than zero.</p>\n<p><strong>Figure 2</strong> shows an invalid form of <code>ROUTE_X_DISTANCES</code>.</p>\n<p style=\"text-align: center;\"><strong>Figure 2: Invalid form of two-dimensional array <code>ROUTE_X_DISTANCES</code></strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkoAAAFSCAYAAAANTvDyAAAgAElEQVR4nO3dQXLbOPo34Le/6j2zjw3vO6H2nVAHiKkDxNTsx1Tv25L3Y8kHiOgDhMzsR8zs/2Jl32L1AYTyAcwT8FsogElJkChSFEHN76lyVbck2ngBCnwJgMgvaZqmBAAAAAAb/l/TBQAAAADQFRIlAAAAAAUkSgAAAAAKSJQAAAAAFJAoAQAAACggUQIAAABQQKIEAAAAoIBECQAAAEABiRIAAACAAhIlAAAAAAUkSgAAAAAKSJQAAAAAFJAoZQSBT4xdEGMX5HnTjfc552Tb18TYBVnWB4rjxdbPiN/hODenKLbSvniSJKHh8I4Yu1D+jjbF43lTMs13sn2CwN/4TNviEefbrnNSl3jEucTYBUXRXPm5KJor49lXJ6e0Lx7xXvaHc577TJviEcbjB/k5y/qQe68t8TjOzdb2WS9zW+IhWn1vLOsDMXZBpvlua//WpniCwJf9tW1fb72eahNPCpLvf02n0y/K929uPqcvLy/K/89aLpfpzc3no5fxELvimU6/pLe3/0zn8/9LP378fe/v0j2e2ew/6cePv6eLxV/ys5eXb+X/r9M9Ht//ml5ff0qXy2Wapmm6WPyVXl9/Sn3/69bP6xDP3d2f6Xz+f3s/9/Hj7+nNzeed37V938VT2BdPke+N0IZ4Xl5e0uvrT+nt7T/leaeiezw3N5833ptOv+z8vukcz3K5TK+vP+WuN9n+YV0b4nn//jf5/svLS3p7+0/l72o6HowoFRSGM+p2u2QYhnyt2+1uzerbwHUH5HlPZFndpotyFLPZjCaTRzLNDhEROU6fRqN7iqKo4ZKV4zh9CsPvxBgjIiLT7FC/36ckSRouWTWeNyXGrqjX6zVdFFgzHj9Qr9cjz3uS511bBcG3jb5tNpuRZVkNlaiaKJpTr9fLXX/6/T6F4azBUpUXhjMaDP6QbWQYhtbxIFEqaLFYbHQepmnSYrE5XAinp0r62toxruOck+/7ZJpm00UpLY4XNB4/0GTy2HRRYIs4jsl1B00XoxZJklCSvMgbqbZhjNFslk8iFotFLnFqO865ttdTJEoFrb5om3fzbb/DP1diTrytHaMg5ugt6wP1+/1WjwAOh0Maje5bP1ohZNeHMXZBrnvbdJFKi+PVRTeK5rl1I+fSvwWBT7bd3lFMy+pSr9fLrZE1DIMcp9900Uqx7R5Np19kP+15U/J9f2ONny6QKMHZiaL52dwdO06fOH+myeQx17G0jViIeQ5tInD+nPvpdDo0HN41XaxSOOfE+ZLG4zEFwTfi/JlGo1FrlxasWywW1O229yaDaBWD5z0R588Uhv8lznlrE1nGGI1G9+S6t8TYBc1mMxqNRtrG82vTBWgL1RDnOQ19noMg8ClJkrO6IBOtEibTNGk4HFIYtq/Dn8/nFMeLrU9YnktbOU6fbPtT08UojXNOYfgkR2Etq0vT6bT17ZMkCUXRnDzvqemilOZ5U+p0OnI01jAMuUa2re3jOP3ciNjqqWU9lxYgUSqo0+lszJ/GcUydTrunds6JuPtta8dxzoLg29r/n19Cy/mSDONN08UoxbK6W6dEz+FGMIrmrZ2iylpvC8YYzeftHGHeRowq6QhTbwXZdo/iOJZ7PXDOaTabncUX8ByIpyWy7REEfmunQlz3lobDOzkUHccLGg6HeFpME45zQ657ezbtYxgGDQZ/0Hg8lq953vQsEqXZbNb6G1rb7pHv+7mpqel02vrpRKLViJ/j3FCv19N2DSZGlA4wmTyS43z+uYiTkec9tbYj8bwpjccP8v/FlIjj9Fv5VJJYSLueGLU1kfW8JxqPH8g03xHR64Ws7SMwnPONTQzbGFMQfMu1DxH9XHPRvlgEx+nTcrmUfYFpdjZGAtsojhetnnYjEmt6RmTbn4hzToZh0Gh0r21isU8cL8i2r4nodesTnftqJEoHYIxRFP1ouhhH4bqDVnfq6zh/broIRzca3dNodN90MY6KMXY2bXWO7XOOMZ1Ln21Z3bOJxTQ7reoHMPW2RmzfX3a7dPHI8Ppdc1MQTx7iqZf4pyPKPp0ntkPQZcoU8eQhnnohnjxd4vklTdO00RIAAAAAaAojSgAAAAAKSJQAAAAAFJAoAQAAACggUQIAAABQQKIEAAAAoIBECQAAAEDhrDac3PYPbgIAAACU3eTyrBIlovPaoZmxC8SjMcSjN8SjN8Sjt3OMpyxMvQEAAAAoIFECAAAAUECiBAAAAKCARAkAAABAAYkSAAAAgAISJQAAAAAFJEoAAAAACkiUAAAAABSQKB1gOLwjxi7kj+veEue86WKVhnj0xTknx7nJxTMePzRdrNIQj94Qj94QT8PSCnz/a3p5+Ta9vHybTqdftn7m5uaz/Izvf914f7lcyvdvbj5XKU56efm20vG7PDz8K33//jcZw3K5TK+vP6UfP/5e299EPMWdWzzX15/S6+tP6WLxV5qmaTqb/Sd9//639O7uz9r+JuIpDvFUh3iKQzzVVYmncqKkSpDSdHXxEhWx7f+zlsul1onSx4+/bzSiSBSXy2UtfxPxFHdO8Yibh/Ubi7u7P1uZ+CGe40A8xSCe40A8r2qbekuShDjnZJod+Vqv16PpdFrXn6wN55w453R1dZV73bK6REQUhrMmilUa4tFbFM2JiMg0zdzrnU6HOOcUx4smilUa4tEb4tEb4mlebYlSFM2JMZZ7zTQ7spLaRDTcejyGYRAR0cvLy8nLVAXi0dtyuSQiIsbyiZ+Ip23rrhCP3hCP3hBP82odUUqSZOvr50bHhq0C8egN8egN8egN8ehNx3jw1BsAAACAQm2JkhhGK/p6m61P+bQd4tEb4tEb4tEb4tGbjvHUlihZVpfiOM69FkVzucC2TcSC9PUhQTGN+ObNm5OXqQrEozexKJ3zZe51EY+OHckuiEdviEdviKd5tY4oWZZFQeDL16bTKQ0Gg7r+ZG0YY8QYk4vQBNXqfd0hHr2Jm4n1G43FYkGGYeSeJG0DxKM3xKM3xNO8WtcojUb3NJvN5M6bvV5Py0oowrZ7FAS+TPw45+T7Pplmp5WjZIhHX4wxMs0OTadf5BN9YTijMJyR4/QbLt3hEI/eEI/eEI8GqmzgtG/DyUPovuHky8tLenf3p9xFXOwkXtdmhmmKeA5xbvEsFn/ldrW/vHxb6661aYp4DoF4qkM8xSGe6qrE80uapmnZJCsIfBoO74hoNXrkuodPq3HOybI+ENFqSC4IvpUtDjF2QZw/lz5eN4hHb4hHb4hHb4hHb4jn1a9V/rDj9CsPlTHGzqoxAAAA4HxgHyUAAAAABSRKAAAAAApIlAAAAAAUkCgBAAAAKCBRAgAAAFBAogQAAACgUGkfJd0wdtF0EQAAAEBDjeyjpKNz2pMJG37pDfHoDfHoDfHo7RzjKQtTbwAAAAAKSJQAAAAAFJAoAQAAACggUQIAAABQQKIEAAAAoIBECQAAAEABiRIAAACAAhIlBc+bEmMX5Dg3hT9vmu+IsQti7IJc95bieFFzKasZDu9keUWZOedNF6u0c4qHc06Oc5OLZzx+aLpYpbU1nux3Ovuz67zinJPr3srP2vY1RdH8hKU+XFvbRwXx6K1t8VRKlILAl0F63nTrZzxvSpb1QdlRcM7l7yialNQtiuYHNdp4/EDj8QMlSSJfC8MZOc6Nthfq8fiBwnBGk8kjcf5MUfTj58n7uemilXJu8bjuLSVJQmH4nTh/Js97oiDwaTi8a7popbQxnjhe5L7TRSRJQo7zmcJwlvs9jnOTe003bWyfXRCP3toWT+URpdHonjh/Jtcd5F6P4wXZ9jUREVlWV3k8Y0xe2HQwHj8clLAlSSKTRNcdEOfPFIbfyTCMnyeCnp1jGM7ItnvkOH0iWrVDv98nzrm2yd0u5xQP55zieEH9fp9Ms0NERLbdI9vuaT8ysU1b44njmIhWZeX8OffDGNt6TBD4xDknwzAoin5QHP8tY9b1jrmt7aOCePTWxnhqm3ozzQ6F4feNBEpnw+Hdzyk3Jhtwn2zDDgZ/ENEqdnH8crk8fkErEsnD1dVV7nWR0Oqa3KmcWzzinDJNM/d6p9ORnUybtDUe8d3tdIr1BURE8/kqVsvqEmOMDMOgwWDVB+oaa1vbRwXx6K2N8WCN0prR6J6i6MdGI6pk7zYNwyCi1SiTaOz1i7cORNnW74pF+V9eXk5epirOLR5xgWYsf+6IeNo2QtbWeKIoIiIi3/8qlwcMh3eVyitGqXTS1vZRQTx6a2M8SJQyJpPHo4yAiflXwzDkVFCb6HiiVoF49KZrPCIBz5YvCHxynM/KtUviBiuK5vJ43/drLmm9dG2fshCP3nSMB4nSkTnOjRxanEweZZYMAO0hbnQYYxQE34jzZwqCb0S06siDYHvy0+//Q65PtO1rYuxC23UXAFAMEqUjWT3tkk+SbLvXcKnKUS1UbSvEozcd4zEMg+L4b4qiH3Ktm2V15Xd6sdi+jkIkVtkHWEaj+9zvbRsd26cKxKM3HeNBonQkrnsrkyTPe9J6yk0sNF8f4hTTCW/evDl5mao4t3jEujbO8w8CiHh07Eh2aWM8YTgjxi7INN8dfKxpduQo1PoTcjrG2sb22QXx6K2N8SBROoLx+CGXJOk+ksQYI8bYxhN5qqcRdHdu8YjRiPWFv4vFggzDKPxEpi7aGI/orLPbf0TRXD5B2e1u3/JEbFTL2MXGGqXs07A6aWP77IJ49NbGeGpLlKJoLjuM1QLI1S6clvWhrj95Eus7dnPOc5ttZnfkFU/J6Mi2exQEvlxrwTkn3/fJNDs7973S1TnFI7anmE6/yIttGM5+bmKq70ilShvjMc2OvOEZjx9y33nT7Mhyr/cHtt2T02vra5Qmk8mpwyikje2zC+LRWxvjqS1RsqzuxiZtOm0seSxt26NHGAz+IMfpy3/2w7I+kGEY5HlPTRetlHOLZzKZEGNX8mLrurdk273cepc2aWM8nve0UT7XHVAYflcewxgjz3vK3RVbVpfC8LuWd8pCG9tnF8Sjt7bF80uapmnZg7Nbjo9G96Uereecy1Emy+rKJ0vKWP0bTM+lj9cN4tEb4tEb4tEb4tEb4nn1a5U/7Dj9ykNl4p8wAQAAANANFnMDAAAAKCBRAgAAAFBAogQAAACggEQJAAAAQAGJEgAAAIACEiUAAAAABSRKAAAAAAqVNpzUDWMXTRcBAAAANNTIhpM6OqfNK7Ezqt4Qj94Qj94Qj97OMZ6yMPUGAAAAoIBECQAAAEABiRIAAACAAhIlAAAAAAUkSgAAAAAKSJQAAAAAFJAoAQAAACggUVrjeVMyzXfE2AUxdkGue0txvKjtuFPxvCkxdkGOc1Po82E4I8v6IOPxvGnNJaxuOLyT5RVtwDlvulilcM7JcW5y8YzHD00XqzTEozfEozfE07C0At//ml5evk0vL9+m0+mXjffn8/9Lr68/7fzMcrmU79/cfK5SnPTy8m2l4x8e/iXLkv15//63dLlcHv24farGI8zn/3dQHc9m/9kaz8PDvyqV41jxbPPw8K/0/fvfUt//mqbp6ry6vv6Ufvz4e21/s854rq8/pdfXn9LF4q80TVdt8v79b+nd3Z+1/U3EUxziqQ7xFId4qqsST+URpdHonjh/Jtcd5F7nnJPr3lK/3yfOnykMv5Pvf90YmWCMEefPFEU/qhalkiRJZNlcdyDLbBgGJUlCYTg76nGnMh4/FB5FEnzfJyIi2+4R5880mTwS0WpUKkmSo5fxGMJwRrbdI8fpE9HqvFqde7x1o0qcc4rjBfX7fTLNDhGt2sK2exRF84ZLdzjEozfEozfE07zapt7CcEaDwR/ywmWaHZpMHmk+17Misg00GPxBRKsyi4ZcLpdHPe4UhsO7n1NuTJZnnyRJZEz9/qrtHKdPhmEQEWl5Iotk6OrqKve6ZXWJiBpPVg8l6tg0zdzrnU5HdjJtgnj0hnj0hniaV1ui5LqDjVEmIqJer1fXn6xEjJ5w/iyTgiRJZKOtX4SrHncqo9E9RdGPjZNShfPXxI6xq43/1nF0RtQ1Yyz3umiPl5eXk5epCpFcZ+uf6DUeHdtgF8SjN8SjN8TTvJP9o7icc5rNZnIapw1c95aSJCHDMOTIWJ3HHVuZulZNrYmTuI10/OJVgXj0hnj0hnj0pmM8J0mUOOc0Hj+Q5z2d4s8dhePcyCHCyeSxcKJQ9jgAAADQT+3bA8TxolVJUpIkG8mObe+fLix7nG5UiZ2ui7iLWJ+SazvEozfEozfEozcd46k1UYrjBfm+35okiWg1bSaSHc97Kjx1VvY43WTnjZPkdW2PWLuk40ksFqqvD9mK5O7NmzcnL1MVYl1bdr0Y0Ws8OrbBLohHb4hHb4inebUlSpxz8n0/t06Gc06W9aGuP1nZePyQS3aKjgiVPU5HhmHIxGM6XW17EIYzeRKLJ8l0whgjxtjGE4aqpyt0J+o4juPc64vFItc+bYF49IZ49IZ4mldbojQc3lEQ+LmdN3VOkjjnuT2eXPc2V/bh8I6INne4Lnqcrrbt2D0YrJ5WDMOZ3OGaaPUko65rrmy7R0HgUxCs9oASibppdrRM7nYR2zlMp1/kE31hOKMwnLVypBLx6A3x6A3xNK+2xdxB8K2uX12LsnvttG2PniJsu0ee90Tj8YOcznLdAY1G9w2XTG0w+IOSJKHh8E4mp5bVbdVTllmTyYTG4zHZ9rV8zXH6WrfBLohHb4hHb4inWb+kaZqWPTgIfHlRGo3ut+6btE92Os6yupUSLMYuiPPn0sfrBvHoDfHoDfHoDfHoDfG8qjSi5Dj9ykNl4p8wAQAAANBN7dsDAAAAALQVEiUAAAAABSRKAAAAAApIlAAAAAAUkCgBAAAAKCBRAgAAAFCotI+Sbhi7aLoIAAAAoKFG9lHS0TntyYQNv/SGePSGePSGePR2jvGUhak3AAAAAAUkSgAAAAAKSJQAAAAAFJAoAQAAACggUQIAAABQQKIEAAAAoIBECQAAAEABiRIAAACAQqVEKQh8YuyCGLsgz5tuvB9Fc3LdW/kZ173d+AznXL7vODdVinMUnPNcmW37mqJoXttxdfO8KZnmO1mu8fjh4N9h29fE2AUNh3c1lPAw6/G47i3F8aK245rCOSfHuZHlLdt2ukA8ekM8ekM8DUsr8P2v6XT6Zet7y+Uy/fjx93Q+/780TdP05eUlvbn5nN7d/an8/M3N5yrFSS8v31Y6frlcpu/f/5ZeXr7d+Fkul0c/bp+q8UynX7aW6ZB6zv4OVdsVVTWeh4d/bY3n/fvfdtZz2eP2qRrPLtfXn9Lr60/pYvFXmqZpOpv9J33//rfKbbAL4ikO8VSHeIpDPNVViae2qTfGGEXRD7KsLhERGYZBk8kjcc7r+pOVjccPlCQJmWaH4vhviuO/yTAMIiLy/a9HP65OSZLQdPqFiIhsu0ecP9NodE9Eq5G+Iu3AOZe/o2lJkshRS9cdEOfPFIbfyTAMSpKEwnB21OOaxDmnOF5Qv98n0+wQ0aoNbbunxSjloRCP3hCP3hBP8066Rmk4vKNut3vKP1lY9qLZ6/XIMAwyDIPi+O9cknGs4+qWJAk5Tl+Wi4jo5eWFiEiWcZ/h8I6SJKmvkAfIfoEGgz+IiMg0O/KLtlwuj3pck0SZTdPMvd7pdGQn0yaIR2+IR2+Ip3m1J0rZNUiMMXLdQd1/shTO8xdMsZ5l31qjssfVjTFGo9E9cf5MltWV68gYY+R5T3sTpSDwKYrmZFldOSrYJDEqxvmzLHuSJPJLdXV1ddTjmiSSN8byZRPl13lUdhvEozfEozfE07zaEyXGmJzuiON466JvHWRHTsRUGhFRHC/IcW6UWW7Z404pW8ZVxh7v/bxYWDeZPNZatipc95aSJCHDMOToWZ3H6ULHjqQKxKM3xKM3xFO/k029mWaHwvA7TadftJnOURFrjaLoBzHGiIhoOt2f4JU9rm4iWQ2Cb0S0SuiCwFd+Xky5jUb3Mg7dOM6NHLGbTB4LTSVWOQ4AAP43YR+lLcRaI8YY2fZqfU+RkaGyx51KdhptPt8+LRiGMwrDGZlmR8tp0tXaq3yyI+q6juN0pGvyWhbi0Rvi0RviqV9tiZLnTXNTT+JCZVldLe/ixeLeUx1Xtyiay7Vhh4zgiQQqjhfyeJFciH2zmhwadd1bWR7Peyo8dVb2uCaIdVPr699EO+rYkeyCePSGePSGeJpXW6LkugPqdrtyE8bVIufVQmIdGYYhR1t8/ytxznNPtKkSorLHnZJ4xD+K5jJZ6HSaL9ehxuOHXLJTdESo7HFNEefT+lqyxWJBhmFocU4dAvHoDfHoDfFooMoGTrs2nDyUDhtOLhZ/7d2YUGzAmC1rkeOaiOf29p9by3V9/Ul+Zls8625uPje+4eRyudway/pmmOvxFD3u1PHsc339Kf348feNDdkeHv5V299EPMUhnuoQT3GIp7pGN5wcjx+U/4RJEWL7AMv6ULUolYkF59nH4S2rS0HwbedwYNnj6uZ5Txv7OLnugMLwe0MlKq/sxpA6bihZxGQyIcau5D8f47q3ZNu9xvblqgrx6A3x6A3xNOuXNE3TpgtxLKv1M89NF+NoEI/eEI/eEI/eEI/eEM8rPPUGAAAAoIBECQAAAEABiRIAAACAAhIlAAAAAAUkSgAAAAAKSJQAAAAAFJAoAQAAACic3T5KAAAAAOvK7qP065HL0ThskKUvxKM3xKM3xKM3xKO3KgMpmHoDAAAAUECiBAAAAKCARAkAAABAAYkSAAAAgAISJQAAAAAFJEoAAAAACkiUAAAAABSQKK1x3Vti7GLjJwj8rZ+PovnWz4sfx7k5cQRqtn1NjF3QcHi397OH1sOpeN6UTPOdLM94/FDoOF3jEUT5PG9a6PNl66EpnHNynJtc3ete5l0Qj94Qj97aFk+lRCkIfBnkrg4+SRIyzXdbkwbOuVZJRRTNmy5CLTxvSnG8KPx5HevB86Y0Hj9QkiS514qcNzrGI3jelMJwdtDny9ZDU1z3lpIkoTD8Tpw/k+c9URD4hZJ2HSEevSEevbUunrQC3/+aTqdf9n7u7u7P9Obmc3pz81n5meVyufP9Ii4v31Y6frlcppeXb9P373+r9Htms/+kl5dv08vLt+l8/n+lf0/VeITlcpm+f/+bLNPd3Z97P3+MelhXJZ6XlxcZw+3tP9M0TdPp9IuMablcKo/VMZ40XZXr9vafMobLy7d7v09V6mGfY51v60T9+/7X3Ot3d3+mHz/+XsvfTFPEUxTiOQ7EU0wb46l96i2K5hSGMxqNRnX/qcrEiItldUv/jiRJZFY8Gt1X+l3HMhze5UYf9jlGPRxbkiTkOH0iIur1ekRE9PLyQkREhmGQYRjKY3WMh4jIcT5TGM7Isro7y59VpR6aIkbzTNPMvd7pdIhzftBIpw4Qj94Qj97aGE+tiZJIGkajezKMN3X+qaNYLFYNFMeL3HTgIdM2YkrENDvkuoO6ilpYEPgURXOyrG7hROEY9XBsjDEaje6J82eyrK6c7mWMkec97UwQdIyHiIixK5pMHikIvhVOcKrUQ1OWyyURreLNEmXlnJ+8TFUgHr0hHr21MZ5aE6Xp9AsxdiXvgHUXxzER5RsqiubkODeFslzOuVwcrMMIWpIkcoHcZPJY+Liq9VC37OjY6g4k3vl5XeMJgm+VvhuH1oOudOwYq0A8ekM8etMxntoSpTheUBD4B12gdTGZPBLnzxRFP4gxRkRE4/F473G+/5WIiEyzo8U0j5hyG43uZRyHKFsPdWOMEefPFATfiGg1ilfk6TVd4ymrbD0AAEBxtSVKURRRkiRkWR+IsQuyrA/yLl5XQfCNOH+Wd/mMMer3/0FEVGjkQVykxNqRJoXhjMJwVmoKsGo9nEp2OnE+V0+jtSWesorWg67KJPE6Qzx6Qzx60zGe2hIl1x0Q58/yJ4p+kGV15d2vbpIkketXylw843ghp0Jsu/lESVwws+tyxJocsa3DtiHOqvVQl+x+VYcsTNc1nrLK1kOTrq5WaxE4X+ZeF+XXsWPcBfHoDfHorY3xYMPJn7JPDInpGM65nE7bl/xEUSR/j44NXVTVejiF6fQLEa2SBpH8dTqdrZ9tQzxlHVIPTRKjXetrqBaLBRmGQaapX5l3QTx6Qzx6a2M8J0mULOtDK6beBoM/iOj1rt2yPhDnnAzDkO+tnjDa3BxTrOTXYW0S0et6nOyPKJvj9Inz559PSm3GU6QeTs2yujKpWS9zdnqxLfEUtR5P0XrQCWOMTLND0+kXOaonpobb8qBHFuLRG+LRWxvjOUmiFEU/5MVa16k3otV0oec95UaEbLtHYfjfvaNEYhpLx8ezD1WlHurkeU80Gt3nXnPdAYXh953H6RpPWWXroUmTyYQYu5L/jI7r3pJt9zbiaAvEozfEo7e2xfNLmqZp2YOzW46PRvel7mY552RZH4iIKq9hWq27eS59vG4Qj94Qj94Qj94Qj94Qz6tfq/xhx+lXHioTjzgDAAAA6AaLuQEAAAAUkCgBAAAAKCBRAgAAAFBAogQAAACggEQJAAAAQAGJEgAAAIBCpX2UdMPYRdNFAAAAAA01so+Sjs5pTyZs+KU3xKM3xKM3xKO3c4ynLEy9AQAAACggUQIAAABQQKIEAAAAoIBECQAAAEABiRIAAACAAhIlAAAAAAUkSgAAAAAKSJQAAAAAFColSkHgE2MXxNgFed50433HuZHvi58g8HOf4ZzL9xznpkpxKomi+UZZsz9Fy+Z508ZjKWo4vMvF6Lq3xDlvuliF2fY1MXZBw+Hd3s+Oxw8yTtN8t3EeNiFJkly5GLug8fiBkiRRHnOs87Rurnur7Bd24ZyTab7b2lfohHO+0b+Nxw9NF6s0xKM3xNOsyiNKo9E9cf5MrjvY+n4U/SDOn+WP4/Rz7zPGiPNniqIfVYvSuCiaa93YWePxA4XhjCaTR1n/q+v8EKEAACAASURBVJP3c9NFK8TzphTHi0Kfdd3b3AU7SRIaDu8abyvHudlIJDxvqk2yU5bnTSkMZ6WO3Zco6sJ1bylJEgrD78T5M3neEwWBXyhp1xHi0RviaRam3n6yrG4uoRONJwwG2xNBYTx+aNUFLgxnZNs9mbgyxqjf7xPnXPtRJc45TadfCn02iubyou15T7lk3fOmjcUahjOZ6IlyTSaPREQUxwtlElj1PK0T55xc97Z0AhqGs9IJ1ilxzimOF9Tv98k0O0REZNs9su0eRdG84dIdDvHoDfE0D4mSghh1IFqNmllWV/nZ4fDu55Qbkw2vM5EMXV1d5V4XMep+sRoO7wqPOsRxTEREhmGQbfeIaNWeQlOxLharRMg0O7Jc2dFWUe59DjlP6+Y4nykMZ2RZXTIM46Bjs3HoTnTmpmnmXu90OvIi0CaIR2+Ip3m1J0qW9UHOQdr2tfajFYKYAjDNjnJaMWs0uqco+rHR+DoSJyJjLPe6uLi9vLycvExFBYFPUTQny+oeJSlYLpdHKNXhxJR1GH6Xr2XvpoqeR4eep3Vi7Iomk0cKgm8HJ0oijmwSqytxzjCWv9EQMbeljxMQj94QT/NqTZSC4FtuimA0GrViDQznXC4kHY1Gez8/mTw2fpE6Jh1PVKLXxc9EJKep9hHJYJIksk11XCTMOZcjKpbVLTQyeeh5Wrcg+LaxBrGIKJr/fDCEncX3SNfvT1mIR2+Ip34nnXpbDcm/0bIisnz/KxGtpkWanMqAPDHlNhrdb4yGqdh2TyYd4im/phdxrxOL6DnnZBhG4STwHM7T7JRbdq0VAIAuTr5GifPlwcPypybu0nu9XsMlaUbRJOSUxELfMlNM6yMdrjuQMTZ9LsbxIpckBcG3wvV/DufpdPrl5yLwQSvW9xWh4/enCsSjN8RTv9oSJc+bkm1fy/UwSZKQ49yUWuh5SnG8kAuFxSLbcyMuSOsjeyLuN2/enLxM+8znq/U7cbyQa97Emh6xn5dqpFKM0ogp4MHgD/nZJmMVT4mt9hJjFATfCicL53Kevj6ROJXtKgyHd2RZH5oqmpJ4CILz/Po20R46dvS7IB69IZ7m1ZYoue6Aer2e3HjONN+RYRjaD69HUUREq4urjg12DIwxYoxtLGRWPY3QZtnESuxZlF2jVGZNzbGIJGk1kvTvg0ZU/hfOU12Jac71JxMXiwUZhtG6kTHEozfE07xap95cd5DbcFL3JInodUW+as1Hm3be3sW2exQEvkwaOOfk+762612yI0LiR5TTcfrE+TMxxjbaxzQ78osndsEWa5RGo/vGRjeDwM+NtmafDs3uSq063/adp7paj2d9Q1rOn+VnJ5NHLTeiFduATKdfZBuKqeEmE++yEI/eEE/zKidK4uJz6D9VIIh/wkSXIXYxJaPz9OAxDAZ/kOP05QJny/rQihG/MjzvKTc9tUqonhp9wkpMJZb1v3Ke6moymRBjV/Kf0XHdW7LtXiu2N9gG8egN8TTrlzRN06YLcSyrdSrP+z/YEohHb4hHb4hHb4hHb4jnFXbmBgAAAFBAogQAAACggEQJAAAAQAGJEgAAAIACEiUAAAAABSRKAAAAAApIlAAAAAAUzm4fJQAAAIB1ZfdR+vXI5WgcNsjSF+LRG+LRG+LRG+LRW5WBFEy9AQAAACggUQIAAABQQKIEAAAAoIBECQAAAEABiRIAAACAAhIlAAAAAAUkSgAAAAAKSJTWeN6UGLuQP8PhHXHO9x4XhjOyrA/yOM+bnqC0+3HOyXVvZbls+5qiaL73uLL1ULdsLNmfIPB3HqdjPEmS0Hj8IMtkmu/2xiGUbdc6rcfD2AWNxw+UJMlBxx1SD3XLnjem+Y7CcLb3GB3bZh/OOTnOzUbbtRXi0Vvr4kkr8P2v6eXl2/Ty8m06nX5Rfm46/ZJ+/Pi7/OxyuZTvLZdL+frNzecqxUkvL99WOv7h4V+yLNmfjx9/33ncbPafrcc9PPyrUnmqxrNcLtP373/bWrZsG6wrWw/7VI0nTVNlPL7/VXmMrvHc3HzeWq5d36U0Ld+u+1SN5/r609YyXV9/2nlc2XrYp67+YDb7j/KYl5eXXF9X9LgijvH9Ubm+/pReX39KF4u/0jRd9Wnv3/+W3t39WdvfRDzFIZ7qqsRTeURpNLonzp/JdQdb3x8O72g2m9Fk8kicPxPnz8QYk+8zxojzZ4qiH1WLUpm4i7XtHnH+TJ73RESr7HfXnaTv54+bTB6JaHU3uu9uuk7ibt40OxTHf1Mc/02GYfws81flcWXroW6cc0qShAzDkOeS+HGcvvI4HeOJorkcZRDfIdvuEdHutiEq3651CsMZxfGCiIg87yn3PYjjhXxvXZV6qFOSJPK8EeUS59h0qh4tDgKfOOdkGAZF0Q+K47/JNDtERNreMXPOKY4X1O/3ZVltu0e23dN+JGwbxKO3NsZT69RbEPiUJAmF4XeyrG6df6qyJElkUtPvrzpE0WGL91XHicYVxzlOX164mmr4Vb2vkoBer0eGYZBhGBTHfxPnzzQa3SuPK1MPpyAutoecSzrHI8ohLsBiKpCxK+UxZdu1bovFqm1Ms7MRFxFRHMfKY8vUQ92iaC7PDXETKM6fOF4oz5v5fPV9t6wuMcbIMAwaDFbHiwuEbkQfZZpm7vVOp6NtmXdBPHprYzy1Jkrz+byxjvtQhmHI7FaMEGVHG1QXZ86X8r+zHbv476bWwWTLRURkmu8KrZcoWw+nIC7GcbyQ89qOc9PKeCyrK0deomhOjF1QHC/IsrpyJGabsu1aNzHqEobf5WvZ8qx3ikLZeqjba7KWHf1+/X6X7cx3JYxNWS5X59R6Yipu9ppey3coxKO3NsZTa6IkOkrbvibGLsiyPmiZLQqe90Sm2aEwnBFjF+S6t/L1bIeZpbqzFI3elGy5sgtq43hBjnOzsx3K1MMpiItM9osURfPWxiNk22rXNNX6Zw9t11PinNNweEdEq2RIJKu7HFIPTSjynRYJYRTNZflFgt5WOl64qkA8etMxntoSJTHl4bq31O/3ifNnCoJ/75zfbxrny407dqLXkYy2EmtZouiHTAx2tYPu9SDWu2XjGY/Hys/rHo/j9OW0mfjOFOksDm3XU1k90fJZrtUpOjJUth500u//gwzDoCRJ5A2irusuAKCY2rcH6PV6cu0BY4w6nY6WHYfomJMkyU0jGIZBnjdVPq6sustscu3LOrGWhTEm14Oo7tbL1sMpBMG33KJaxhj1+/8gonbGsy77QESRReaHtOuprEa2XpOkIPh28KjdofVwKkW+04wxCoJvuSnd7PKDpkeay9Bh1PWYEI/edIyntkTJMIzcotn193STXbwpLsSm2ZFTBqrRh+w8a5K8yP8WIxhNNXqRqY5tytZD3ZIkkeuSDkkGdI1H7M9j29cHHVe2XU9B7B/EOZcJw77ylq2HuonvbTY5yo5K7orLNDsyqd/2lK9urq7Eesr8qKuIXccy74J49NbGeGodURqN7sn3v8rh8yDwaT6fa9nZZxtHjDJk10mokrvsYmEx7RGGM9noTS0WNgxD/m3RBtknplRtULYe6iae7iJ6nWbjnMtHyFVJua7xCHG8kG2S3aTUsqytny/brqcgkqTVSNK/DyrLofVQN1HHSZLI8sxmr3WsOm+yG1Sur1HKJug6EbGuLzRfLBa5/q0tEI/eWhlPlQ2cfP/r3k3hfP+r3Bzv48ff5QZT65bLZeMbTqo2zHv//je5kd90+mVjc0xdN5xcLP4qFU+RemgiHlHWc4hn18aE2fNmWzxF2vXU8WQ3n921Ieh6PEXr4dTxpGmxDSfX49m1Gaiq7ztVPLtcX3/K9c9iA8CqbbAL4ikO8VTX6IaT+zhOX+7xEkU/9MwWfwrD7xvbGdh2b+86C9vubTxB5bqDxrdGWD3pld/DyrK6e+MpWw91c93BRj3bdo/C8L+ti8cwDArD/+b2GhILn/edN2XbtU5i/6BDVamHuo1G9xvriyaTR+XoJdFqBFM8ZSlYVpfC8LvWfd9kMiHGruQCdNe9JdvuNd4GZSEevbUtnl/SNE3LHhwEvnwEeDS6V+7OvQvnnCzrAxG9dvZlMXZBnD+XPl43iEdviEdviEdviEdviOfVr1X+sOP0d/7TEUWIf8IEAAAAQDe1T70BAAAAtBUSJQAAAAAFJEoAAAAACkiUAAAAABSQKAEAAAAoIFECAAAAUKi0j5JuGLtouggAAACgoUb2UdLROe3JhA2/9IZ49IZ49IZ49HaO8ZSFqTcAAAAABSRKAAAAAApIlAAAAAAUkCgBAAAAKCBRAgAAAFBAogQAAACggEQJAAAAQAGJEgAAAIACEiUiiuMFMXah3JDK86byfdN8R2E4O+j3u+4tMXZBnjc9RnEPMh4/5MoeBP5Bx4vYHeemphIehnMu65OxC7Lta4qi+d7jqtZD3TjnZJrviLGLnWWLormMY9tPk+2UJEnlei5aD6ewHg9jFzQeP1CSJHuPrdpn1K1sn9RkX3YIzjk5zs1G27UV4mlWpUQpCHwZ5PoXJ9tR7OrIOeeNdvLiwqsyHj/kGjBJEnLd28Idn+dNG+skXfc21y5JktBweFf4hIyiuVYnL+ecbPtTrj7jeEGOc0Occ+VxVevhFIpegHWmqudDLqo61YPj3Gzt1/b1U1X7jLqV7ZOa7MsO5bq3lCQJheF34vyZPO+JgsCn4fCu6aKVgniaVXlEaTS6J86fyXUHyvfETxh+J8Mwcp9hjBHnzxRFP6oW5WBB4JNtf1JeZJMkkXe1IhbH6RMR0XS6u/MXCVhTF+MomstOzfOecmX3vOnOxIJo1dnrMookiIuoaXYojv+mOP5bnk++/3XrMVXr4RTCcFb4AmRZ3dx3SnQywmCw+T08hSiay5E98V2x7R4Rqdtm3SH1ULcwnFEcL4jo9byZTB6JaJWci/fWVekz6la2T2q6LzsU55zieEH9fp9Ms0NERLbdI9vuFRp91g3iaV5tU2+uO9hInmazGXU6nbr+5EFE9pokiezQ10XRXN7dilj6/VWnF8eLnXe+jvOZwnBGltXdSA5PIY5jIiIyDEPGNxrdy/d3XZDEKABjTJ7ITVvdfazK3Ov1yDAMMgyD4vhv4vw5F1tWlXo4BTHqcozjR6N7sqzusYp2MFG/IjEQSShjV3uPrVoPx7ZYrBIh0+xsxEX0el6tq9Jn1K1sn9R0X3YocbE1TTP3eqfTkRfpNkE8zTvpGqUoipRJSRMcp09R9IO63e0Xl9eOnsnXsp3+rgZl7Iomk0cKgm9adi7L5XLn+6PRPUXRj42TuSmc58sr1rEUXaOksq8e6iZGyVSJXtHjTbOzdVT3VCyrK0dexDqqOF6QZXXlSMwuVevh2MRoUBh+l69lzzPV96JKn1G3sn2S7n3ZOvGdXk/QRdl1GEU+BOJp3skSJdFBZDuQJjlOnyaTx4PLU7SjCIJvuTvQUxNxZacCii6OnUweG73obpO9E8+uYxFrlFQXoCr1ULcomv9c58dK1TfnPDPNMzp28UrLttWuaSqhaj2cAudcjnhZVvegkVZdkouyfVLTfdmx6XghrgLx1O9kiVIURdTr6TOadO5suyc78+HwTvunCg4h1ihF0Q+ZCKnWf+haD9mppuwao0OItT+m2Wl0ym2d4/TldKhYyLxrHWDVeqjb6gmdz8Q5J8MwCo2QAcD5OFmiNJvNyLKsU/252ujyRE4R63eCrjuQiYUud7lliDVKjDE5lbtr1ELHephOv/xcJDsovQ5MjCbpegOSHR1SrQU7Rj3UaTVi+ZokBcG3g0eh29Rn/C/QZVbjWBBP/U6SKImOQseOcJfstI2QXSujezzi7lc8HTUY/CHv7N+8edNw6Q5Tpa51rIfXJ/Fet9EQhsM7sqwPO4/PLgzWYd2fiMO2rw86rmo91Ek87bXawoRREHzbex62vc84B1dXq7Uv6+saRZvoeCHeBfE07ySJUhD4rRxNEtMZSZLI/VRms1XHbpodrUdlsptoirJn1+a0bc2BYRiyPXz/K3HOc0/CqS5A51YPQhRFRERyZE0XcbzIJT9CG7//IklajST9u1CS0+Y+41yINlh/MnGxWJBhGK1LVhFP806SKC0WC22nB3YxDENOH4gdekXnl92vRuwwqtNutabZkSecKLtYmzMa3csOW7edt3cRC5Y552RZH8g038kLmXhaaj2eovVwalH0Y2M/JGEyeZT7iqnaRzw5osvaJMfpy4RN7N4s6jk7rbYeT9F6OLUg8OV0bpIkZFkfchvnimR7PZ6ifYaudOzLDiW2NZlOv8g2FHt0tfHGCPE07ySJkuc9aZklFjEa3eceWRbTODpMd+zjeU+5cjLGyPOetH2yaB/T7FAYfs8lB5bV3btu5Nzqgej1yRBdRigMw6Aw/G+uoxPfFV0e+T/EfF5+y4k29xnnYjKZEGNXZNvXxNgFue4t2XavleciEeJp2i9pmqZlD85uOT4a3Zd+xFmsQxAXvbIYu8jdkbYd4tEb4tEb4tEb4tEb4nn1a5U/7Dj9ykNl4p8wAQAAANDNSXfmBgAAAGgTJEoAAAAACkiUAAAAABSQKAEAAAAoIFECAAAAUECiBAAAAKBQaR8l3WT/nSgAAAAAoZF9lHR0TnsyYcMvvSEevSEevSEevZ1jPGVh6g0AAABAAYkSAAAAgAISJQAAAAAFJEoAAAAACkiUAAAAABSQKAEAAAAoIFECAAAAUECiRERxvCDGLpT7LHjeVL5vmu8oDGeFfq9pvpPHZX8458cs/k7j8UOu7EHgFzrO86a58o/HDzWXtJhsWzB2QcPh3UH1KY53nJsaS1lc2XOkbLueCudcxlakbFE0J9u+ljG57u1JvyfbJElSqp7XjxPfnyRJTlDq45erbD2ckuveEmMX5HnTvZ/VtX124ZyT49xslLmt2hZPpUQpCHwZ5LYTlHOe6/y2VQTnXL7fxMWLc06ue6t8fzx+yJU7SRJy3du9yVIcLxr/4rnuba5dkiSh4fBu7wnpedONjsPzpo0nF+ttQbQ6Bx3nc6Hjo2iu1Zex7DlStl1P6ZALTxjOyHFuKI4Xa68Va9e6qOp538XYcW42PqPD96dsucrWw6l43rTwzSuRvu2zi+veUpIkFIbfifNn8rwnCgKfhsO7potWStviqTyiNBrdE+fP5LqDjfdc95Ymkwlx/ix3+Fy/E2GMEefPFEU/qhblYEHgk21/Ut65JkkiyyvidJw+ERFNp7s7iTiOiYjItnsyfvHDGDtiFNtF0Vx2Hp73lCu75013xjydfsmVfTS6l7+zybt80RaiXJ73RESrZHdfRzkeP2jXEZY5R8q26ymF4eygC5foHB2nT5w/UxB8I6Ji7VqXKJpTFM2J6PW7b9s9IiLy/a/K48JwJhM+0T6TySMRrRLjbDJ4SmXLVbYeTkHc5B5yg6Br++zCOac4XlC/3yfT7BDRqs+w7Z5smzZpYzy1Tb1xzskwDFkRRET9/j9oNmum41snstckSeQXf10UzeVdsUgE+/3VRWnfaMByuSQiok6no/xMncRF2DAMGZ9IeIhIeQFKkkReeHu91XEvLy/ydxmGUVuZd0mSRNa3aINsu+1qC3H3yxjLnY9NK3OOlG3XUxGjDUWF4WyjXS2rKxNG1XfzFMTfFt8HkYQydqU8ZrFYXWhNs7NxPNFr+51alXKVqYdTcJzPFIYzsqxu4X5J1/bZRSQPpmnmXu90OjLpaJM2xlNbomQYBnG+zN3hRtH8JKMpRTlOn6LoB3W73a3vv3YIr2XOdg67GjSKIiJa3XWVXU9TJ3GRXscYk3eOltWV06qMMfK8p8YSpWzS7furkaVsUmBZ29tQGI3uKYp+bHw5m1THOaJq11MRU27Z5G2XfP8QabNGybK6csRh1W9dUBwvyLK6cgRiG/HdCcPv8rXsXXJT51/ZcpWth1Ng7Iomk0cKgm+F+yVd22cX8Z1eT0xFzLpcU4pqYzy1JkqTySO57i1Z1geyrA80m80Kd6B1c5w+TSaPByduRb+QIonKNrpYT3OKtUsiruz04aELMLPlXGX6zd5ted4TmWaHwnAmL6bi9V3tuDoPN6eGm1bmHDlGu9YliuY/1y2yUvWdnUIRa5SaXudHlP8eHDo9wzmXI2yW1dVmRLNMuarUQx2C4FtuNKgMXdvnEDomFlXoGE+tT73FcUyj0Yii6AdF0Y+f65GaveM9hSRJyDAMYoxREHzbWHdxigubbffkl344vCv1VIFYPybKPh4/NHpRXo1Qbp4/Yji9TcqeI8do1zpkp9zE2rFDbVujpEMSKMo1Gt3LhzmKdOarJ3s+y2UITY/ACGXLVbYedKVr+4B+akuU4nhB8/k8NyUyGPyxdxG07orc4RqGQXH8N0XRDxm/ZXXlnPipLuzrd1yuO5AjEodMoVlWV8Yxnzez2E50zGJaRwyfG4ZBnjfV4oJ6iCrnyLHa9Zim0y8/F9cOSt+VZ9coid/R9FRiVnaUrMhTr9mLcBB802LZwTHKdUg96ErX9imjreVW0TGe2hKlJEk2Al6NULTnDiQ7zSFkRzRUFwQxNWSa7+ot4B7iLkksjB0M/pD1/+bNm63HiHUIjF1oMe0hZBfWiyTBNDuyDdo2qlTlHCnTrnV7fRLvdZ8rYTi8I8v6sPU4HTtFotc4bPv64GPF01irrU9WI4Y6TOmUKVeVetCVru2jcnW1WsuzPpou+kNdv0MqbYyntkRp9eRKfgW7503lk1RtIO70kySR+26Ip/ZMs6O8e88mWOK47GPdqsXjx5TdRFOUITvqUmRuX2wTkH1EuKmn+LJfHhFHdp1EUyMpZZU9R47RrjrJPrE0Ho+JKN+uolNtShwvckmgYFmW8hhxEV6NVPxbm4twlXKVqQdd6do+KuI6tL5GdLFYbDxZ3gatjCetwPe/ptPpF+X7y+Uyvb7+lF5evk0vL9+mDw//2vnZm5vPVYqTXl6+LXWc73+VZVz38PAv+V72Zzb7j/zMzc3n9PLyba4ubm//ufW46+tPtccjZOs++5Mt53T6Jb28fJur+2OUfZu64nn//rd0uVwq48m6u/tz5/uHqBpPkXreFk+Rdi2jajzbft/l5dvU97/K17bFI17T6Xx7eXlJP378fWu5sv3YejzZvmTbT7YuThlP0XKt92VF6+HU8awTZVz/DqzHo2v77HN9/Sn9+PH3dLH4K03TNJ3N/pO+f/9b5TbYBfG8qnUxN2NM7ryZ3biwTUaj+1y5xbTHvv1dPO9pI17XHeQeS62b5z3lyike8d/3RJIOZd8mDL9vlMu2e61dX1C2nsu2q65cd7Dx5KLj9OWi7iYYhkFh+N/cCJ347u/qx5paw7dP2XKVrQdd6do++0wmE2LsSv5LF657S7bda2UbELUvnl/SNE3LHpzdcnw0ui/VUXPO5foFy+pW6hxX/0bWc+njdYN49IZ49IZ49IZ49IZ4Xv1a5Q87Tr/ymgjxCDoAAACAbmqdegMAAABoMyRKAAAAAApIlAAAAAAUkCgBAAAAKCBRAgAAAFBAogQAAACgUGkfJd1k/30pAAAAAKGRfZR0dE57MmHDL70hHr0hHr0hHr2dYzxlYeoNAAAAQAGJEgAAAIACEiUAAAAABSRKAAAAAApIlAAAAAAUkCgBAAAAKCBRAgAAAFBAogQAAACggETpJ9e9JcYuyPOmW9+P4wUxdlF406ownJFtX8tjXPeWOOfHLLLSrrImSULD4Z1837avKY4XB/3+fXVVp/H4QZbdNN9REPgHHe95U2LsghznpqYSHiaK5qXOk6r1UBfTfCfLlf3ZF1PZejgVzrmMbV9dJ0mSax/GLmg8fqAkSU5U2mLlKnrelD2ubseo5yb7sm1E/yTqOQxne4/hnMs4RJ8eRfMTlLY8zjk5zs1G2+mqUqIUBL4MctuJliRJrjK2VQTnXL7f1MXL86Y7T0hxIhYVRXNy3dtcArJKnD7V3lnuK6vj3OQ6uThekOPcFL4o7aurOrnube48E0lf0S9YFM21+jKG4Ywc52bjPHGczzuPq1oPdYnjRanzu2w9nNIhF2DHudnoDz1v2nhyrjpv9iUJZY+rW9V6brIv22Y8fsh9h5MkIde93VnG1TX2c+4zok/XKbZ1rntLSZJQGH4nzp/J854oCHwaDu+aLtpWlUeURqN74vyZXHew8Z5odM6fifNnurq62rgTYYwR588URT+qFuVgIqnYdYEJAp9s+9NBd7fT6erLyxijOP6bwvA7EdHPE6O+k3dfWaNoLi9GYfid4vhvMs0OJUlCvv915+8uUld1iqK5rDvPeyLOn8lx+j//f7q3fcbjh8YvVOtEp+A4feL8mYLgGxGt6lp1nlSthzrFcUxERLbdk9958cMYUx5Xph5OKQxnhcsRhjP5HRPtM5k8EtHqAnbo6O2xRNFcjjKIPtu2e0REO7/7ZY+rW5V6brov2yZJEnltFPUsvtfierJNEPjEOSfDMCiKfsg+nYi0ii+Lc05xvKB+vy/Lats9su2etiNhtU69RdFcdnpEq45wuVzW+ScPIjJxy+qSYRgb74sMN0kS2TkU0e2ufp9t98gwjNzFa9cFo4oiZZ3PVyehZXXJNDtkGAb1eqvPRlG08/fvq6u6iYuwqFeiVYci7LqQibtfxpj8YjYtDGdyhKLfX3WIltWViYWqDavUQ93Ed7vTKV7HZevhVMToSVGLxeoCbZodWXZxwSN6bb8mrJdH9EuMXdVyXJ2q1HPTfdk2UTSX3wMx6CC+D7tGarN9OmOMDMOgwWB1vEhIdCOSIdM0c693Oh1ty3zyNUrZjrFpjF3RZPJIQfBN+YVxnD5F0Q/qdruFf6/rDiiO/6bR6J5s+1pOhU0mj2RZxX/PofaV9bWDe03WxH/vOzmL1FWT9iXgo9E9RdGPjS9nU7LJcxRFR1ub0+SNiEi2ff+rjGc4vNsZT131cCxiyi2bjO4iXnEe8gAABS1JREFURgPEKDIR5e6Smzr/LKsrR16iaE6MXVAcL8iyunIk5pjH1a1KPevYl23vm18T0bLJQ5OJuYroo9YTbdEWOnzv19WaKFlWV053iMVbq//WY1QpCL7l7kLWOU6fJpPHSqNASfIi/3s+n9eWJBYp67a/XbSj2FdXdRNxZYeoiy4onUwet04N6yI7RC7W5qjOkyr1UDfRmWc7uiDwd8aTdUg9nMJqRNwnxljp84dzLkekxEhu07J1esh0YNnjTuGQem66LyuqSN8sEsLssgrf16M/KOt/LlESd2GMXZBlfaBeryfXxPyviKIfFEU/iDFGYTjTdt5Yd7bdk52feGrvXOpy29ocVfKjaz0kSUKGYRBjjILgW+F4sg6ph7plp9w876nU71jdHH6Wa0iaHIHJEvU8Gt3LBcNFLk5lj6ubrvV8Cv3+P8gwjJ9LLlZPjeq6zqfNak2UDMOQnaZYnBZFcy3uqk6JMUb9/j+IqNk1JNvuUNqUtK7fCbruQI6w6DKEXkZ2bY74buyaQtOxHgzDoDj+m6Loh5xetqyuXD8i1pTscmg91Gk6/fJz0e+gVH+1evLo9eIdBN9qW59YVnaU7JB+qexxdWhDPZdVpG8WNybZJR3ZaeI29os6tt9J1yiF4UwuIj5XnHOyrA/E2EXjnci67LSNIO4I25C8irtFkXgPBn/I8r9586bh0h2mSmegYz2E4Uzu/XIIHTtFIso8Wfi6r40wHN6RZX1QHiueqlptfbK6kDX9/RJx2Pb1SY47BR3ruaztffPrTcKuuEyzkxuQ2LYGVSdXV6u1SetLcETsOpb5ZIlSEPg0nU7PflhUDIMSvT7WyTmXj9LWuZh7H/E0UvbR2tls9rNcVmPlKiK7iabYOyU7LdOGNQdZ2SduxuMxEeXXfYjOZJ2u9ZDt6EW5slsZqB4wKFsPOhMX79UIx7+1unjH8SKXBAr7vv9lj6uTzvV8KHFdyH5/RN+8a3Ahm8ivr1EyzY6WdSJiXV9ovlgsyDAMLctca6KU3S10sVic1bCoIJ4EEXecq8cz/yCi14uaZX2QX+iiT8/UIbu+Rcxnx/Hq5BRTg0QkNwltekO5rOyXXuzGK9bmjEb3siPRbedtlex5Is4hcddumh05vbEeT9F6OLXsY9qiXNkyZ/d6yr5XtB5OLYp+bOwFJUwmj3Lft/V4gsCXF6wkSeTosvhpas2V4/Rl3yv6ZXHeZKcX17/7RY87taL1rGNfto1hGPJcF98fUWbxuD/RZjxiC5rVf+fXKE0mk1OGUJjYpmU6/ZLZ12/28wEOPW94a02UxGOlYjOwc55yy3LdAXneUy4ptO0eheF/G08U19e3mGZno6y68ryn3L46jDHyvCetn2jbZdt54jj93N5j2+haD573tHEj4LqD3CPc25StBx2JfW10YxgGheF/c999MYW76+at7HF107WeqxiN7jfWF00mjzv3EhPf/WzCalldCsPvWo7MCJPJhBi7ksmd696SbfcaPad2+SVN07Tswdktx0ej+1IdtVjTQ7Rq4Cqd4+rflHre/8GWQDx6Qzx6Qzx6Qzx6Qzyvfq3yhx2nX3moTPwTJgAAAAC6OfnO3AAAAABtgUQJAAAAQAGJEgAAAIACEiUAAAAABSRKAAAAAApIlAAAAAAUKu2jpJvsv8cEAAAAIJTdiuisEiUAAACAY8LUGwAAAIACEiUAAAAABSRKAAAAAApIlAAAAAAUkCgBAAAAKCBRAgAAAFBAogQAAACggEQJAAAAQOH/A9FR1tKJcb5OAAAAAElFTkSuQmCC\"/></strong></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the distance between Kiko and Longlines.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm in pseudocode that checks the elements of the array <code>ROUTE_X_DISTANCES</code> and outputs whether the array is valid or not.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm in pseudocode that inputs the names of two bus stations and outputs the distance between them. If any of the inputted names are not found, the method should output an appropriate message.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The array <code>ROUTE_X_TIMES</code> (<strong>Figure 3</strong>) stores the approximate number of minutes it takes for a bus to travel to a bus station from the previous one. For example, <code>ROUTE_X_TIMES [6]</code> stores the number of minutes it takes for a bus to travel from Kingsley to Allapay: 7 minutes.</p>\n<p style=\"text-align:center;\"><strong>Figure 3: The array <code>ROUTE_X_TIMES</code></strong></p>\n<p style=\"text-align:center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align:left;\">Explain how this data could be used to determine the number of minutes it takes for a bus to travel between any two bus stations.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>5.9;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for correct outer/row loop</em><br/><em>Award <strong>[1]</strong> for correct inner/column loop</em><br/><em>Award <strong>[1]</strong> for use of a flag</em><br/><em>Award <strong>[1]</strong> for checking whether all elements on and above the main diagonal are zero</em><br/><em>Award <strong>[1]</strong> for checking all elements below the main diagonal (they all should be positive numbers)</em><br/><em>Award <strong>[1]</strong> for outputting the appropriate message</em></p>\n<p><span style=\"text-decoration:underline;\">Example 1</span>:</p>\n<pre>VALID=True<br/>loop R from 0 to 9<br/>   loop C from 0 to 9<br/>        if R&gt;C and ROUTE_X_DISTANCES[R][C]&lt;=0<br/>             then VALID=False<br/>         end if<br/>         if R&lt;=C and ROUTE_X_DISTANCES[R][C]!=0<br/>             then VALID=False<br/>         end if<br/>     end loop<br/>end loop<br/>if VALID<br/>  then output('VALID')<br/>  else output('INVALID')<br/>end if</pre>\n<p><span style=\"text-decoration:underline;\">Example 2</span>:</p>\n<p><code>FLAG=1</code><br/><code>loop R from 1 to 9</code><br/><code>     loop C from 0 to R-1</code><br/><code>         if ROUTE_X_DISTANCES[R][C]&lt;=0</code><br/><code>             then FLAG=0</code><br/><code>         end if</code><br/><code>     end loop</code><br/><code>end loop</code><br/><code></code></p>\n<p><code>loop R from 0 to 9</code><br/><code>     loop C= from R to 9</code><br/><code>         if ROUTE_X_DISTANCES[R][C]!=0</code><br/><code>             then FLAG=0</code><br/><code>         end if</code><br/><code>     end loop</code><br/><code>end loop</code><br/><code>if FLAG ==1</code><br/><code>  then output('IT IS VALID')</code><br/><code>  else output('IT IS NOT VALID')</code><br/><code>end if</code></p>\n<p><span style=\"text-decoration:underline;\">Example 3</span>:</p>\n<p><em><strong>Note</strong>: Marks should also be awarded if a candidate wrote the algorithm in Java/Python/Javascript.</em></p>\n<p><em>Award [1] for correct outer/row loop</em><br/><em>Award [1] for correct inner/column loop</em><br/><em>Award <strong>[1]</strong> for stopping as soon as an incorrect value is found</em><br/><em>Award <strong>[1]</strong> for checking whether elements on and above the main diagonal are zero</em><br/><em>Award <strong>[1]</strong> for checking elements below the main diagonal (they all should be positive numbers)</em><br/><em>Award<strong> [1]</strong> for outputting the appropriate message</em></p>\n<pre>function check()<br/><br/>{ for (var i=0; i&lt;10; i++)<br/>  { for (var j=0; j&lt;10; j++)<br/>    { if (i&gt;j)<br/>      { if (ROUTE_X_DISTANCES[i][j] &lt;= 0.0) return \"invalid\";<br/>      }<br/>      else if (ROUTE_X_DISTANCES[i][j] != 0.0 ) return \"invalid\";<br/>    }<br/>  }<br/>  return \"valid\";<br/>}<br/><br/>output(\"ROUTE_X_DISTANCES is \"+check());</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for all variables correctly declared and initialized;</em><br/><em>Award <strong>[1]</strong> for looping through the array ROUTE_X_NAMES;</em><br/><em>Award <strong>[1]</strong> for determining positions of the first name in the array;</em><br/><em>Award <strong>[1]</strong> for determining positions of the second name in the array;</em><br/><em>Award <strong>[1]</strong> for outputting a message if one or other not present;</em><br/><em>Award <strong>[1]</strong> for a comparison of positions to find largest;</em><br/><em>Award <strong>[1]</strong> for the correct output of distance from ROUTE_X_DISTANCES;</em></p>\n<p><span style=\"text-decoration:underline;\">Example 1</span>:</p>\n<pre>NAME1=input()<br/>NAME2=input()<br/>POS1=-1<br/>POS2=-1<br/>K=0<br/>loop while K&lt;=9 and (POS1==-1 or POS2==-1)<br/>      if ROUTE_X_NAMES [K].equals(NAME1) //accept '==' instead of equals()<br/>        then POS1=K<br/>      end if<br/>      if ROUTE_X_NAMES [K].equals(NAME2)<br/>         then POS2=K<br/>      end if<br/>      K=K+1<br/>end while<br/>if POS1==-1 OR POS2==-1<br/>   then output('stations are not found')<br/>   else<br/>      if POS1 &gt; POS2<br/>         then output(ROUTE_X_DISTANCES [POS1][POS2])<br/>         else output(ROUTE_X_DISTANCES [POS2][POS1])<br/>      end if<br/>end if</pre>\n<p><span style=\"text-decoration:underline;\">Example 2</span>:</p>\n<pre>ST1=input()<br/>ST2=input()<br/>PS1=-1<br/>PS2=-1<br/>loop K from 0 to 9<br/>      if ROUTE_X_NAMES [K]==ST1<br/>        then PS1=K<br/>      end if<br/>      if ROUTE_X_NAMES [K]==ST2<br/>         then PS2=K<br/>      end if<br/>end loop<br/>if PS1!=-1 AND PS2!=-1<br/>  then   if PS1 &lt; PS2<br/>         then T=PS1<br/>              PS1=PS2<br/>              PS2=T<br/>         end if<br/>         output(ROUTE_X_DISTANCES [PS1][PS2])<br/>  else<br/>         output('stations not found')<br/>end if</pre>\n<p><span style=\"text-decoration:underline;\">Example 3</span>:</p>\n<p><em><strong>Note</strong>: Award marks if algorithm is presented in a Java/Python/Javascript/any other program rather than IB pseudocode.</em><br/><em>For example, please see the following Javascript program</em></p>\n<pre>function findStation(station)<br/>{ var found = false;<br/>  var i = 0;<br/>  do<br/>  { found = (ROUTE_X_NAMES[i] == station);<br/>    if (!found) i = i + 1;<br/>  } while (!found &amp;&amp; i &lt; 10);<br/>  if (found) return i;<br/>  else<br/>  { output(\"No such bus station as \"+station);<br/>    return -1;<br/>  }<br/>}<br/><br/><br/><br/><br/>var station1 = input();<br/>var station2 = input();<br/>output(\"Finding the distance between \"+station1+\" and \"+station2);<br/>var station1index = findStation(station1);<br/>var station2index = findStation(station2);<br/>if (station1index &gt;=0 &amp;&amp; station2index &gt;= 0)<br/>   { if (station1index &gt;= station2index)<br/>       output(\"Distance \"+ROUTE_X_DISTANCES[station1index][station2index]);<br/>   else<br/>       output (\"Distance = \"+ROUTE_X_DISTANCES[station2index][station1index]);<br/>}</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Determine positions/indexes/subscripts of both bus stations in array ROUTE_X_NAMES;</p>\n<p>Calculate the sum of the elements of array ROUTE_X_TIMES (calculate the number of minutes as the sum of the array elements);</p>\n<p>Between (lower +1) index and higher index;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The majority of students attempted it, and many got it right.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Algorithms were required based on the question scenario. Some candidates did not attempt these questions. Some candidates achieved high marks by demonstrating good programming skills. Many candidates scored several marks for the construction of a partial solution or a solution that was partially correct.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Algorithms were required based on the question scenario. Some candidates did not attempt these questions. Some candidates achieved high marks by demonstrating good programming skills Many candidates scored several marks for the construction of a partial solution or a solution that was partially correct.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Varied from poor to excellent.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "21N.1.HL.TZ0.13",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A transport authority is investigating how many people use a certain direct train route.</p>\n<p>At the end of each day, the total number of passengers who travelled on this route is stored in a collection, <code>PASSENGERS</code>.</p>\n<p>The first item was written to the collection on Monday 1st May 2017.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>The next items, collected on Tuesday and Wednesday, were added like this:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Data for 30 complete weeks was added to the collection.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct pseudocode that will read <code>PASSENGERS</code> into the two-dimensional array, <code>2D_ARRAY</code>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the pseudocode for the procedure <code>total</code>, that takes as input a column number of this two-dimensional array and returns the sum of the elements in that column.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The transport authority wishes to know how many passengers, on average, travel on each day of the week.</p>\n<p>Using the procedure <code>total</code> construct the pseudocode to output the day of the week with the highest average number of passengers, and the value of this average.</p>\n<p>You should make use of the sub procedure <code>convert()</code> which converts the numbers 0 to 6 into days of the week, for example <code>convert(1)</code> will return “Tuesday”.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The transport authority stores details about the ticket prices in a one-dimensional array, <code>FEES</code>, where <code>FEES[0]</code> contains the price of a ticket for Monday to Friday, while <code>FEES[1]</code> contains the price of a ticket for Saturday and Sunday.</p>\n<p>The procedure <code>salesCalculate()</code> takes as input the column and row indices that define two specific days during the 30 weeks, and outputs the total amount of money generated from ticket sales between those two days (inclusive).</p>\n<p>Construct, in pseudocode, the procedure <code>salesCalculate()</code>.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4]</strong> as follows</em>.</p>\n<p><span style=\"text-decoration:underline;\"><em>Example answer 1</em></span>:<br/>Assumes that an array of sufficient size is declared (<code>2D_ARRAY[][]</code>) and <code>PASSENGERS</code> contains valid data for all 7 days in each of 30 weeks.</p>\n<pre>PASSENGERS.resetNext()<br/>N = 0<br/>loop while PASSENGERS.hasNext()<br/>       //accept not PASSENGERS.isEMPTY()<br/>       //accept N&lt;PASSENGERS.length()<br/>  WEEK = N div 7<br/>  DAY = N mod 7<br/>  2D_ARRAY[WEEK][DAY] = PASSENGERS.getData() // PASSENGERS.getNext()<br/>  N=N+1<br/>end loop</pre>\n<p><em>Award <strong>[1]</strong> for while loop.</em><br/><em>Award <strong>[1]</strong> for correct calculation of row index(WEEK).</em><br/><em>Award <strong>[1]</strong> for correct calculation of column index(DAY).</em><br/><em>Award <strong>[1]</strong> for correct array subscripts.</em><br/><em>Award <strong>[1]</strong> for correct use of collection methods.</em></p>\n<p><em><span style=\"text-decoration:underline;\">Example answer 2</span>:<br/></em>Assumes that an array of a sufficient size is declared (<code>2D_ARRAY[][]</code>) and <code>PASSENGERS</code> contain valid data for all 7 days in each of 30 weeks.</p>\n<pre>PASSENGERS.resetNext()<br/>loop for WEEK from 0 to 29<br/>   loop for DAY from 0 to 6<br/>     2D_ARRAY[WEEK][DAY] = PASSENGERS.getNext()//PASSENGERS.getData()<br/>   end loop<br/>end loop</pre>\n<p><em>Award <strong>[1]</strong> for nested loops.</em><br/><em>Award <strong>[1]</strong> for loops that generate the right output (correctly read <code>PASSENGERS</code> into <code>2D_ARRAY</code>).</em><br/><em>Award <strong>[1]</strong> for assignment into correct array location.</em><br/><em>Award <strong>[1]</strong> for correct use of collection methods</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre>total (N)<br/>// if N (column number) is not passed as an input parameter then<br/>// assignment must appear in pseudocode, for example, N = input()<br/>  SUM = 0<br/>  loop for WEEK from 0 to 29<br/>    SUM = SUM + 2D_ARRAY[WEEK][N]<br/>  end loop<br/>  return SUM<br/>end total</pre>\n<p><em>Award <strong>[1]</strong> for assigning the input to a variable / passing the value of <code>N</code>.</em><br/><em>Award <strong>[1]</strong> for initializing and returning/outputting <code>SUM</code>.</em><br/><em>Award <strong>[1]</strong> for correct loop.</em><br/><em>Award <strong>[1]</strong> for correct addition of each term to <code>SUM</code>. (<strong>Note</strong>: The array position must be completely correct with the input column and the varied row.)</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre>MAXAV = 0<br/>MAXDAY = \"\"<br/>loop for DAY from 0 to 6<br/>    if total(DAY)/30 &gt; MAXAV<br/>      MAXAV = total(DAY)/30<br/>      MAXDAY = convert(DAY)<br/>    end if<br/>  end loop<br/>output MAXAV,MAXDAY</pre>\n<p><em>Award <strong>[1]</strong> for correct loop.</em><br/><em>Award <strong>[1]</strong> for use of method total() when calculating the average number of passengers.</em><br/><em>Award <strong>[1]</strong> for initializing, updating and outputting/returning <code>MAXAV</code> and <code>MAXDAY</code></em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre>salesCalculate(2D_ARRAY, A, B, X, Y)<br/>// A,B are row &amp; column of first day,<br/>// X,Y are row &amp; column of last day.<br/><br/>TOTAL = 0<br/><br/>loop for COL= B to 6<br/>  if COL &lt; 5 then<br/>    TOTAL = TOTAL + 2D_ARRAY[A][COL] * FEES[0]<br/>  else<br/>    TOTAL = TOTAL + 2D_ARRAY[A][COL] * FEES[1]<br/>  end if<br/>end loop<br/>         //calculates total for week A,<br/>         //days are in the range from B to 6<br/>         //weekdays are days 0-4, and weekend 5,6<br/><br/>loop for ROW = A + 1 to X - 1<br/>  loop for COL= 0 to 6<br/>    if COL &lt; 5 then<br/>      TOTAL = TOTAL + 2D_ARRAY[ROW][COL]*FEES[0]<br/>    else<br/>      TOTAL = TOTAL + 2D_ARRAY[ROW][COL]*FEES[1]<br/>    end if<br/>  end loop<br/>end loop<br/>        //calculates total for all weeks from A+1 to X-1,<br/>        //days are in the range from 0 to 6<br/><br/>loop for COL= 0 to Y<br/>  if COL &lt; 5 then<br/>    TOTAL = TOTAL + 2D_ARRAY[X][COL]*FEES[0]<br/>  else<br/>    TOTAL = TOTAL + 2D_ARRAY[X][COL]*FEES[1]<br/>  end if<br/>end loop<br/>        //calculates total for week X,<br/>        //days are in the range from 0 to Y<br/><br/>output TOTAL<br/>end salesCalculate</pre>\n<p><em>Award <strong>[7 max]</strong>.</em><br/><em>Award <strong>[1]</strong> for initializing, updating and outputting TOTAL.</em><br/><em>Award <strong>[1]</strong> for nested loops.</em><br/><em>Award <strong>[1]</strong> for correct calculation of <code>TOTAL</code> in row/week A (column subscripts/days/ in <code>2D_ARRAY</code> must be in range B to 6).</em><br/><em>Award <strong>[1]</strong> for correct calculation of <code>TOTAL</code> in row/week X (column subscripts/days/ in <code>2D_ARRAY</code> must be in range 0 to Y).</em><br/><em>Award <strong>[1]</strong> for correct calculation of <code>TOTAL</code> in rows/weeks from A + 1 to X – 1 (column subscripts/days/ in <code>2D_ARRAY</code> must be in range 0 to 6).</em><br/><em>Award <strong>[1]</strong> if evident that the number of days to be included in calculating total amount is different for weeks A + 1 to X – 1, week A and X (three different cases).</em><br/><em>Award <strong>[2]</strong>, <strong>[1]</strong> for checking for weekdays / weekend and <strong>[1]</strong> for using correct subscript in FEE (0 for weekdays, 1 for weekend).</em></p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18M.1.HL.TZ0.15",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following recursive method, where <code>N</code> is a positive integer</p>\n<pre>mystery(N)<br/>  if (N &gt; 0) AND (N mod 2 = 0) then<br/>    mystery(N−2)<br/>  end if<br/>  output N<br/>end mystery</pre>\n</div><div class=\"question\">\n<p>Determine the output produced by the method call <code>mystery(4)</code>.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award up to <strong>[3]</strong> as follows</em>:<br/><em><strong>[3]</strong> for fully correct response (sequence of output) “0;2;4”</em>;<br/><em><strong>[2]</strong> for response (sequence) “4;2;0” (all elements are correct, but they are in inverse order)</em>;<br/><em><strong>[1]</strong> for response ”0” (only base case is correct)</em>;<br/><em><strong>OR</strong></em><br/><em>“0;2” (incomplete output, but initially correct, and with correct order)</em>;<br/><em><strong>OR</strong></em><br/><em>“–2;0;2;4”,“0;2;4;6” (correct sequence immersed in some unnecessary and incorrect context)</em>;</p>\n<p><em><strong>[0]</strong> in all other cases (e.g. responses “2”, “4”, “2;0”, “2;4”, “4;2”)</em>;</p>\n<p>0<br/>2<br/>4</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "17N.1.HL.TZ0.6",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An application package used in an office includes a word processor.</p>\n</div><div class=\"specification\">\n<p>The office manager decides to buy and install new software and hardware.</p>\n</div><div class=\"specification\">\n<p>The changeover to the new system can be achieved by either direct changeover or phased conversion.</p>\n</div><div class=\"specification\">\n<p>The new software allows basic text summaries and analysis to help check text files, including functions such as calculating word frequency.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a spellchecker checks whether a word in a text file is correctly spelt or not.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> problem that may arise from the installation of new hardware and software in the office.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare direct changeover and phased conversion.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> way of testing this software.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Each word in the text file is compared with words in a <span style=\"text-decoration:underline;\">dictionary</span> (held in memory/online);<br/>If the word is found in the dictionary it is correctly spelt / if the word is not found in the dictionary, spellchecker will recognize that it is incorrectly spelt;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for stating a problem and <strong>[1]</strong> for an elaboration, up to <strong>[2 max]</strong></em>.<br/>Users/employees might be afraid of these changes (for various reasons);<br/>And not willing to help in this change;</p>\n<p>Data migration problems;<br/>For example, different file formats so conversion must be performed;</p>\n<p>Employee efficiency may drop;<br/>As they learn to use the new system;</p>\n<p>Issue of compatibility with legacy software/hardware;<br/>So features of new software/hardware may not work correctly;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for outlining what is meant by direct changeover, <strong>[1]</strong> for outlining what is meant by phased conversion, and then <strong>[1]</strong> for an advantage or disadvantage<strong> of each</strong>, up to <strong>[4 max]</strong></em>.</p>\n<p><em><strong>Example answer</strong></em><br/>In direct changeover, the old software and hardware is completely replaced, in one move, by the new software and hardware;<br/>Phased conversion involves selecting one section in the office for the direct changeover and other sections will be switched when the first section selected is running satisfactorily. Eventually the whole office has been changed;<br/>A phased conversion is less risky than a direct changeover as any problems that might arise will be isolated in only one section in the office;<br/>Direct changeover means everyone in the organization has same software/hardware and so there are no compatibility issues;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>Debugging (<em><strong>Accept</strong></em>: white-box testing – i.e. structural testing/flow testing;<br/>black-box testing / requirement testing);<br/>User acceptance testing (alpha-testing) / Beta-testing;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.1.HL.TZ0.10",
    "topics": [
      "topic-2-computer-organization",
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "2-1-computer-organization",
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Investment planning is something that many people need to be aware of. Planning for your future requires research because bad choices can be very costly.</p>\n<p>Interest paid on money invested is usually in the form of compound interest. The formula to calculate compound interest is:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>T</mi><mo>=</mo><mi>P</mi><mo> </mo><mo>*</mo><mo> </mo><mo>(</mo><mn>1</mn><mo> </mo><mo>+</mo><mo> </mo><mi>r</mi><msup><mo>)</mo><mi>n</mi></msup></math></p>\n<p><strong>T</strong> is the total value of the investment,<br/><strong>P</strong> is the principal sum invested,<br/><strong>r</strong> is the interest rate per time period converted to a decimal (for example, 5 % is 0.05),<br/><strong>n</strong> is the number of time periods.</p>\n</div><div class=\"specification\">\n<p>An additional $1000 is added to a principal amount of $30 000 at the end of each month.<br/>The monthly interest rate is 0.5 % and this rate is compounded at the end of each month.</p>\n</div><div class=\"specification\">\n<p>Each month, tax is calculated on the monthly profit at a rate of 25 % when the investment total (T) is $40 000, or below. However, when the investment total (T) is above $40 000, the tax rate is 40 %.</p>\n<p>The tax is calculated at the end of each month after interest has been added. A running total of the tax is kept and only deducted from the investment total (T) at the end of the year.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Calculate the total value of the investment after two years if the principal sum of $30 000 is invested. The yearly interest rate is 10 % and this rate is compounded at the end of each year.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline, using a diagram or otherwise, a method of calculating the total value of the investment after 12 months.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm to calculate the fund value at the end of each month. This algorithm should also calculate the total value of the investment after the tax has been deducted after 12 months.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Many investment companies offer alternative investment schemes and use modelling to set the rates of interest.</p>\n<p>Explain why the investment company would use modelling when setting the rates of interest.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>36300;<br/>if interest only award<strong> [1 mark max]</strong>, $6300;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em>Award <strong>[1]</strong> Initial investment 30000 for month 0;</em><br/><em>Award <strong>[1]</strong> Investment 1000 each month;</em><br/><em>Award <strong>[1]</strong> Interest rate of 0.005 * Principal</em><br/><em>Award <strong>[1]</strong> Calculate compound interest for 12 months;</em><br/><em>Award <strong>[1]</strong> Add interest to the investment;</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><strong><em>An array does not need to be used to obtain full marks</em></strong>.</p>\n<p><em>Award <strong>[1]</strong> Create array or variables / initialise array or variables / P[0] = 30 000;</em><br/><em>Award <strong>[1]</strong> Create interest rate and assign it to 1.005;</em><br/><em>Award <strong>[1]</strong> Calculate interest;</em><br/><em>Award <strong>[1]</strong> Add interest and 1000 to each month;</em><br/><em>Award <strong>[1]</strong> If statement to determine the correct tax rate;</em><br/><em>Award <strong>[1]</strong> Output investment - tax;</em></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Alternative solution without an array</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>A what if scenario can be employed;<br/>A mathematical model will allow you to adjust variables to see what impact that will have on the investment;<br/>You can see exactly what is happening to the money each month; <br/>If the client decides to pay in less or more money for a given month you can see what affect this will have on the profits;<br/>If the interest rate changes, you will be able to see what changes this has on profit;<br/>If the tax rate changes you can see how this affects the investment profits.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.4",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>State <strong>two</strong> compatibility issues that may arise when international businesses merge.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Language differences / lexical differences present across datasets to be merged;<br/>Data representation differences / different data structures (e.g., date format, incompatible file formats);<br/>Incompatible hardware;<br/>Incompatible operating systems / different software versions;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were generally able to state at least one compatibility issue related to the merging of international business systems. Some candidates lost out by naming two issues that were too similar, so therefore covering the same marking point.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.1",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline <strong>one</strong> reason why accurate user documentation is necessary for a system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>User efficiency;<br/>To ensure that users know how to use the system correctly;</p>\n<p>Support/Troubleshoot;<br/>To provide users help when they encounter errors;</p>\n<p>Accuracy;<br/>To ensure the correct methods are used to enable reliable output;</p>\n<p>Improved user experience;<br/>the user is aware of all available features, so they can make the most out of the system;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were able to identify a reason why accurate user documentation is necessary for a system. A few candidates mixed this up with system documentation. Some candidates also named two reasons rather than expanding on the one reason, as required by the question.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.2",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following binary tree.</p>\n<p>An inorder traversal of this binary tree will produce a list of names sorted in ascending order.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>two</strong> applications of stacks.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the use of a one-dimensional array as a static stack. Your answer should include brief outlines of the push and pop operations and the tests for empty and full stacks. </p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the result of postorder traversal.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Draw the binary tree after deleting the root node.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare the use of static and dynamic data structures.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Holding all data of a function/method call; simulation of recursion;<br/>Conversion of expressions (infix to postfix, infix to prefix, etc.)<br/>Evaluating expression;<br/>Parsing;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em><br/><em>Award <strong>[3 max]</strong> for the following</em>:<br/>An array A of N elements should be initialized (fixed, predetermined size);</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align:left;\">keep track of the top of the stack since not all of the array holds stack elements (in an integer variable, for example, named TOP);</p>\n<p style=\"text-align:left;\">the main property of a stack is that stack values/objects go on and come off of the one end of the stack (LIFO data structure);</p>\n<p style=\"text-align:left;\"><em>Award <strong>[1]</strong> for each stack method outlined</em>.</p>\n<p style=\"text-align:left;\"><em>Push</em><br/>Places a value (object) on the top of the stack;<br/>Increase TOP by one and set A[TOP]= value;</p>\n<p style=\"text-align:left;\"><em>Pop</em><br/>Returns a value from the top of the stack and removes that value from the top of the stack;<br/>Returns A[TOP] and decreases TOP by 1;</p>\n<p style=\"text-align:left;\"><em>IsEmpty</em><br/>Reports whether the stack is empty or not / returns True if the stack is empty, False otherwise;<br/>Returns True if TOP is less than 0, False otherwise;</p>\n<p style=\"text-align:left;\"><em>IsFull</em><br/>Reports whether the stack is full or not/ returns True is stack is full, False otherwise;<br/>Returns True if TOP is greater than N-1 (where N is size of the array), False otherwise;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]  </strong></em><em><strong><br/>The names must be in the following order:<br/></strong></em>Elm, Elder, Holly, Rowan, Larch, Hazel;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]  </strong></em><em><strong><br/></strong>Award <strong>[1]</strong> for the correct root.<br/>Award <strong>[1]</strong> for the correct left subtree.<br/>Award <strong>[1]</strong> for the correct right subtree.<strong><br/></strong></em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em><br/>Static data structures are fixed sized (for example, arrays) whilst dynamic data structure (for example, trees, linked lists) have flexible size;</p>\n<p>The size of static data structures is predetermined; the amount of memory once allocated to them at compile time cannot change on run time whereas dynamic data structures they can grow or shrink as needed to contain the data to be stored;</p>\n<p>Slower access to elements of dynamic data structure (sequential access) when compared with (direct) access to elements of static data structures;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly well answered questions.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not all candidates answered this question, but most of those who did outlined the push and pop operations and the tests for empty and full stacks. The use of a one-dimensional array as a static stack was weakly explained.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly well answered.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly well answered.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Mostly well answered.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.1.HL.TZ0.12",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Galaxy Bank</em> is a US based bank with many banks (branches) across the country. <em>Galaxy Bank</em> uses a relational database to support its operations.</p>\n<p>Each <strong>branch</strong> has many <strong>customers</strong> and each <strong>customer</strong> may take out a number of <strong>loans</strong>.</p>\n</div><div class=\"specification\">\n<p>Some of the data in the <strong>LOANS</strong> table is shown below.</p>\n<p>The underlined attribute indicates the primary key.</p>\n<p style=\"text-align: center;\"><strong>LOANS</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"specification\">\n<p>Two other tables in the database are identified below:</p>\n<ul>\n<li>The <strong>ACCOUNTS</strong> table contains the account details of the customers.</li>\n<li>The <strong>CUSTOMERS</strong> table contains the contact details of each customer.</li>\n</ul>\n<p>The underlined attribute indicates the primary key in each table.</p>\n<p style=\"text-align: center;\"><strong>ACCOUNTS</strong></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: center;\"><strong>CUSTOMERS</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"specification\">\n<p>Security is the top priority for <em>Galaxy Bank</em>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the entity relationship diagram (ERD) that shows the relationship between the bank branch, the customers and their loans.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the table <strong>LOANS</strong>, state the output of the following query:</p>\n<pre>SELECT LOANS.Loan_ID, LOANS.Customer_ID, LOANS.Amount, LOANS.Type<br/>FROM LOANS<br/>WHERE (LOANS.Amount &gt; 600000) AND ((LOANS.Type = \"Home\") OR<br/>(LOANS.Type = \"Venture\"));</pre>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify the steps to create a query to find the names of customers whose account balance is greater than $300 000.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how controlling access rights contributes to the security of the <em>Galaxy Bank</em> database.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how row locking ensures the consistency of the data in the <em>Galaxy Bank</em> database.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Bank, Customers and Loan correctly positioned;<br/>for EACH 1 to n relationship / for “has customers” and “has loans” (accept words to this effect);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award <strong>[1]</strong> for any two correct rows, <strong>[2]</strong> for all three rows correct</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[4 max]</strong></em>.</p>\n<p><em>Answers may include</em>:</p>\n<p>Step 1: From the table CUSTOMERS,ACCOUNTS;<br/>Step 2: Choose Family_name attribute;<br/>Step 3: For a condition of the Customer_ID matching in both the CUSTOMERS and ACCOUNTS table;<br/>Step 4: And also satisfying the condition of the Balance &gt; 300 000;</p>\n<p>SELECT CUSTOMERS.Family_name, ACCOUNTS_Balance<br/>FROM CUSTOMERS INNER JOIN ACCOUNTS<br/>ON CUSTOMERS.Customer_ID = ACCOUNTS,Customer_ID<br/>WHERE ACCOUNTS.Balance &gt; 300 000</p>\n<p><em>Award <strong>[1]</strong> for identifying the FamilyName.</em><br/><em>Award <strong>[1]</strong> for checking that Customer_ID matches.</em><br/><em>Award <strong>[1]</strong> for identifying the use of both CUSTOMERS and ACCOUNTS.</em><br/><em>Award <strong>[1]</strong> for balance check of greater than 300 000.</em></p>\n<p><em><strong>Note:</strong> SQL is not required.</em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.</p>\n<p><strong>Access control</strong>;<br/>This means that different users will have different levels of access to the <em>Galaxy Bank</em> database;<br/>Therefore customers will only be able to access the information that they require to carry out their transactions;<br/>Whereas other users such as the DBA will have access to more of the database;<br/>Ensuring that the sensitive information within the <em>Galaxy Bank</em> database is secure from unauthorized access, editing etc.;</p>\n<p><em><strong>Note:</strong> Do not accept responses pertaining to how access rights are granted</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.</p>\n<p>Row locking prevents two or more database users from updating the same data at the same time;<br/>When a row is locked it means that another database session cannot update that data until the lock is released (which unlocks the data and allows other database users to update that data;<br/>If the database as a whole is locked, then only one database session can apply any updates; Locks are done using statements;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.1",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Identify <strong>two</strong> differences between a wide area network (WAN) and a local area network (LAN).</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>WAN covers a much larger area (national/international), LANs usually cover a smaller area (such as a single site);<br/>Nodes connected to WANs often make use of connections through public networks (such as the telephone system), nodes connected to LANs are usually connected through private infrastructure;<br/>LANs are more secure than WANs (due to how WANs transmit the data /how far the data would need to travel);<br/>A higher bandwidth is available for transmission in a LAN than a WAN/ LANs can have a higher data transfer rate than a WAN;<br/>LAN is typically cheaper than WAN to implement/ maintain (as the equipment required for LAN is less expensive);<br/>WAN includes a large number of devices connected together, LAN includes less;<br/>WANs are typically slower than LANs due to the distance data must travel;<br/>WAN requires hardware to connect different networks, such as a router, LAN can be simple and does not need to connect to other networks;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were aware of the differences between wide area networks and local area networks, but often spent a long time writing a description that only covered one difference, rather than the two required by the question.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.3",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline the reason for compression when transmitting data.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>The reason for compression when transmitting data is to save on transfer times;<br/>As it reduces the number of bits needed to represent data (when compared with the original data);</p>\n<p>Compressing data involves modifying/restructuring files, so that they take up less space;<br/>And this results in cost savings in cloud storage;</p>\n<p>To take up less bandwidth;<br/>Because data compression reduces the size of files to be transmitted over a network;</p>\n<p><em>Note to examiners: Award <strong>[1]</strong> for a reason (for example, to save data usage for sending files over the internet, to save storage capacity, to speed up file transfer, to decrease costs for network bandwidth, etc.), and award <strong>[1]</strong> for an expansion.</em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were aware of a reason for the use of compression when transmitting data. Some candidates, however, directed their responses to generic reasons for compression, rather than the scenario of data transmission, as stated in the question.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.4",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following doubly linked list which holds the names of flowers in alphabetical order.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n</div><div class=\"specification\">\n<p>Consider the two stacks: <code>FLOWERS</code> and <code>FRUITS.</code></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the features of a dynamic data structure.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how <code>“Primrose”</code> could be inserted into this doubly linked list. You should draw a labelled diagram in your answer.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Show the output produced by the following algorithm.</p>\n<pre><code>loop while (NOT FRUITS.isEmpty()) AND (NOT FLOWERS.isEmpty())</code><br/><code>  X = FRUITS.pop()</code><br/><code>  Y = FLOWERS.pop()</code><br/><code>  if X &lt; Y then</code><br/><code>    output </code><code>X</code><br/><code>  else</code><br/><code>    output Y</code><br/><code>  end </code><code>if</code><br/><code>end </code><code>loop</code></pre>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A third stack, <code>FLOFRU</code>, is needed. It should contain all the data from <code>FLOWERS</code>  and <code>FRUITS</code> and will store it as shown below</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align:center;\"> </p>\n<p>Describe how the <code>FLOFRU</code> stack could be created.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Each node contains data and also a link to other nodes;<br/>Links between nodes are implemented by pointers (a pointer references a location in memory or holds a memory address);<br/>List size is not fixed / predetermined;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[6 max]</strong> as follows. (There are 7 marking points)</em><br/><em><strong>[1]</strong> create new node;</em><br/><em><strong>[1]</strong> instantiation of values and pointers in new node;</em><br/><em><strong>[1]</strong> state where the search starts from;</em><br/><em><strong>[1]</strong> how to detect position for insertion;</em><br/><em><strong>[1]</strong> update pointers in new node;</em><br/><em><strong>[1]</strong> update pointers from the node at the insertion point, to the new node;</em><br/><em><strong>[1]</strong> update external pointers;</em><br/><em><strong>Remark</strong>: Some answers may just use illustrations alone, or very minimal explanations: see </em><em>note below;</em></p>\n<p>Create a new node (with pointer <code>NEWNODE</code>) with data field Primrose and two pointer fields (next and previous), to be inserted;</p>\n<p>Perform a linear search, either from the beginning or end of the list (using pointers <code>FIRST</code> and <code>LAST</code>, on the alphabetically order list;</p>\n<p>The location/position of insertion, is found by comparing nodes (Primrose to be inserted after Lavender, <code>LOCATION</code> points to Lavender) <em>(<strong>Accept</strong> any description to that effect)</em>;</p>\n<p>(<em>At the end of this phase, the situation looks as in <strong>Figure 1</strong></em>)</p>\n<p><em><strong>Figure 1</strong></em><em><strong><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAv4AAACaCAYAAADRn0zcAAAgAElEQVR4nOydd9wdRb24nym7p7wtPSQhCSEhBBI60iwoilG4V0AsoBf0p3ABxQZYUMrFK1wUpFlQLgaQIkVRFKQECUgTAkkglDRCes+bt522uzPz+2POnvd9k2C7CSDsk8/55Lx7dndmd2dnvvNtI5xzjoyMjIyMjIxtSzq6CgBb3yB6P+mfABhAvc4VzMjIeLuh3+gKZGRkZGRkvCURm/8h+m/s97t8PWqUkZHxNifraTIyMjIy3tYkSQJAHMfUajUAKpUKAM45nHPEcfwPn9dai7H+3LVaBAhKpRLWWryxva/BXTSOSZIEYwxRFGGtbdQDIIoi+hrq07pba4miCGMM1Wq18XsURQAYY7DWUqvVtjiHr1+tcY70HmRkZLz1yAT/jIyMjIy3Lc45tNYkSYJzjlwux5IlS7j88st54oknEEKQJAlK/eNuOEKAkv64XC4HQFNTE1JKhBDEcYwxBuccxtj6MQKlFEopwjAE/CTEGNP4PZ0MAEgpKZVKjXP2PS6OY8IwJIqiftcXhmHjutKJQy6XwzlHGIZIKf+piU5GRsabH5H5+GdkZGRkvF1JhXrnHOVymVtuuYWf/vSnLFy4kMMOO4ybbrqJQqFAGIZYa5Hy79eXOWcRwhHHFq0DoigmlwuIkxgpQCmBc35i4CxI5bX+xpiGIO+cQwiBMQYhRKP8dCIgpcRa29gnCILG8el+WuvGtnTf9Hvfa0onFOnvQvTzRcrIyHgLkGn8MzIyMjLetmitG4Lu+vXrufrqq5k7dy7lcpnp06dz3XXXNQTgf1RP5pzFOcPateu45eZbyYUBOBAIlJKAwzmLdaYhuKcCPXhBvFwuNyYnqYCeJAlSyoYVwjm3haAuhGDBggWce+65dHZ2opTaQqCXUqK1RkrZ2K61RinVz6qQkZHx1iET/DMyMjIy3rZYaxvC9ujRo9l3332x1hIEAXEcc9NNN7F06VLiOP6H3X28MC154vEnuPTSS+nq8nEDWklc/Z+UEiUVztEQztOPlJKmpqaGxr5arTY09Knwbq1tuOU88sgj3HHHHY0JynPPPccdd9zBihUrGnVKz53uk2r9U8tC3/uSkZHx1iPL6pORkZGR8bbGOdcQfk8//XQef/xxFi5cCMCzzz7Lr371K771rW9tIRz/zfPisDZh7gtzGTlyFK2tBYyxSOUQCDa2b+TllxZRrVYZM3pnJu46HmstWmu6urpYsmQJnZ2djBgxgrFjxyKlZPXq1Sxfvpympiaam5sxxrDDDjuwZMkSrrjiCnp6epgyZQq77LILkyZN4ic/+QmTJ0/GOcfy5ctZvHgxQgh23nlnRo4cSZIkrFy5Eq011loWLlzI0KFDmTRpEkEQbK9bnpGR8QaR+fhnZGRkZLztSX3fK5UKjz32GJ///OdZsWIFUkpGjhzJXXfdxb777otzruFqk/rPv6bfv+sgWbeIj55yNZOm7M4F3zuTQgy4LuKeZRz/uR/y+JMPUq1WGT94AP/9h0fYc/JIBnWu4stnXsC0adMI2sYwOtjEfQ9NZ/Wri/j2ZVfzzFOPMbi1QJDfk9332JUrrrqEK856F9f/YQOV/HAOe98+PPDLL3PaqaciJp7G1Rd8gFfmvcS/n/BTupbeQxTBlPEf4ro/3cJwU+W0jx3FYx3d5ElYtWgR7fHO3HP3rzjiAwcAPk7gnwluzsjIePORufpkZGRkZGTgBdxiscjBBx/MOeecw8CBA7HWsn79es4++2xmzZrV0MgrpQiCACllI2XmFljJyy++wovPz2PHkaN8wk6XUC1389UzzmDDhhK//vXtPPTQg+y2x1i+d963iavws6v/l3unP8R3/+f73HPvXZzz7W+yaf16zjr7HNaub+eP997H984/D52DnXbekbHjBvOpT/0nUyaP5bbbbuSHl3yPF+Y+x7PPvsDYMYN48uG/8PFjT2KPPffjgT/9nt/edT1OVvjTA/dQrUXMW7iU5a+u5fQvfpWbb5zGmF3GsOiV+Y20nv9sjENGRsabj8zVJyMjIyPjbUvfLDep73tLSwunnHIKM2bM4LbbbkMpxfTp0xFCcPPNN1MsFikUCg3Xn9d0ibGSmX+ZzcZ1Hew6YWcv+Gt4+eUXuePX0/nJNfdzyLsOpqcnYfS4waxfq6l29fDoE09xxjfP5vQvfA4H5PYfw3U//19eXbGK63/7AO86eA/2HDuUq2+ewW6Td60X1kpT00A+/KF90ALuf/pVOjojxo0dzc03fJ+OjR186+wvsfv4EgjN0OG/wSZlFi9ewrKVGzjxy9/gE5/4NCteeopCs6a5pdCwZGTZfTIy3jpkGv+MjIyMjLctfd100tz2abDsRz7yEYCG5vvRRx9l1qxZFAoFoH/6y60iDMuWzievNK3NzZgIEAmdXe20DYKJE3enswcKzZoN3SXGDx1Ade1KNmwqsc9Bh1ADqpUquIjOUpm9Dz6UQUNHYJVmzpN/4YWFCxi8wwAchpcWrCEsFNmwCeK4xvyX1oKBsSNbefLPzzB5l70Z1NIGog0osnr9aka0tbB63Vpcy0CO+PCHaWkOWLl4IStWrWLo8OFbTGiyCUBGxr8+meCfkZGRkfG2JRXe04WsgiAgCAKiKOLII4/ks5/9bGNxqziOueqqq1i7dm3jmLvuuosXXnhhq+eudG5g2fL5TJgwitbmIuvXrWPNqo1EsaVUhe5ymSiuMP3BJ5j5zGKOPeYYmotFOjs6KHd3Ui3XWLZiBetXr6VUqeCcpbWpyIszZ3He+d8jimHs2J2ITA+LlrzCqNHDyOVK9HSXmPXMYoYNGU9zXlOrluns2IhjE6tXb+SqH/2MOJHsv/f+LFi0iD3234fdd98Z4ohHH32EameF0TuO3eJ6MlefjIx/fTLBPyMjIyPjbUtXV1e/hbHSiUAYhjQ1NfHd736X0aNHA34l3Pvuu4/f/va3RFFEV1cXF154IdOnT9/quTs71vH8Cy/z9KxHOO6Tn+Dd7zqMr3356xRbBhCEcPIpp3DSyV/iiH87hne+8wgOe+872WnMaJqbi5x2ysl84hPH8eEjj2DFmrWM3mkc0++/n/84/pN86lMn8sLCFYyfOIURI0biRA0ZKO6574+cceZZvPzSPJ76yzyGD9uV4UMG8tGPfoQXXljAcZ/8JEcceQxfP+scTjjxJIYP35HZs59jp4njGTiwSE9HOzMefJBxEyczcOAQkiRpuEL1vTcZGRn/umQ+/hkZGRkZb1sGDBgA9Lr8pP+n2u3Ro0fz/ve/n/nz55PP56lWq/z0pz9l4sSJBEHAggUL6OzsJI7jhmtMuhCXlAXe/Z5342a3k8gyQVilpW1Hxu+yD98+93+49Ie3Mef5xzj6mPdz6he/hBUgAzj5xOP5ybQbWD7vOd5z+JGM2nUfWobsyFH/9kEWzFvM4R+ciokNcsfJaC0QieGdhx7Gw3Oep6trHc3FIu846H0cfvjhtBXznP6lM6hUDffOehZdE3z76+dy3GeOo7apiwmTJjFh770Z0NxMV+dGjvy3Y+ga+g5amwto3d+1J8vsk5Hxr0+WzjMjIyMjI2MzUu22MYZ58+bxiU98gnnz5gHeHejd7343O+20EzfddBMnnHACV111FcVisXGMtZbAVIhL7ZhwCFYaaraGdoNoanIkpkxPj0KGCVIpcroJh0BpAdVOStXIL65VHEBTGBJIQ7mznTiytLS0IZVmbawYmKuhTZVQt7KxuxPVVKAoA2prS7QMasWEEcpIah3ddIaColUUCkVqYULRCnpqES5fJIgtWia4Ugcb9VAGt2iyLP4ZGW89MsE/IyMjIyNjK0RR1NDi33bbbVxwwQXMnz+/YQ0IwxBjDFOnTuXGG29k0KBBjcXAABQSDKAhdiWEMkjbhnMGpQQmliSqg0AGWJoa5WpTgfo5ulUTOQnKJai4irPggjyJ0FQl5JwhhwETEjmHCwQBFmkE4EBZiDVYiHIQViKQimouIW+gaiUuCChYwBlwCTWdQ5G5BGRkvBXJfPwzMjIyMjK2QpIk9PT0UKlUOOqoo5g6dSpBEDSy+kRRhFKKjo6ORi7/dFIgpQQhQApwAilzOEISwAqLdTWUhJzUSByKCAUoAJUDnQeVo0VA6EChsaoAuSJOKYQESRVnLMQhiBilHQ5HkgAqJnZlolhhTAKBn4MgAGdIABKJAIyzYMFZhTU5NGUkr7E2QUZGxr802YQ+IyMjIyNjM4wxbNq0iTvvvJOnn34aIQSbNm1CCNFI7wl+crB06VIqlQrOuYbgL4TA2SpCJiSmGScECQIlAGcRwmcRwmmctSRWonUEQlA1ipwUWATSRAipsQ5QCoMjjg1aKxwK5RTEDmQ3jgJJIsiT97/pPCYRyJwBLInzmn9wWBRIjVIGJwzOJKAkBBrpNKD8JCEjI+MtRSb4Z2RkZGRkbEaSJIwYMYKPfexjrFy5kh/+8IeNVXqllEgpvQ++tbS3tzfSgfZD1EBWwDQjhMYYjdIghAEiXAJCK4QM0EIhTBdojVPevSiOLXlpoVaDXI7EQYIgCBQShzUOISQ4QNeQKPK66L17EonWAiVBiIQoqZGoPDgN1mHRWANWJQgMIgBrJE5opJM4yAT/jIy3IP18/Ht6HIWiQBBTMwFBANKAoIZQCpNIlJDESUSQU+AMJgIVhFQkJIkhRJHTnSQmxMkCSiTIapc3eao2rC6RJDFCtSBEgLUQGUuzqndeAKqMcxoIMEIgnUFivGYEQEqEA4Q3tUqt0TLzWsrIyMjI2DYYYxpZbLq7u7nuuuu4/fbbefzxx1FKIaUkjmPCMERrzWOPPcY+++zTmABorRsr+2ZkZGS8WegnLRebfAclhCDQ9TgfB6Le+aX9l5QCYwzOOmSoMYCxNULpCCSARUoQDiwCggLoPNZGCKcIZB4foeT3DbTEAomxmCQB5ycBzjmcdVjrsLb+Nz7bgnMW5xxhGCJFJvRnZGRkZGw7UoE9SRKKxSKnnnoqv/3tb7nyyiuZOHEihUIBKSXGGCqVCt3d3Tjn0FpnaS8zMjLetPSXmOsadyG8H6JW3uXP2/sEUkkQoLRGKY1QAcb6s+SkQjuJtODFfVeX7QWgcbFDBgohQoQMCKRECtBSeqFeggwkKvS+hUJ4jYqSwmtXVIDwzpEoKRFSIoTIFhTJyMjIyNjmpPn8tdaNSUBbWxtf+tKXePjhh7nkkksYPXp0w69/7dq13q+/j5Y/0/ZnZGS82ejn4+/wWn3rHCJKQAqs8gFEDocyCU5oBAKTGKyNIMhRs1CwJaQogNGgLSY2KAUJEqskidMIEnRiEEph4xpOeS2+kCFVA1qAcgZdS3BhgNAgXYRFYoRG+RpiE4tQDoFCILDOQqb1z8jIyMjYRqRKpVSY11o3fPxbW1s5+eST2XfffZk2bRqPPvoo69at63dMeg6ZuaFmZGS8iejn4x/F3k+/WBDQ2cmC+fNZ2q2o0k0YhDTbMh21FnYZP4aJE0aRdK/k6XnrGDtlH0YVenh17iI2rO4gGWSplhyFcCA2jBk9YldGjhxABBRoBwTty7r5y5yFbNiwAdVSY5cpBzBpwgQ0mg3zZ7FyQweJCmmWXdTUEEpuAMKtgppjjwm7M3j0YEQkEFqBrDv8Z2RkZGRkbEdS3/8kSTDGkMvlWL58OWvWrGHPPfckDEOAhuY/0/pnZGS8mein8Q8CEEIBlhdfeIFp06Zx15/nYnM18vk8snsD67pznP6FkznvnDP5y5NPcsV1v+cr51zOqMk5brzxRv541wN0hl1EVUkhHEisKuy317s54IAD+X+nfpZCIFi/ciWX//B/ueG2P9Dd1Y1srjJpz4P43gUXM37srtx5+x1cf+sdRELTonqIgiH02DYwKynIHF//0lkcf9Jx5JTPpVyr1cjl8m/E/cvIyMjIeIvinCOOY4IgQAjRyOhjrW0E+AIMHz6c0aNHkyQJzjmSJOnnIpSRkZHxZmELVx+ET0/Wph1PzXiAfQ88mqnHfpA4jhkYr6eDHdlr9/FYA0OCMvPmzkHlWyHuYcXKVVgZ8OXzv8/wpgHoGBYunsP11/6G6X+8lwl77c2HDypy1Y/+i3vvmct3zr6YPfaYwIoND3P1dffyta+ezT1/+CPHHn8043ffjUQ2MXP69dz6wCJO/OqXmTC2higZDtnjAMDhYoNQkjDMspJmZGRkZGxbhBANDT7Q73v6e9/tWuut7peRkZHxZqGfxCwAaxOEgIF5hY7KvP8D7+VjHzsKgLZkAyU9hMD5pDx510Nba4FEByD9yQ464EDe86GjGNtcgFKFj3zkvRRyo/jiF05j4aJl7DUqxyMz/sThR3yG0774CV+uHsbESe/jO+f+iLUbBO+YPJExu+yBVEX2HVXmgWeu4tjjj2XCDgnKOAomoKZKCNc/21BGRkZGRkZGRkZGxtbZIuooDEKUVkRRRBgGVKo+BCCGRgCtA6T0ac5MYnHObwzDkEqlQr5QAAdBENLT083SJUsBSRCEDBw4kJ12GssjjzzM7+56gpWrfM7jvffcm/86/3wmTVJgDKIeepBGIEQROBzSyvq64/gVCBNHnMTb7QZlZGRkZGRkZGRkvBXop/G31pKYhFArQpfQHAgu++Fl3HjnL/0qhm4Dnc1TuPqyC9lt3EjCUFOJFTUBRGVCW+NPD/+Z004+ieHFPE1RlVeWLeKVZY73fvgYDnvfgTQPcHz9jIv41gVXcNpXT2TooEGMaRMcdNiRTD3qWIQGExWQKsQlDpH0oHVIJYGKqxGIQj2ON41JdugsZ3JGRkZGRkZGRkbGX6Wf4C+lIJQBOIvGoqxhp50nMOV9+1GtVtkpXsQquReDhwxGa4ERGhdoag4QFhmVaVGGXE4hbcQTj85gwfIVnH3RbXz1y0f7wqoRe+z3IW698wD+/JcnmT17Nssefp4f/fBHXHvzLdzyh7t45667YhKH0oIkKlOpOCILocj1r70AlCSKI3KZT2VGRkZGRkZGRkbGa9JP8I+TmEAH9RRkUK3V+OhHj+E/vnAsZQNjxDpWJ8NoU97Vp1qtIoRfVRclkUoycddd+eYPfsDoQoE/3HwDZ/3X+XR0bGLFahg7AlYvXUZ7+0YGTxjO4e89nIMPPpjipwRTrr+J/77ih7z00ku8c+KuSOkd940xNDeHvQr+OhYLsYNAZEJ/RkZGRkZGRkZGxt+gn4+/0yFVgLiTpeFQlgSDiU2VFmC4AtwARgTdhLJGJCAXDiWIywxVFYg1XTokGdSMcW1YqTj+xM9y7ueO45qL/pPvnn4MXes3MvvFp/jocf/O9dfeSxLnKeoh5IcFfPbU4xk5Ygc6V/ZgE4MwMSQRPU0jKSUlWrQlcAFSGcj3ELpWyHWC7MCw7QR/ay19ljbo/e4AKkAF0/heBSK/8rCrAp1YB84ZcDHYCKwBCzUMVP2nA6AaQTXCEvtzN4q0+CAGg8PinG2sDJnWJXHpHhCZiJqpErsIg2ls7/vB1IiwJBgcBkiIsZSBBIO1MZgakOD8lKrfpx9us892pO81G2NwzlGtVrdvodDIz52WHUXRFs9gWxPHPk4lXQDojVyROr3GvnVIr90Y09iefo+BpP7xbca3ot5/rvF7DODixt9J/e/0s/l24wwRYLBY5+rtzoC1vrB+bdH2Hmsalel/TmvBGqwzJFu27n4YY/pdb5Ik/+Qd/b9hre33LOI4xhjzV47YNmze5rdX2+9L2v+maTEBTGLAbP4cTePTb7vp02/Zar3HBkwXJaAEYHqoAYmzvi1t3qf1uUwHW/aH1vf8VYAk6S0jhjIQWwtJ4vvWv9HGtkalUsEaizUOY1wjhq7fp14ZW7/uCF8Bh/EluqS3wn0fW2J66x4ZcA7r/KhA7Mvs+/5sPhb2HR/79gd92+Pm98zQ/9nFpP1ACeeixj59D0r38ddVf3IuwvcurnHdffsdl5bsIt+fNEbArY5kr9s4lpHxZqO/j3/6ciiFEJIkMWjVJ2WOEHj/mro2XuexJqE5sFBoIrICIwNyArSz2DjmmBM+x+//PJs77/wdHz7qRD7ywYOZuOtErvrxxdRcwp577omsLOcP9z7CurWb2GfvfZBBAokFoUAEBEGATSKUyIMTgKzXYNun83HOUSqVWLVqVSM1m9YanAbZ43cyA0F11usiwLaALIOsgCkhiBEkyFROEYKaEgyq+MNXtTpcl+9tOvIgbVC/v71xC74uvXVKF4Kx1tLcOohypUwUVcnlFE5YnHV+BWNXn8s5RTqvK5gq5UAinV/rTACxFMRSEhpDYB2BddSUwCFx9fv7WndX9B0Yt2NGJWMM+XweY0wjJ3ZPTw9hGG5XIURKSRiGVKvVhvDX3NzMsGHDUNspniQIfBswxjB//nwKhQJKqTdkApCuUhrHMVrrxgDf0tJCuVwmiiJyuVyjfpERpG1W9JU0RO/3xtMSEBpLTRYa5eVspfF98+2JkCRS+XbrfIuWtnEqnEjfsfrwXk/xlesjF9dUb1vx2x2JdBgJ2m59VdU0R3tPTw/FYpEkScjlckRR9A/cyW1DGIb09PSQy+XQWlOr1Rg9evR2Lzftd5xzrFy5kjiOt/sqtGk/p7XGGOPz4QsF1m3lOXpqfV7JnBHEdWtxzlbo1gMAGBivpz0YDsCAZD3dagDKxWiXAL0neK3+rK98qC1Utb8PrZFlU30JmYEV6ChALnHkjKOiZaPN/r0458cbrQOiKKqvIRDibN/+rreSTpi6wAsSSzo+b9FH1y8ssI5S4L+31RyVIL02QVPsqCmINORjMMLhpGi0Aeid/A4YMIBBgwZhrSUIAj9BfI1reu0hQr7mr6Lf/3KzrX8LUT/mb/TVWTbAjLcpWyTAl0gvcEvBLhN3obmpabM9et+WXFMb48aNQ8QVcEV2HDuOFavXEQKxkRTDgGEjx3D+uecyaOAA7r/vAf7jk4fyv9dN45LLf87tt9zITTcm5F0PAwbvyCU/uJj3vGsKxnSjpAYH+WIzY0aPRtcnJb5Pr3ds2+HFlVLy7LPP8uSTTzJq1KhGPmalcl64B2TSjFW9mmdhCzgR4WQNaQp1gceinPOdsnBEUtNW8/uva0pYXvKdWXdOIGzfC0m/e02blIIkMUgpkVJSqVRYvnINI0bsQLGpiBCWKKoglUIpSaOTdJq048uZGlXthX7pT00iIVEQJn4g0xZqOi1fpFXYKn1ru72VJdZawjBkzZo1bNiwgcmTJ29hldnWpEJeKugsW7aMTZs2cd555zUE/75C0bZYpCc9z5o1a/jFL37BPvvsQ6FQeF00u1urS99Jh1KKcrnM0qVLGTRoEIMHD27sa4xBBn0Xz/PCvr8jve2/b0sJLER94nVCV2t833y7EQIjBMJJBA7hHMr5RQYFFovCCYcTDovE1SWeMJGNMiPdO3kKjd9uhMNIh3oNwT+9tlTL+eKLL7Lnnntut4nfX0MIQZIkhGGIMYbly5dz0EEHccghh2y3Mn3f4xeqmjNnDjNmzKCtrY22trbtVib4ex6GIevWrcNay6BBg5AItNBEuvdd8M/RE6k+zzdRJPVHFNoaJdUCQGuyiU49uP69nZJuRbkE5RJcH9H8NXuVPq+4dL1lNtUCugpeGG6rSLpyjsBCYASxEvXsdH9/XxVFCUEQsGrVKoYOGdp45kr3HapF7ymFRWAQWIwIEM4incNIjaCPhaquEAosVIO07pKqdo1T5mNBpByxgnzibc5ofzNT5UulUmH58uVMmTKFI444otFGpJR/t21D9Pv21wX//n9tfevWj/5bffLmzySbBWS8fdgsq49BSQlxwoTdJ3P7Hb8mKQ7a4kX1/2DMXvtxww3XE7YNAhfxje+cRy5fpAwEThBVqgRhwIEHH8R+++1DJAaicyV2GDWKS35wEd/9XoBUElPaRJgfiA6hUoNCYMAKEJo99juQX97wTkSh6DV8ToDw5acJ/Lf1KxtFES0tLUydOpVhw4Z5Ac+Ck15AkUkOp3o7dGElVlicdCgr6guhCS//iwSEIRE5dF1ZWA5rFCMv4JgQpEu7TNlP5WRsglKKOK6hlI+96O7u5oYbb+aQQw5ml10meFeLpEIuzNfdePoI/vW+TTmHUcZrn5zACe/UY4Qj5wRYL1hZZRvP981CqVSiubmZmTNnMnfuXD760Y9ud5eDdCAzxiCE4KGHHmLmzJn9zdnbSOBPSc/l0+iGHH300X617O2sYd0a6f0VQjQG/K6uLq677jr22msvDjjggMZ+SikQcuuiTUPgT43/fkIgrSQRvV2P17p6Nt9uhbeY4RQC64V/qwADwoALvcVLWgwaUc/1q02vgJ6o3ueWbrfCYqVDua3f33SV1iAIWLt2LWvWrGH//fdn/Pjxf+9t3GakzyGddNx9993b3RKUtm8pJUIImpubmTp1KjvuuON2LRe8K9OMGTPI5/Mceuih2MQihdzqc4Qtn286J1Auoia94iZnKlTrK73nTZVY5ZGurpwR/+g75jDCjwU6LlANfPvNx5oosCgrUPV+Ftw/NED5dpdw9913s/deezN69Gh0oP763MElCBIS6a9JOkssNVKk98UhrX+vhAOjvFuhigOMdr3vVaIx0mGUIzQC6yyyLvinKxGXSiWuvfZayuVy4/1PLYLir0yKt3oLRK/GX2y2U//9+2r8tz7m/z2TiS3ZuhUlI+OtTj/BXwuJBBLj0IEg39ZG1fYxVYrUzUbUNcc5mls1SVRjU6lM68CBJICMLToQyGIzIKDajS4U6yb6dNEtRTEnSQwU2lqw1p8+F4BUee97WK0gmzTFQoHIQmQshUD2VrveYW/LVzYd8LTWDRNmGuxsqbvkECFciBO+c/ceUHXhR5SBACeCevUciATt8g3LY54EdK+WVDQ66P4dpy/Xa0yFcA1Xn7hWAWuReNOwSxwy7LUUbH5TnBT1M0sQ0s+pSFBYBLrhwuWEQmxtnOp7Lvp3l9tbLE1dYOI4pgZYgWMAACAASURBVKfHu1ptS4F7a6TuBun3tOxU2Nrc73Vb1id1Jenb9t4o+pbtnKOnp6dh6hdCoJSq7xOTDrgW1ad9pIoCkPQRXuTmKwf2EfY32y4bPrr1dyP1YRAGZAw2rG/zjoq5dJEP1Vuepo95UKb/Ce/Q9lfub+rm5Jyjo6ODOI7fsOchpewnjL8esS7gBb5ardZol6/H9SulqFQqvRNQJUBQt/rWBbs+HY9vM/WeSaneXlToRo+NDHsjwWTojxGyn/IctqJb3orAbYVAU3dPEwVUquuWEGBxUmIRfrwT4h8boBxopSmXyiQmodFExWY7NdxBJZCAq2KEHzelcySi913S2N5mLkDVPedRQf3kzk+YpfblwRaFps/dOceQIUMarpfgFSWv1S62KoanLqxCN2yDjZ3rv28WfJhWgrQ/2Vo5jRMI2X+g2mrVMuf+jLcv/d4vJXyXqfMFsJa4FqWWvj70EQaUF050IGkbOBDr/KQhF/je1AHGWghzgEBIh0NiU6dcB0pQD2KNAYszABqkRhYKOJFDOEkgIB/4SYej/mKn3co2fH9TgaZvUKm/WIdDkFjng3YdGANRbMH5oNhaBNgIZxMcPkzBpgFG9TErMRbhEowAI8A6MNZfe70YksT6zq8+2HtNX29AVRDUBRLr65vL5XHOW2zAYkx9QTMHSQKxrdffmMZzcc5hbOQvon7/YpM0jrOJv25nbEOjA2BdPZaA7d9tOufI5bxlxPu96tfN5z11dZBSEgQBhUKh32/pJGxbCkLOOcIwbLQ/rbfwxHvd2JobUxp30Cvw+zo7U8W5mFqt1M/HP0l8Y3OGvkr/fhOozd+zVKDtu08S1ert1gcN++bn/HuX1F1ysNSS/oF8afs0NqmHA/oMZGkzj+PXDtZNn216D8IwfMOex+b3qO97sb1Ir10pRVNTU6Ndbm9Sa1uhUGjc795gUkNSX6zR93c04qB8n9QbdO73EdgkRuCwTiCcxcQ+mNQZg60HEKfjlO8T+/cvjbKtrbdl48cvfPKGxIJ1CQKHcz5MNTE+ycLmsmVfhcFr9WPGOqQErYP6PVBYP2BirQ9j9f27JTExkQVnewPjfQC8D3KPbeyd7VziM0I4/5+fKMTUhxmsMeAsxoETDuMMrt7+03r2VYRUKpV+k+DUMvqaorTDxyjYxuAD1mEQjf2tTephu/49bzxg68CJejx/7xiYntgkMbjevsK759Wvy9resarustd7/1NlgMCYNy6RQkbGG0E/wb93di5BKoJcrrGtd0btzXNeK6D8LNzVw4IFaCWQOL8ZgZCyrlmRCOkAiRAaIZQ/mwCEQ2nvs+sVvLr33GiE1xduVofeGm9rHVQ6wKWTAKh3HAikVHSuXcmKpZsACAIJwjL/pVdRGpAJy5YsYf3GLpCQ1DvpRS8tIk5AB5IN69cw54UFWOGvX0kBWJ577jkEoJVslO8c3v2qT5cqG7/7q1dK+wGw7lqhlPdLf/HFxWgFUeIgiv2zSAyRMSQ2JpT+OdvYd3xaaWrVKrbu214tlxFKkiRJ4z4kJkHUrRuJ3b5ZTjYXql8vt5dU6EnLSzNX9K3PtvTv37zsNzKjz+ak9yAdbNMBP90uhEAoRxKVyeU03T1dPP7EEyx85VVAEMc0spI44/oJ/n0tK2n76itcW+tdEHTgt1UrqbUBwLJw3lyu/fm1vPLKKzw58wluu/MuqlGZnnIXzz83F4GXHYzxAp4PfvfGAGv/+sQqdXFJYx1SX+Y3ir4TwdT1Z3vSV9Cz1hJF0eui7U/LSO9/+t3/BlorqrUqs2fNo1ZN+mjmvSCcukRFUURivKAv8H23wBFohUMSRbVG+0t1SE74spIk6WfhSGq91y6k8kbsrnbuu/tOHvzTPLSQOAxJHCGwSAWd5Spx1U9S+mbsSi1mr9WXKdk7oU4tzUJAFEf1FNeO9k0bWbBwPlpJpAInoWfZK8x6cQWxhXjjBpat7mjs70zMvBdeZOHcl4mMo9bZzszHHiEyXtAWUkAS8dTTc+jqLKOERCjZL6i3b3vo2zcCrzkh7KvtF+mFpLOD1MqcjupeoMDV75dLvLXDRDE2tgjZ606YCvECGpaHvvFIrm4mkaq3DW3+DkupiGNDHCco9cYpWDIy3gjk5n9I6BW6hWhs89sFCJ/xR+ITZaWd5ubOKmm6LYnFCo0VGp/UUmI2cxCxXowkTb1l+5w3IcBRz+pRT9yVdgAJkqSh/d++CCzVKCFxcP8dN3H+uReyfGm7n/BUu7nttjtYsLAGlU3ccP0v+P3d91GLQWrBxs7V/OJnv2DVhnZiYPZjj/CzX97Iqs4acQKGGuVaFzfccD1Llqxny6yBjr4BkrIewNV3sw/cFT41J5bbb72Vy394Ba8uWuPdgNJOVym0UgRKkZgKGMu8l1+h1A0dpRJhLo8UEoQgn8s1Ouq09Eq1Wn+2/0yiuoy3JM4RBAHzX3yRb3z9G5zyn6fysY99gu+cewHr1q0ljhzta9sROJKeMlJrbwZwBhNVKVW9FrYaW7QSlLo6cCYGKeuaVm+VyucU5VqP19g7Q0fHWv54z32sX7OOBYvm8eiTT5EPNffd83u+8e2zfZ+kQAkHJkHW+6nEecH/zRTLkvHaOCAyCbgEa2KefPRRfnDpFcx+bkHdmmmRUqCVJEoSTByBswitCKXFlLsxUoCLiUsdVBNDPh+CgJ5ShZqJvdsmAoMXMoPQrx0Tx3Fd+BVE3T1gHZUauFqJGff/kXsfeApIsCYiKIQkPZuwJiZfzBPkFSau+fSc1qKUalgt/3ac0uZKDwCLsRFzX5jDpZdezPwFL/rUnNUyt9x0DZf85Bp6apaHpz/AtBvvwtQtEXGlxM3/ex0XnXM+neUKTz8+gx9ddjHzFq8gco7ExCya9wI/u3Yac16YW7/p9h+e6Lo+n37bE9Or7bcOV/MprJP0uWKRfeYESmuES8BZVKB9nIHz4fy1OGkkvcBZbF3jL3A462UDn27b4ogbngRKbWmhDXSAViHbNTVdRsabkNeY6v61F6GPqw/UZ+z12bhzfs5e16J4i7zB1H3xpZN93rHeLsKlx+OzIKT7iHSP1HbX8PGk0RG8bghBLvTTmjnPPs+dv3mR8buN56xzTiOqlpk/bwFr1m1kzwmaVxYtRA8YT6pI0IFi0YKFVGo1rIBN7RuYt2gRKIlSoBBoLVm8+BXK5Yp3T06F+vRi+3Wnrv9XBziBtQapJB2b2rnxlzfyyoI1jNtpJ0475wyKSYI1guWvLmajhB13aGFwW8iaZSv5xlnf4L8vvoKWkSED8gVMYnh1wQJyTUXa2tpobm0himM2btwIUrB63VoGDR5Ea8v2zfCR8a+BqxqemTWbk04+hbYhozj77G9TqsTMmjWHVxcvZfTI4VS7ExYufIkhgwdRKAYEzmtVoyimnMDKld0MHz6MwEnWr18POEaN2wXlLMImJDG8svwVck2CwcVBNLUYcvkQKVVdG+gnoklc44B37EdMGz2lMsVigfVrVlOreqvVkBETvAAmJbVKlVwx/7cuL+MNxjpbtxx7i2ulXOYvTz3N6bbmXSPjGEdCV+dGNnZZAuFobW2lKcizbs1qCoUCYa6FnI1x1tK+qZPCwDzlWsLKVWshp2ltaWHwoCEIBxvbNxJoTbWnTFSL2GncOEwUETY1UerqZlVXjcGqSqgUqnkAzllCFbB+/Tp62pehB9UoDBpFs3FUat6y0NPTQ0dHB2PGjMEY04hd+nvRSmG9HyzzXn6Jm2/5FWvWrubcy29gj+GOWbNnsrBmSIyjo72dRYt7CITCYUmiiNlPzaS9vR3rHJVSN8/NfpZypYQSwlsZnGHms7M49hMf8a42QvmJsv2/W5ZS1x1hnbf8hyG2WiPKheRUqpFPcFKTmIRA+qyC6Ro4xkrvRpWT3s3VJHVXJIuQgmq5TJQk5AtNqLqsYZxBC4MTECc+EFuggbqVx/qJojXeApjpADLeTvSPpfur33r/2nKbbIjw3sdX94qqzmBF2snJhmnPS/XpUb055xtCbR/TX++5+wvA7nV8W61JqBqFlNBShP33O5AbbriZT3/+BMa2hUipgQBsD0pJpNKNrA7FYg4TG8q1GuWqvzv5pmYSFHEMIoiw1pDL5zGJxSQQx1DYPJNqY3rU+72X1P3Ccuutt7DzuHHssfvB3PqrWzn6pBMZNLyFP9z2Gy66+FI2SMeB++7KN756Mtdcej2PP/k4U6ceyTs/vC9Xff8H/Pj7P2DGww9TMwmjRo7k2uuvo6vcw9lnn82TM58myIWceOKJfOVrX6PQnAn/b3dE2MTvfv17xo3dhZ9ddxNDhw8mtnDSSSeSRDEXXvQ/3DTtZ+S0pKdzE9f99n7ec8BePPSnP3HZZZdREznWrl3HwQcfxI6DW/j9H36P1gHTfvUbJk0Yw6p5c/nuxbfw1JyHEbkKu4z4EL+8+UKq1RK5MEehmMc6i0OiA82y5Ut57vnn+djHP84f77mP87/1RaLIWw2+c8FPOO64I0kiQS6fCf3/CngNr/JjSZwgpaRWi72LigAtJevWb+TLXzqNZ19cQksQMmrHUVx5zQ186/Qv0tPdzdW/vo+mgZobr/sFTy3exDVXfJf/+ua3uXf6DDaWNvHBww/nJz/+Kbkg4Ovf+AaznnmGQpCjo72dM884k1O/8EWWLljAT396NTf84XF2bZnHiy91c9LZn0cLxZJlSzjt1K+xbvEsmkbvzI9vvp0hg0dw0w3XcfPtvyZJEnbbbTemTZv2Dwv9qVlXCO+2JgQcfvj7mDt3Lt885zv8etrF5EKJcHkcjqhWwYevOCQS4SxxqULgJLGDpkJIIae9tdzfYB85pwOEklTjKsKF3uqxDZAylaz9mLVq8WJuv/U2Jr3/Axx6yH4kNkHjo3CU0hiT1N1b4fEHH2LBknXsv/87mLjPRDDOuyJJiEoV7r77bh58cDobNnUyafcpHPq+97PXPvvR2qKY/vD9zHp2DgMGDPbuQUZSqxrK5TIDBwzluOM/zcABhdesd0bGW5W/w2m67xp8W0HU8/4D4Fes7XdEn9U4cYEPNsIBJRA1ELW64K+9s6IDRzU9Ig3Jw6cs678M4V+p1XbAoQMIFBRCxUc+cjTjdtqZu+++F5Qgn88jZAguJggUuXwRU1/5MIprNBebyRebyOdBJDFGSqySBCFooQiDeoen/KDQG0u6VePpa7gpONo3rudPD07nkEMO4cgjjmTdug0sfGUxAH9++GF23213LrzoIt733vcyefJuHPXvH2H8uAl8+oTPcMKJJ7Jo4ULuu/c+Pv2pT3HS5z5PZ0cHs2bNQinN/PnzaSoUOPbjH+eAgw6iLRP6MwBMjlcXrWK3iXuxw7DB1KoWpaCnFFOtlOjq2siBB+7J9773bXYcPZD7H5gOStGxqZ2FCxew/zsO4JiPfpTf3HknTz/9NJ86/nhaW1q4/vrryec0102bxuOPPcFZZ57FMccczcyZs3nmmWcoFEIf1+cglw/qE23L+rVrWLJkCV1dJZYsXcrUqYfzPxddxG67TeI3d/4OB2iNDwDIeNOT+nc7kyC1Jgw0Tc0tRLFXxCAl7e3taK34zjnnsO+++/DcnDmsXbeeEcOGMu+lF1m2YjVUSsx46EGaWlpZMn8+zz7zDF874wyu+tGP2bipg0f+/AgSyZo1a2hvb+fTn/40u+yyC7+65RZ6Ojq44fobuP22WznmmE/yjv32p60ZhNQkJuaXN9wAwnH1T66iqSnH1df8FFfpolTqYs6cOTQ1NbHXXns1rumfi9FwSCWpRhV22mkshx/+fmY88jBLli8hXwzoKlfRBW81Lza3IuvRDdI5BjW30ZoroHWAEg4b17wrHeCEnyzoMIdUinyQ/6eE/i1cfep/2HqMQ+puunjRIn7y4x/z+JN/oVrr47qTPm8hvVKwWmXatddy9tnf4ZJLLkUq746VGEN3Zyc3XH8dF1xwPvf+8Y/MmDGDK6+8ki99+Uv87OfX0NHZzm2338z3f3Ah3//+RXz1q1/h3HPP4corr+Kyyy7npptu4dXFy3vrmZHxNuLviGp5ba1633X3NOniXxoBvcG4IuxNqSa8W4snbBzdm35N1M+i64KtN85JIaCuNRC9+fjQjRR/f/sq/q8IAnJ4k+HarpjJO47m2E8ezo9+dAF7DTuTxFoSHYIqElWrhKJG4iBEUu2u0O0gqcUIIMkJtIkJbIxLNE4XsCbBJJYwZ0EajFN1O4rAOYVzFiFkfcDYbMojEhAOax2P/XkmLzy/iKOP+gw7jh7ErpNG8dwTz/Jv79qboDXPihefZ93qZXzoA1NReiDv++B+jL99FF/82qmMGdPGJed9l571G+iIE5Ke9azvWMMzc+dx4P570BIK/t/XvskZn/8sACYyEL7+CxplvMlIqoTa0dycwwoIcpI4hpZCQBI1c/aZ32LlsvnMfPoJisUBdHQWIF5PU1jm4Pd8jO+dfzZaa155/mk+/+XzOOwDBzB8eMKdD21k9fJXmP/8I0haaG/vpLucoHMFVq5cy9gxO2BNQhRVfYCxy4FLaB3UQk41M6BV8tnPHMuzT+7CnLkPUGxzVEtNiHqAsVKv/xoJGf8ETjQWaJPO53YytopUkQ/KjWvsOmlXzj33fB57ZhGD2loIJPSUe/jUZz7HEzNns+LlWcxoH8FTLy3h0tMO5O677mTN2g2s27gJV+1h1eIVPPHU80w99GDaWpu46pprmfr+w9h//704/aTP8uqaTdz7h9+x95Td+O75p2LXv5vu2nk4UabatZp7fncTxaH7MvflxRDnmfXoS6z81GryhVbe866p/PznVzBi5Ahw3n1G1hM6eBohsI3IKYlBWgnWpw811iIxaGURiQCX55vf+ibPvnQcd1x7M7I4iWLQg4sjZFszba904OqptxME5aZBrOmsQRRRVjk60eSxPmublJgwj40qSOvt6LHZPBLvbyPxAr6wMs207UewMMDgs8ppaSgEFaINa1ja3g1aoYTAuApJfT2PMK5AAPMWzuW5xWsYNjTgqSce4LHnX+WgPceisTw38wl+fPm17LbXgXzznLMYOXIYz82ezZlf+QqXXXIx7//Aezj5y9/klJNOJyqv40eXXc4rK5u58uff96uQhwG777qzH1ffgLVSMjLeSP5Pgn9fkS+Auubfi/aNE8tcb/5k4XP0eMLGqfsJ/kL4CUS/8/p8y6JviRKC11HwB4WiRGxiZHEwG6o1jjzsYH55bYXvfP1M4qEHEqsQXMjAtlY0FQINiREMaBpAp3NIFHEEJVtFmYiilmghEBTp7tpEEIRYKuhA1Zert42sBc4koOpZFvquhCqoC/6+E7/rt/excNEyrvnZz+mprGfevPm0FHegfPZn+Na53+GWX1zDtdOu5o5bfsetN9/DsLZuRD4iEjVCB7ZUZcKoHSkOGQrViEsu/x/G7HYo1Y7VyKjEDhN2BVtDSYUic5XIAJRlYFuR+fNfoFyLcQjyeY22EFVrfOULX+GF55/m0Pe9mwEDhqH0aEg2MLjN0jJkd5q1haiHyeN2YPi4vWgvKwYMLKObd6C7u4NXFyxm+NAP0NTUwg65MVx48Wd597tGUep8jqZiAZQPepRBCwhH7GJai0Po6l7NlVdcyYN/XMDHPzMJEZbJVQaghNci95RqNLds37SYGf93GtnLdA5Xn+jpQPjVbAWIUPPc7Nl85oQTmLzf4bx7n3G0NRcROmSffQ/iQ/9+FA/+9ldsCEYweOzu7L/3Hsy67zaGDR1I28AhiA3L+eoXvsIuB7wHXdpIGChah4/A5nI0tRZRrsy6siGvBVMmTSTUEbkh4xm24+64oEr3puUMKML4vQ/GxPCxo05gzwOm0jSwRGwcI3YYz+BBO2KND9L1WXD6Km96s9RZURf8XYJMLeBCIFWAS2qApbnQjEkCBg3ahV9c+m1OOeV8OjvztO45DESNpJhjSFJBoNnU0U4xzLPO5VA7jKUgFZ1OkhSaKWqBRuKMINIhplYmqVWwxqJV6J1z/gHLhCJNI11Pwyrq7rhC+bTVgUZTJqdKDBCWKGzCaeUdBSTo+noeylVBKqY/dB/hwFF8/YtHc8YZZ3LvEwt5x55jcSImqXax8tUNHHv8Aey2996EIuHwww7liksu4s57H0bn8oyfPIVBpgq1xcwYvwMbu3dg8n77IIAmKihr0saVkfG2Istj9fciAHIoGVCuViiX29lxzIF8/axz+PnF3+OZxUspFiyIkDFjd+baa6+lqlt5577juefO6xHOMXRwK4GCXL7ImpWr+POMhxnZ0sLAQS1MmjSJcrnMzJkz2bBhA83Nzey00040Nzf3y5kuhM875/rVy1tHnp01h/un38/Xvvo1Pvf5z4GsUatW+coXLuDxPz9Gs5AcfsSRjJ6yFxf+95XefUoX6djUyZxZz1CoTWbczjtzx9q17D5pIpN2OohVq1aBNehcnthYbFRDSEViHTrrMDMAELzvg1M5/Stf4dRTTuHk//xPNmzs4tkn/sJ73nkwz856lpE7DOHkU07lBxdfSLm8EfKD6e529JTWgMpDPiSKIYo30dJUpKdbUC5tYtjIUex5wERefjVmjym7MWzsYJYtrBCEDqlzPiNQInAJ2HIXoIhjS6nUTU93iccee5y99v4Ah7/3vSx5dRmr22v1OnsLRcabH2fxqZC1Rcg0r71lzuxZKGVx1fUsXLiQDRs3ctwnP86Sl56iVK4QxxCEOT7zmc9yzIfezyudczjvv/6L4cOGsdvkKcx4ei5TJu/KlJHv4qVla2hr0YhiniiqEah0MUtLpRrR1lrggAMP5Dd33sl+Hz6crtXr/z975x1nRXU37udMuW0r2yi7wNKWDqIQQI0gYMdYoonGkmhi5Y0hJGqiUd/XoCnGJPZK3mhsUdNNXmPUn0ks2JASo1IsYAGpu3v3tpk58/tj7pmdXcHQlqWc5yPeu/fOnTkzc+ac7/lWHnn0UU44cyRlFVWUlvdgw9q1HPHVs/lwbQ5BG5WV1WSzubCWnG0HdV9MKyjIuDlFsxpSVURbeA18iWFY+DJPNpcjn8tRkoLR+01k/ITx3HrHgxw0YSaWEaeuri//eOEX3HX3HRw4aTKvPvssy5f/m/POO4+Skjj4BtITvPTiS7S0rqe2rpJUIoVlmSxfvpy6BVWk29qYcMAESlOpbbhTShEX1J0Js/REssJRDPSFIDtdu60jErlmmDz/z+f45T2/YurMb3DCySfz2muLeOr3T/Cdsw+l1DIYNHQQR8w8kLvuvp6Fy19j6MBKRg0dxiEHT+GyCSdSXm21R8RZFq5srxzeHqmoJzDNvokW/LcWEdQFNQT0qK2hpMTDMGyOOurzlPrw9atvAm89FGKcdsbZ/PudD5l75VXEjDaqyg1+8rPfUVmeDHySjTivL1rIf51/Hn4mx9fO+TKzZ89GCMGVV15JOp1m8uTJ/PKXv9xsU3zMdk2F76M0RkuXLmfM2DF84ZQvMLhpCHYMnHyB+r59eHn+fFateJt/Pvk37LJqDj5wOrW1MaxYJaNHj2X21y9g+uQp3Hr9T3j5uX9w/jnnEDOz+D784t7fUNeznMbBwyiNxwALx8trwV8TYBjMOOoortywgZ/dcTcnnnAiUgpmTJnKeeedzTnnn8cdt/6cU754CvGEzbRhx0ImTj6XpLpagCvIt2Yoq6jFddZiUA9+NbXVrfiJcr4z92fM/uYdnHrayZRUWnht/bn/gZ8TS/iU9agBYZGyymgoz5J3XWwrSUVFGQ19+vPlM89m7lV38fLi+/noo4+YMfk7ZLN54vGdE7io6XoMA4LbZeG5Dp4Pwpf891VX4TgOgxsrueKKK/nMxMl8c/ZsYrIFhEFpKo4wTfr368/EyZPoX0hy/Odm4vs+p3z1a7z21nt86dTT6G1LEjUNXPHjGxjZdwDJVAnp5mZMguG1R3UtcUty3qxZtGYyzJr1X4wc1MTwkSPp07OK0uo+XPezm/jKrBv53OeOwzd7cM4FlzB+v5nEYjY9epSRTreSSpUBUY1/Z+fydkFYVYVHDfMUc8/nJaVlFdTW1gAgZIKzzzmHx5/8OyVmAq8gmXH4sbzzteVcdumlpEpKEHmLK6/+MeeeeyqmD8lYOQYxvve97yEMh9NO+yJnf/VMystSXHvNXHw8Kit68OAD93PA/gds9X2SRXHCEGYx1Xe7VSNw4iU8bx+wfC84N0HowhtYGeCRR//Ie6tWM2P6RGKJUmZ+7njuOPN/eO2V0zh4whD6DhnB/b97kNtv+1/u//UfePrP99GWdmloqOKzR1/C5VdeREOfZLBH00SKIDNQ+1pLoIV/zb6KFvy3lsj48PkTP4/ZfwJCCCzbZtoJx3Nb7xGMGtML3Bbq+vThqqv+m2M//zZefj21PSw+c+CksGz6EYcfyX33jca2bYy8S//G3tTW1nLllVfy4YcfUigUGDx4cIey6MB/LKAzY8YMxu8/iQEDB2LbNr7MY8dj/Pf/fA+jXGJJyWvTp9PmGcyYdngQ4Oh7zJo1i88ceARlZpKKnrXMnTuXiQtfJRErgO8zdsxYKowc5557Do0jRiCRQWaKgo8eODW4LqlUkq9deCGTpx3OqwsW0ru+gWGDhtBQX8fXvvY1Jo4fw8ZN62ls7IcsGwoJmyOOOIJ+nwEsi3hZGaeddhplw4cDcPhRR9M4sQelcagePYZ5837Ba6/PpzW7jjJ7LPsfMIKCs44LLriAQUPraBrVm6ZhNkIIDj7oIHpWjkcgOOO0M+hVMxbiK6ioqKAydih2LNYhmFCz52BaNkcddRRlPRpJp9O4rkPSznDwZw9h6NChLPzXKkYP6UU6nWZg00DwfRzX4ZJLLqHFrqGhby+Kyc+YNXs2Q8Z+hho/R7K2gQkTxgItfPMbsxk4dgIAo0eOYO7ca2ho6EsPsyffu+IKjlzxRYY1DqKquor1bYt/jAAAIABJREFU6XKglYGDB3HLLbfw8tOPUFE9gCnTjgPa+OIpX6Cl2aeurgxP+uTzBYRhFl1+tt633BQGniwQiyeYNm0ao8emg+KXlsWIkSO57bbbiNU3kIwnMQw49YzTqR82ANd1qS6tY/yUQygmQWLqoVO5/vrrEV41GA4DBjQwpGkI1157LRs3raOldRPJRJKmpqE7/wZuBR9//DFLlixh44Y8t9xyNy/8Oc6bb77JpjVrePaf/+SgiSPJptcTN1Kc8ZUvM3b8wRSyK3n2H3/nxz/6Eb+643amHTaRkz4/NdynMERYxVej2dfRgv9WInGQbhzThBHjx5OPlYH0cTyLmBljwoHjyAH4caTn06tXT2bW90N6eXyZDQOhPQk9qnrx+WOLGR48QAR+lOPGjWP8+PFhZcT20vORTEZSIoWJJIh7CERvE4mgZ696evU2g+qQAqQ0MHyDwUMH4ok8phD079ufQt7AMi2kC9JI0a9fFf36NQV1Flyfsvp6ThpQHxQ98SWGbyJ8g8NnnkCbMPBVIlWhBX8NYFn40gfXo2nIIIYPGxoU//bBdTxKq8qYdMgh4HtBFVIzAQ6kakoZ1SsPBRNMg8Zho8lZOTzy9O49kvKGBPg5fGx61pdyRP2hGKaLky0nHnMRVhnjxvfB87PYsd5UJeN4/kYsw2Tsfv3IZNcTi8WYPuMgCl4jiVgC3ylHConryWJlXh3Yt9vjB9piCXiuh2HHmHLwRCCwBkg/j+9Lho4YyZCmsZiGi0CQwQbhk0ilaBo1howfxyxaSb1sgT69e/GVM07Bch1cwyAvAQ9GjhmLLyBXcEjGUxx61EwyIviuV2MjxwwZHGiwTZPyvInntSGlTVNjb0affyGua2AakM0WqG/oTc86MxinDUEyGcOyVQVZpctvH99Vb5RCIIUPonjunhto0qVHvwEDaegPhZxDIpEiZjvMOOwQ0m4cCwMpobwmxcxjjiGfzWKZZUgLPC+omJMwyzj68OOI2aliUavgIo/bbzSpVALTDOYQy7K2KfuQRFW8L05rFF14/OBZUzp9KUw8AbZ0gl8IL4zf8/FZ/Pq/Wbj4TUaNGUVJ0uSj1esoLath0gG13H/PPZzy+WN5+KF7WfjCq/z8xpuYMHYkVnw4kyZNZP/PjOGU827k1dde5oTjp6ImSd+08KUbRgkKX5Ul9YMbi05Sodl30IL/VmIaRS9EH0QshoEXFD+JBzmLLGEEoa5mHENIDOGDDNK9YZYEnkLF5ES+SGCosd4kTIdqWcHtUJp90zTDUuOqRLpt2+B3rjgSDGLKIKD2o1KDCiMwv6qCa/F4e1C0pLyY96EAJILc2LZJzJS4GAhhYOIhDRsfSCCRqm7DHtJ7nGI6OXU9/WKl2e5AyvaKmOo+qXu8x2K1Z+GyVb/0AxO+Wcz6JCyr2G997LAUOFiiAFYcJSYYOMUq4Ymg3I6I4xLDtEQx25cknghcIWwrARIsEuD7GKbEMEqwreD4Kas8bEvCDFLPCpMwR3hXo2JyfN/H8zwsywrS+xYKCCGw7cBC4XkeUsou75Oe5yGECP9FPzMMo0M/LBQK3faMdEaNdIYwMezi2BXxkgnyxAefG1bRDRKIqT5lBEGy8WKqGRMBqbL2wpGGgSEEcQvwE1iYSKAkZmP4HogYMRH80jCD4lJKXI9ZIM1STBNsKZAYmKaB8CGZLAUEdrzdq1xd3/bnfUuFGTv20cA9CIJB1wwKPyaLPzHAp0CpGbQ7LJIpIR5PhlfQMAUmPvgWsSCfbYdxp6ysfVs1Nm0bsngWRngm0ZozruNhmwIjVkLBgIqETSFXwHVa6VHWg40tG6kor+DR3/2RQUNGc+uttzB23BisQgYMk2f/9iifnXk+L7zwIj42f/rLnxg1+jN869vfozxmEIuneOXVhaTbWqiuLCdhgu8GY49nGFimEWYYNIRVbJ3KkqcFf82+wx4iumk0209UgFETneM43SLYRBdxypKzRwv9mi2iBH8hRIdFfTweBBUrods0zYhgt2tRxw00v+3KhFgs1sHSqNHsEMWFrcCkUCiQduD+++/nrSWv4KTXURIvYcjQoZx+xun8+403GDpsNAMGDgx+awXjdO++DdTU1LBo0WLOPecsXn32H9x+x+2seHs1TcNqWP7Ov/ndHx8hWT6OAw7YHwBhBovCtrY22rK6P2s0oAX/rcZXUVYARZ2QwAgCk4SFgcTCgbBKsV/8z8QXJmZkzJEihtk5k9s2tcXEF0agXym6+3yq96IAQ4IUBlIYGJLAvUhIPOzAeoGDFIkgc6oP+BI/TMbs4BatEjEcXIrZUITcvhPYxShztRJuLMvqNmE7SNMaVB9VC48wW9Meii+CtCXKIgYQUfwHSklRDKTzQeAV63IU9WyRW2GqPwSYRYcBgYEoFvQDuxjwGIl8VO+NPBBdzEXT/cYj73cN0QWesjRFrXnKGqAE7K7uA9H9q2OqNimNv+d5eJ4XLk52J6Khmcanfq40+xGE2XGkEmZ7aKdhtAefisB61Z5iOnDSNIrbAeALPGVd9QnHw5gPHgaGL4qZbIIgXoFDe9WbzuOOQaB1BuW4CeAJgRQgBYHVAb+oSS86zPgq749R/NzClIG2X4rg2FbRaiBNMMLnxwdZbIPpAHYx76YAo5hXczu135Y6hh+LZMkM+pnneRimhSRGWW0DXzj1aJYWypGFLMlUAlwTXzq0tmzigEkHMWPG8ZRXlCJl8ZmWkr59RzJnzrdZv/5DBg5q5ObbruPuu+5l/ssLWfP8G8Tjcf7rgov57OGncshB47B88H2B57lMmHwgPcprOgX3qvZp33/NvoUW/LeSQNgO3kssDFx8IcKcBYLgYnoiMrzLYBxtNyR64e9Nf/slf2VK9Tscvf3bdtTQ66l1CFK5lQoJOEhsgrNwgl+KoqVcevjFvMr4bjg14XvhddhTPPyVv6oKllYCTndoWU3TxDCMsB25XI5YLLadpvXdAw/CwMGgE7X3QeVe1p62j8D+Hi687A6LBKMoREkRiDRCimAhakp8TCRm6JMsOk3YwTK0feHQ8dmyow2ItLHrFoBqwRnV6EfdzqILg12x8NvcMaJuP1LK8DnZ7fBpV0QU/w7fdv68eJqiwwjVeZRsd79R79WioeNVUi5onX/Tjqf6lgTPNCIipREscnEJZgDVzi31OT/00wmy+vjF4wQSfVBlN3oGSvFiBguCoqt60Lvt4mAfCP7td9QD3+74PnwcVNzB9lLM4x86WfnhPTAsCwm4xBgw6gBuuO9RCkYSQQEDB9MvARGk2p0w6RB11sQMHy/vYdoWsWQ5F186K2ihhL79B/E/11wNQiBJIAsS6fjYCTNIm1qcJy0rzmlf+jJerjfGJ2onQMe7qdHs/ez+6lqNZgdRQr7jOORyOQzD6DbhxnGcUANsGAapbcqTrdmTiGrSC4UCELid2baN4zjhwkAIQS6X2yXtidYEUf/S6TTQ0Q1OxaFoNDsFAdIHx3eQ0oNCfoubul5gOchmg7+DopUC8nly+XYLAsXYGYVpGdi2iTAgHlcKgKC/u6HFQ6PR7Iaqnd2V9jWShKDiIe1BXkbRIaGDFtzv8BK+85VaZrtpL+4SffU7fELYkvZSKn67QtYPUlj46gMhO+xL+H77nnzZ8X2oofV3SD+0q7Asi8cff5y///3vbNy4kaFDh3LMMcfQ1NS0y9uiMja9/PLLvPbaa4wfP54RI0bsnlrWrWSzekLfDyt3qt4XeuXggygUt7ORxaxWPgKzWOdbBrr9dku86ePT3ifDvYoCvh/48boIYlFXo/B99KlU2lfYwYfwP6IEaRW4WygUWLhwIffeey+LFy+mqqqKL3zhC0yZMoVevXp1aVs2h+/7LFq0iHvvvZfRo0dz5JFHUl9fH6QC3g39+7fUos6fd+wjRa03dND2+rSPooFFSo2PRrvbmtqXAIFsd7PpcEwZcTWSRWts+6wQvLbnuNlMI7d4ZuGYrgTfT5ybbK+OW3xWfF/9rn2O8cPjF/cQHk62/+2L8Cptvym3KIT7HiLqSuV5+JZdvJZ24OYXs8LZySlm95Hq90YMT0IiCZ50MM3S4POkIGZJpBTETBOcEoyiu2negbihxphNeI6FaQW/y3sOCLCj17p9QmNL11+j2VvpEmkjl8uRSCQAeOKJJ3j33Xfp168f06dPD03ehmGQzWZJJpNdOsnkcjni8TiGYZDJZHjjjTc44IADPhHc+Z9cPz5pGjEwOnwe/GVF3hMLRAwT9b/g2xh0dEXeRgxymL4XmDJ9v+hPKT/RonYsKJp7rfDYcSBOQgCkgFTHzmAmCL18rXJCvbRRSrJ9o+0/iS6is7+07/v8/e9/59RTT6VQKFAoFJBSctttt3HHHXdw6KGHkslkSKVSYZ9QmlHHcbAsi2QyGfpkq337vo9pmtvcd2OxQLB95513OO+88+jbty9nnXUWs2bNoq6uDt/3yefzYfYXtf3uTIcWBo7SKPeIjn7V6k2iw2eb67GhR3b4AMU7bRf25jCbVYxYR4FFfOIN7UNe1y9Zoxm5CoUCf/3rX7nooot47733iMfj5PN5/vCHPzBr1iyuv/76Le5H9bN8Pk88Hsf3fZLJ5Ba18lvqk9HMPSpdcGtrK3fccQe5XI7PfvazzJkzh4MPPpjq6mqEELiuG1ouum0xUJQgO4xPkc7Q+XOxmY2Mzf4tOr0XkcUpdHxrtP9aRPq8ZVCi3ttGMDYa7f0/2HIzz3CH7md94nPLd4N/gC/sMB1mexuCB0MQGYXj0aei/bB2h3E6GZl7Sum4g+Qn1ifbRjGLlqHaUHx47fbHOA5B3IRQGXbi2Ea8+LNglkkRaYNR1n7SVmlwSurvePtVTqpzMgEqMSOefXGzX7h9x7gfCC7G7pG9SqPZVXSJ4J9IJFi9ejXz5s3j/vvv591336WxsZGzzjqLiy++GAj8rpPJQITsSv9W27YxDAPHcVi8eDE//elPOf744znxxBM7tKO7smpodi5Syg73Mp1Oc9NNN9HW1tbBv3rFihU88sgjjBw5krq6OqA9+49aBCrNpwp2VBrcbSmq1pl8Pk8sFqO0tJRYLMaqVau4/vrrWbFiBUcccQQTJ06kurqaQqFAeXn5Dl0LTfeiFojKzeexxx7jvffeA9r7ge/7PP3007zyyiv069dvs/tR26nA8NbWVtra2qivr9/s9lsj+Ks0omvWrAmtTc899xzLli1j2rRpfP3rX2fSpEmhz38ymdyjrVIajUajCeiykfzhhx/mBz/4AdlsFiklK1as4Prrr6euro5TTz011Cap3OpdhZrgbNumqqqKZ555hqeeeorrr7+eL37xixx11FH079+f0tLS0A1Ds+eiBH8lvDc3N/PGG2908AVVGv0HHniAVatWhdvHYrGwT0IgQFmWxcqVK9mwYQPPPPMMyWQSz/PCgMxtzcFv2zbpdJqNGzdSKBRIpVJkMhkeeOABHn/8ccaMGcPw4cMpKSnptgBkzc5BjSdSSlpaWli5ciXQrsFX8R7Lli3j0ksvpbS0dLP7UfsQQlAoFGhubsa2bX7xi19sU3vUQkT1V8/zaG5uJpfLUVJSQj6fZ/Xq1Tz00EOsXLmSyy+/nMMPP5x8Pk9ra2u4cNZoNBrNnkuXCP7ZbJbHH3+ctra2DhrRNWvWcMMNNzB//vxQgAJ2SSCZEIJMJkMmk8HzPBYsWMDChQu54YYbOPjgg/nWt77F+PHju7wdmq4l6ldtmibJZDK0+qgAX+XGM2DAAI499lhqamqIx+PkcjlM0wwrVioLwCuvvMK7777LzJkzSSaTuK673YK/lJJ0Os3ixYuZP38+mUwG27ZJJpNMmjSJyZMnk8lkcBwndJfT7JmofqHcGQcNGhR+J6UkkUiQy+UYOXIkt9566xY1/o7jEIvFQpebp59+GtM0OfDAAze7/adZoaK1BTzPY/78+Zx88sm0tbUB0NjYyDHHHMMZZ5zB+PHjQ7egeDyuF6EajUazF9Blrj7KN1kJUcrcbZom5eXloTBWKBS6dELxPC9cZKhsFblcDsuyME2Tnj17UldXRyqV6l4/Vs1OoXNlzLKyMr7zne/wve99j1WrVoXuO01NTcyePZsvfvGLod+08ntW/SBaLdkwDI455phwWyAU/LfV3UcIQSKRwLZt+vXrx4wZMzj55JMZNmwYnufxv//7v7iuzkKxpxNN0xmPx7nwwgtJp9P89a9/Zc2aNdi2zfjx4zn77LNpamraYu58tbhUi1khBJWVlVRUVGx2+60R/NV+U6kUPXr0YODAgZxxxhmccMIJ9OvXL0xBqoqMFQqFDlYzjUaj0eyZdIngL4TgsMMO47HHHutgHq6qqmLOnDmceuqpu7RyqnKZeO+99/jTn/7EBx98wKBBgzjvvPO48MILsW0bz/O2S4jT7D5E+5RpmmH+/jPPPJO+ffvyox/9iJaWFhoaGpg7dy5NTU0UCoVQA6q2V4KWEnqADoLP5gKItxa1sKirq2POnDmccsopjBw5Mmz3smXLQmuDCubU7Nko97GhQ4dyzz338PTTT/P4448zZMgQTjnlFGKxWIfg8c6ovqy+79w3O7Olz6P9VPX5xsZG7r77biZNmkRlZSWO44TxB9HjR2NcNBqNRrPn0mU+/ocddhjHHHMMTzzxRKhhnzFjBocddlhw4F0UKKaqpAKsXbsWy7K48MILOeWUU9hvv/2Ix+Nhur2of7dmz6PzQjLaxw455BDGjh0bZvCpqqoC6CDgRLdX2lUpZehmEdXCR4X/bVksqgqukydPZvLkyR2+UzEFKr961NVHStlB86vaq/q3YRihwOa6bngtduUCW9MR1S9Uv1Kv06ZNY9q0adu0L3WPoxV3o1apbDYbKjCUhUr1E1WYq3NfNQyDxsZGGhsbw89UnQF1DLXo+E+L20KhEPZd5ZKkiNbRUPENepzdc8nn8+Tz7Xn4XdclFot9Yqzpzork6tiqTap2iup/6r1S9mg0+xJd0uOllAwePJjbbruNl19+mffff59evXpx0EEHUVNTE1ZOVQ9kVw4O0fiBoUOH8sADDzB27Fgsy+rgttGdg5Sma1GDfFVV1VZnyokKLqqPJBKJMMWm7/uh0L0tMSpbEnhUsKdaGLiuG+5XCUqdNa6e54XClpQy7M/quYouJHTf3vuIZumJx+Nh8K76p7bZHqL76LzPzRGLxTosllU/VgsWoEPQvWbPpUePHqHCTGU/g/axTVnO1fjVHZaizv0/qgBU32lFn2ZfpUsEf5U+s7a2luOOOw4gFPbV9yq4TAknXUnU33vSpEk4jhMGcyYSiVAo2p687JrdH6UpVQL0fxKCO2tFo2kUq6urgUDDuT1uOFvqX0IIYrEYlmURi8WIxWKbnTCVMKWE+XfffZfKykoqKyvD75VmV52HFrb2TqIaVs/zQjcyda/VQiCfz3cY57aFaH/9tLFRCVG5XC6MHVCocVZZpbQFas8mnU6Hc7giqk2PjlGdt9tVKKVINEObQj030YWKdmPT7Et0icQd9RGNPlSdtahCiC4XSJR2VkpJNpvt4D4RnYCiRcc0ex9qkRkVjLbldwAlJSWk02mef/550uk0xx57bKhR31o+bRJ0HAfHccjlcqFlQS2WTdMMMwApjf5vfvMb/vKXv1BbW8vRRx/NxIkTSSaTYWxANGe7Zu/Dtu0whirqohONDdlRa8/W1qywLKtDJqrW1lYymQw9e/bsMK4mk8k9pjCdZvMIIcKYDyllKEhHBeoVK1bgui719fXdEqekFh4vvPACb7/9NlJKpk6dSr9+/cKxEdoVKRrNvkSXCP7xeDzUMqogNLUCV9VQlSmwq32Q1SIkapJUr9EHXmuh9l5UX9xaAUgtFKNac9d1+dWvfsXvf/97XnnlFU488UROPPHEbdamf9oiwTAMbNsmkUiEC4qoX7fqx2vWrGHu3Lk88MADtLa2IqXkwQcf5IgjjuC6666jtLQ0TFsadbXQ7F0oLbvrunz00Uc8+eSTPPfcc1xxxRXU19eHC7/tUWhEq1SrPvRp/Uj10RdeeIHHH3+c5557DtM0qa2t5bTTTuOoo44Kx35tfdqzUdYlZWFSCopCocCyZcv47W9/y69//WuOO+445s6d2y1W9NbWVm666SZuu+02Pv74Y2zbZsiQIVxzzTUcfPDBujiiZp+mSwR/JQwpX2XLskItZVTAVinjupLo8VSApgrkjWpDo8GRmr2XrfU7VW4SH3zwAY899hj33Xcfzc3NYYDvhg0bWLp06U5LcRjd77vvvstbb72FECLU4KvFs5SSv/71r9xzzz3k8/lw4fz+++/z4IMPcvrppzN16tQOml7t4793YlkWH3/8MfPmzeO3v/0tr776KiNGjCCXywEdF5nb4+4VHRv/k+AvpWTt2rXMnj2bl156KQw8B1i0aBGDBw9myJAhHSxomj0T27bDBaGaU9944w3mzp3LggULWL58OYlEgkQiQUtLS7fc7xdeeIFrr702DELO5XIsXryY2bNnc9tttzFt2rRQsaMtUJp9jS4R/KM+plFtU3cIH9HJKrrQ6BxXoMyXelLa+4gKPFuj/VaxAPl8nrlz5zJv3rxwsaj28be//Y1FixbttBiVWCzGxo0bSSaTrF69mvnz54dZWpQLRUtLC4lEgvXr15PL5UKhXy0+stksq1evBvhE/Ipmz0ZpVyEYuzZt2sQ777zDVVddxSOPPBIK+0IINmzYENZPUfd/Wy2aSqCTUrJmzRpaWlp4//33w3osncnn8yxatIjFixeH7VW8/fbbvPrqqwwZMkS7nu0FdI7heOyxx7jyyitZsmQJEIyPuVyOX//616xdu5ZMJrPL27h8+fIOx1WuSCtXrmTBggVMnz4dKWWYkUij2ZfQeaw0mk5EA9NOP/10Wltbee6551i/fj2ZTAYpJePGjePrX/862Wx2px5zzZo1zJ8/n5kzZ4aZfJSVSgXIP/vss9x7772htkoJhPF4nLq6OqBdKwda47+no9INK1eZQqHA008/zQ9+8AMWLVpELpcjFotRKBRYs2YN3/zmN0kmk0B7AcVYLNYhBePWoBa16XSaFStW8Je//GWLC+fy8nL+9a9/hQuQaKVsx3F46623aG5upqysLHT71OyZKGWE53lks1lisRhNTU18+OGHbNy4MVS01NTUkEgktlhoritRAciqDyq3OM/zwj6oXCt1cK9mX0OPvhpNJ1SGCtM0OeSQQwDo1asX/fr14+677+bf//43vXv35vjjj/+PaQ63FpXe9q233qKlpYUTTjgh1ESpSalQKGDbNgceeCDr16/n0UcfJR6PhwuDz33uc4wdOzbc3+aC6jV7HiqIUmnt4/E4hx12GN///ve57LLLmDdvHo7jEIvFqKio4Oabb6auri7sF9vTP9Vi0XVdli5dypIlS5g6dSo9e/bc7Pau67JkyRK+9KUv0dLSQjweJ5PJYBgGZWVlNDU1hX7VWujfs1FxJereHn300UyfPp3XX3+dBx98kDvuuIPW1laOPfZY5syZ0y1tXLZsGW+//XYHtzMpJSeddBJTpkzpEPMVHSs1mn0BPQJrNJ1Qk0C0+EuPHj0455xzOOKII1iwYEGH3OY7Y9JQwlnUdUf5VSuNlNLiNzY2cuONNzJq1CheffVVqqqqmDJlCjNnzqRHjx5hwSbN3kN0gdnW1kZ1dTXl5eVcf/31nH322Tz77LM8/PDDtLa2UlpaSp8+fTpoMrfH6qN+09zczLJly+jTp88WBX8pJaWlpXz1q1/l97//PStXrkQIQWVlJSeddBJHHnlkmNBBa/z3bJS7lrqfhmFgWRb7778/TU1NnHjiidx5550kk8kwScKuprGxkauvvpp58+bxxhtvkEgk2H///Zk1axbDhw8P+6BS8mg0+xJ69NVotoByv8lkMmQyGYQQjBgxghEjRlAoFEJhbFsKeG0JpV1V7jvqsyiqOJKqkXHFFVfQ1taGbdsd0ueqbTV7D6ZphkGIJSUlYYpE0zTZf//92X///TnhhBN4++23w6rU0LGuw7Zq/lX/U8J6Npv91H2UlZXx05/+lC996Us8//zzeJ7H8OHDmTBhAj169KCtrS3M56/Zc1FjnhL6c7lcaHlMpVJMnjyZSZMmkc1mu+1eCyE44ogjmDp1KqtWrSIej1NbW0sikQifCTVWajdIzb6GFvw1mk6oiUFp823bDvNWq6DZWCxGW1sbJSUlO+246pjR9KPqvdKcReMPpJSUlJSE2X46Z17R5ej3DpSgpRZ3bW1tlJWVhd+rvP29e/emb9++uK5LNpvtkDY5Fottk4AT7XeFQgHTNEmlUlvchzpOLpdj/PjxjB49mkKhELaztbWVsrIy7U+9FxCtLaJeo1ZGtUCNx+PdljFHxaaUlJQwePDgDu1WxRd1Ig/NvoqWCjSaTkSFJWgv9+77fmi+3t786FtDVFhXk2vn7FTRz7ak3ddC/95DVOCOx+NhoK5lWeF9jr7u6L1X6ZhVnnbXdf9j6trocePxeIfCTWoBoIX+PZ9ohd7N1QpR4+auSNf9aZSWlnb4W7VF9Uut6dfsq+hRWKPRaDQajUaj2QfQgr9Go9FoNBqNRrMPoAV/jUaj0Wg0Go1mH0AL/hqNRqPRaDQazT6AFvw1Go1Go9FoNJp9AC34d8LzvDB7RTStY3eijq8yKKg27ewiUrsj0WwiKu9y53PfWdVzFdF9+b6Pbdu4rrvT9r85PM8Lswmp0vLd2e/UsaPPgspm5Pt+WHPgP2V62ZPp3Lccx+nWdriuG1Yg3RXHVK+7siBctMiXyrrS3ePvrkY9U6q2R3fSeV7xfR/LssL+GL1Pmt0L9RxFx7BtHa8/ba6NjkVhPRuf9n94gAMUiv+c4LPoNj7gO0A2eC+L/8i1/yEp/rZ7xt8toWpZAFuV9ayr8TwPx3HC+/5p84TO99eJaEq6aM707hiAlYD7jtCFAAAgAElEQVSvBEEpJfl8PkxLpvK0q4G3uyeJnY0QokM6uEQi0SG/eFcRFXLUxBZNTdgVqLz8qr+p43ZXv1N1A1SbVMEy6Djgmaa51/U7aK+anM/nMQyDTCZDSUlJt5yr6o/qeVfpFLsS1fdM0wyvg+M4XX7+hmGEaSBjsVj4HHT3pLqrUOeuxvWokL2riSoi1KJfzUOJRALbtsNt97XF2Z5AdNGm5s2dNV6ruVn1B1UkMNy1kCA8IqsAQATvfRH8QwQfCT/YXkrwVZsl+MVnXhrt7/3da5GpZK9o3Z3ueBZc1yWRSOA4Tjg/5HK5LdYZ0oJ/J5SQE9VuKuG7u1APmRAC27bDHPNRAbW729iVRKssquvQlecatfQIIUKhpytRRWXU+UUXfd2BaoNqTzwex/O8UCBW1YXVhLK3oRY3arJU16A7znVzWtVdMblEC4clEolw0dHVx4SgKJkSLLv6ed/d8H3/E4us7jp/ZWGG4D7EYrFwDIgWCPQ8T9cN2c1QMkw+nw/vjeM4O+U+KYVQtF8G42XxD2EAJp8U/AWho4kf2VZYIET7Z4YFvhX5mWrz7jUOROtZKLrjWbUsC8/zwtpCnueRSqW2vP2uatiegpp4crkcH374IXV1dd1qclXuPYVCAcuyaGtro62tjQ8++CCs2hq1UOxtZDIZEokELS0tpNNpli9fTkVFxWYfrp0lDHmeh+u62LaNEIJ0Ot2hUmpXoO6d53nkcjk2bty4SzSsW2qLmiDU5JHNZjEMg3Q6zbp168hms6EVZG/sdxCMAZZlsWHDBoQQvPXWWx20nLuK6PUtFAphFdyuREoZnms+nyeTybBhw4YuP3+1kHQch02bNrF27dpwDNwXEEKQyWQQQtDS0sKGDRtwHKdbz9+yLBzHCefBtWvXUlNTs9lCg5rdh5aWFuLxOGvXriWZTALBvdzWOWVzrlyVlZVs2LABaHcfCqxTEY09EvADjT60a/kxaF8EALggXPDtdmHfyLW/9+3gbwB/9+lnqrCnaZqUlpZSKBQ6WMZ3JRUVFaxduza0FNbW1oYyzObQgn8nXNfFcRxWrlzJvffeGz4o3VF2XCGEoFAokEgkyOfzLF++nBUrVtCrV68OE8Ku8P3d1ViWRS6XI5PJ8M4777B06VJ69uy52XPdWQ9c9J77vs/y5cu7fGJTD6jjOLz77rvcc889Hdy6diVRwV+ZDgHWrFnD+vXrWbhwIel0Oox92Bsnfdu2Q2Enn8+zZMkSVq1aRWNjY7e0RQ3ihUKBTZs2ccghh3TpMdW4IqWkubmZ9957j0cffbRLjwmEVoU1a9awceNGFi9eHLZjX0C5/K1du5alS5duddXkrsS27dDlTUpJLpejpaWFQqEQzota47/7ceONN4Za33Q6HS6gt7UvbU7wt22blStXUl1dHd73wCVMzcGdX8O9dXofdfY3It/LyHuj+PfuhWEY2LYdKirKy8tZt25dt8S85PN54vE4zc3NJJNJrrjiinCxtzn0k9oJy7I49NBDGTZsWAc/tu7G87zQzce2bbLZbOiGoMxue1uQle/7xONxMpkMyWSSdDodPmCbE/J3luCv3GyUq1ehUKCioqKD60H0dWegtAdNTU1897vfpaqqCiFEtwSUKl9F5Q+qtBpRIURN9Ds7sHp3xLKsUJujTKm7EtUfLcsin88jpWTw4MFdekzl5iOEYOLEiTQ0NITjT1eihBPLsti4cSPJZDJ8DvcFlHuZZVmhpVfdh+5CjQOqDVJKysvLsW07nHf2FYvMnkRzczPTp09n8ODBZLNZYPtcMzfX9+bPn09zczMnn3wyFRUVoWuo6qvR5zU6V6htlHJJzS3trtWBwB89ZlS2icYTdHbJVduq180Fpm8uYUDnfWwtKtaltbWVv/zlLxx33HHdFg/q+z6xWIwHHniAv/3tb6Hr8JaeSy34d8IwDBKJBAMGDOjupmh2U9QAsTMnu6i71pgxY3bafjWa7SHqV59IJBg0aNAub0Pv3r13+TE124cW/Hc/hBD079+fnj17AuxU5aCygg8fPrxT4guVAMInm82GwaW+T9FqGShRbFslJRHk8wXi8VhR2aCSd4BhRLX/RmhhUlZmdS7RzHPRz6PZEDsTXUxvr+AfjQFMpVI0NDR023OgFk59+/alUCiEcThbQj+tGo1Go9FoNJodRBKk8fQpKSkhl8uTzRQQgF1UbsViNtL3AIn0HeIxC4oLA9OEgpPFl4F7s+u6SN/Bc31EMcBX+bFHtfYq+5iySEeF/s1l2olaBqKv+wpa46/RaDQajUaj2SGk72EIgxUrlvPhh2tIJlLU1NTQu3c9sbhZdAvzQ5ceX/r89cknGTRgCP0HNuB6DjHbxpdgmDaBn7+HYQqEbHcTUq9AB/choINLkfo+isqStq8K/aA1/hqNRqPRaDSanYLPXXfdxQXnX8hF35jN9OmHcdddd9HSkg7ddywzSPVpmgY/++nPeOCBh7FMEx+Jj4cQgcCey+WK1oEgzb/neR0y5yhtfjRo2TTNMBZK+dtHYwRU0gblDrMv1QlRaMFfo9FoNBqNRrNDCBFk6HFch+nTp3P+eefTp08Dt956O2s//hjP9cnmsmSyGQpOAel7XHLppXzxi6fguAXyuRwCQVs6ENiTyQSFQp7m5jYsK8gmpLLtua7Lpk2bQs2+Sje9YcMGMpkMpmmGKblbW1tJp9NhDKfyzS8UCuRyuW5J09ydaFcfjUaj0Wg0Gs0OUgAEcRx6jzmQU848mdymVcz5/p2k0208dv+d3PLw/+FLyfCmJv7722fy5ptvYpT2Z+NLT/Cti39KeWk58WQbMbcH4/Yfx+trlvPRqwuo71PPZb/4Nb3kG1z+3Zt4eWkrverjfOcbX6M25vHDu/8fH7y/FM9todIv4fs//iE9+/fliisv4eOPWnBdlznfPJexo4Zzzz338uRT87FKLdKtaS7/7+v47ITR3X3xdhla46/RaDQajUaj2SEEHuCSsn2WvL+Ovzz+BA/+4gZilXXESlOseeNVnnl+AZMOOoxLLp1LVW2Sle+9x8aCTeumt1m65CNGDJnIZZefy8LnX+HW627k6M+fykVnnsQb85/iheUrqYytZvHiV5l0+Glc+K1v0a9XBVfNOYMnnlnID6+7kWt/dA1mW4Ebrvs57733Pq8teplbbr6T++77NaOGDuC2G67jjpuv59vfmMNd/3sX9f378Os/PNndl26XojX+Go1Go9FoNJodQjoWhm2Tybdy31238vwjSUrTkosvO4++/WpJlKbYb/TBnPT5k+jdJwE5iYVLwvLxjTiDRozmK+fNYtCwNIcefjjLly3nwAPGUd5D0tjQi5h0wUrS0K8fp512MqMGgffxm7y1bB0Tpo9j+LA+WKInJ558Mr95/M9UlJWyaWMz993/K6Ydeihjx/RhU3MLH3ywjocf+TWJf/yZl19+hf5Nqe6+dLsULfhrNBqNRqPRaHYIw0qQTbdhWnlOOfssTj98EqPqa6kaOYpYZh2OlPRrGEBNdTWu62P6PrZlIt0cwg8WDSXlpRS8NJYdI1laSmlpCY7jMHDgQKQ0kBtaGTBwEHU1QdWAXMGhtNyid89a2toKlJUIhGWSSCXo37+e63/yU+6+/SHmzbuLqy6fhY9B//71DBjQSLy2jG9cNJt+TRO6+9LtUrSrj0aj0Wg0Go1mB7FIllRgxmDwwIEcOv1Q+g8ZhcBHxGKYsThuQeI6HpYlwLJpbdmEdPNIDHKOQ2tbBt+wseJxmtOtGNKnvLyMdGsLJbbAsJJs2LgJywgE2HgiSVVNT37/u0dZvvw9nnnmGW646SYaBwzgxZdepmddT+644zZGjRrJ6/9+g6qqat5c+gGjR4/ijDPOZL/99qe0tLK7L9wuRWv8NRqNRqPRaDQ7hu/j+z6VlfWU9awGH6STx4zFkJ4kUV5Nv74msVgxl79I0ji4iZqqMnJ+BcOGNFBW6oHjU9uziuEjmvClh2uX0G/YWEpNi5xdRf8Bg0AGGYSSldV870c/56Zfvsil355DKikZvt8YzrnwXD5Y8z5X/8+PMYw4tmVz1MzjaGrsR23PPtx864384uGH2LBhA+fP/g6M7NvdV2+XIfx9sXqBRqPRaDQaDVsu4pTNZrFtmxdffJEXXniBuro6DjzwQIYMGYKUMswhL6XENM0ub6fneaxbt47FixczYcIEKisrO3wnhAj/ffvb32b27Nk0NDSE59i5mNX28tRTT/HUU09x1VVXEY/HI98EaTjxfbLCxvJdbFwckcCQDqbv4JkpBBT/ZXGwcLFJ0gqyLPi90Qpe8N41wfIcAPKmTdxfh0sKV6QwkRh+DpM8kh6BZUH4IA3AA+GBiCGLrTMA/GIbhUQSA4JcRIltvAbqeubzeX75y19yzjnnhEXEdjWqgNlDDz3E7bffzp/+9CfKysq2uL3W+Gs0Go1Go9F0IpVKcd9993HzzTezcOFCqqurGT16NLfccguDBg0il8sRj8cxDKPLK8CqarQvvfQSt9xyC+PGjeP888+nrq6OZDIZFqVSCxKNZktowV+j0Wg0Go2mEwsWLODqq69m+fLlmKbJhx9+yPr167nmmmu48cYbMQwjLATV1UWgPM/DMAxSqRSrV6/m5z//OY8//jhjx47l5JNPZtKkSVRXVwN0m+Y5xLexBRQL9WICQpggDAwc8A0EJo5I4qOCTW3aLQYCRLB4MRBgBNc27gOUIzAxAROJEBZgYPig7AjSAOFLBC74sUDzD/iYiFDszQbRwbS3c19BC/4ajUaj0Wg0nXjiiSd45513sCwLxwncTTzP44knnuArX/kKtm1jmmb43c5ypdkclZWVbNiwgQ0bNvDWW2/hOA4LFy5k4cKFPPzww0yZMoUzzzyTE044ofsq0fpFkdKQWBjFvy0M4YIQBGK8CUISCPkWBoG47pNAGF5xP8ni9hQF+iISpBkDJAYeRrhssIIvEeALXAEmPiYSfPCLgj+YtN8hL3y/j8n9WvDXaDQajUaj6Yzy3bdtOxTubdtm06ZNvP322ySTSRzHwfd9TNPsUhcbx3GwrEBky+VyJJNJpJS4rks2m2XVqlWsWbMGx3G6T/DX7BFowV+j0Wg0Go2mEzNmzOBnP/sZmzZtwrIsXNelUChw/PHHc+ONN1JSUoLv+2FgbVe62KjFx/PPP89ll13G4sWLqa+vZ+DAgZx11llMmTKFPn36YNs2nuf95x12BcV1jy/SCFmOWgcJsxWfOD4JDE+AcMAoYFECSAQ+LjaBU1Cg7FdXUkgZ2b+BBxhFjT8IfAx8ii5EGAQOQBStAbLYJj/avPbGqg/2MZW/Fvw1Go1Go9Hss2zJReczn/kM559/PjfddBPNzc1UVVVRX1/PJZdcQp8+fYD2jCq7Etu2Oemkk/jud7/LuHHjPvF9t/n4Fw8rKAcjKk/3CDP5BLJ9HIgXvw9+tEVhNHouRhAJEGxtFY9V3K9ot3IkAERwDEwwiWQeChtVhijuel+zj2jBX6PRaDQajaYTjuMwa9Ysxo0bx9/+9jeGDh3K1KlTGTJkCBD4+5umiZQSKWXoitMVSCnJ5/MMHz6c7373uwwePJgxY8Z02fE0ey9a8NdoNBqNRqPphG3b1NTUMHPmTI466igsy0IIEQb0Ks26EKJLhX4ItPimadLQ0EBDQ0MYcKz9+TXbihb8NRqNRqPRaDpRKBSIxYIiT7lcLhS+pZShwK20/Z7ndSpmtXPJ5/Ph/n3f79AGjWZb6OZkrxqNRqPRaDS7H5Zl4Xkevu+HhbqklAgh8H0/zOJjWVaXa/xjsVgo8Ktj5/P5Lj2mZu9EC/4ajUaj0Wg0nVAa/mjwr6rSK4RACIHrBkWndkXlXtd18X2fQqGAYRihNUKj2Ra0q49Go9FoNBrNp9BZ+Fco4burNf5A6NpjmmaXH0uz96I1/hqNRqPRaDQazT6AFvw1Go1Go9FoNJp9AC34azQajUaj0Wg0+wBa8NdoNBqNRqPRaPYBtOCv0Wg0Go1Go9HsA2jBX6PRaDQajUaj2QfQ6Tw1Go1Go9HsEDLvYNg26/081fkP+eDNZkRZE7VDUrR6bZimTYV0aN2Y4Z2l71HbtD91/mrWL3qZBWWDqLA+RkofW5ZhmiaFQoEBI8eQX7SIqh49cIcPJ29CmbcRsWIh/2quor5vb3r1qqPNz5DwW0j/63n+b8N+HDG5GrlqAS3JcTTWV9KSbiNWWoLFBkxMlr74JsMnTsDLtWDbAj8nWbV2Hf988UXkpjy9B9cz8dCJSKMHdsEhZdl89P5i1qyJ4zgOrmilJFVDMpWif0MduaRNpgC9bJc1S17lw7TPRpkiYbaB4ROL2eTdEmKJJKNHD0AAhu9iCx+kDcVMoZ4AnahT09VowV+j0Wg0Gs0OYVgW0pUkYjb+h+/wrTlX8F5LKZdf+y2mHjaNgpRg2Pzlz//HD66+lpsf+j+aCx8w5+zzWd5nFL1K2gAwvUC4Nk2Tc79xKc/Ou4lly5byo6deoKlvHe7atVxx8bf57YKNnH3ueVxy+aUkDIvM6vWc/19zeDY9mgkPX8+13/g6i1oGc+utP2a/0U3IApixDEuXvsnVP76L71z7A0YPrOb9txZx+71/5tHf/pa63r2oKa9k5Ufv4RiSI48/m1lfPYOGHj34xZ23ctOd/2DQkCYSiVaybR7pdJr62h584aKLOeao6eQKDjffeju/eeolqvsPw/I2IAzwfI9NrYJjjj2OkaPmYAkwhAC6tuiXRrM5tOCv0Wg0Go1mh3AcgR0TGHiIuE9z20bmv7aY71z8MQ/99lF6D+6PD9ixGK0tLazbuIE+NYI1a1dz/vdv49RjJmKaJl7WI5FIsG7dOmr7DmLNP/7E3558nCXLVjG8TxUb1q7j2X8sYG1biqeeeZZvXH4pSXw++Gg1y955n7FTv0BdZQw/n2HhgoV889Lvcd1113HwsP4I8pTHJWtbspT0qOf9d5Zwyblf5pHn3ue6n/yYCy84j1QqwcKFL/PDH8/lx1ddTG1pKd/6r3MRvkOisjdXfP/HjB9TRba1wPvvv8/Vl1/ChRecz0033cjpxx3GxxuaGTZsOD+58z5KjGY836OkNEVrxkL6gWrfcV1MS3z6BdVougjt46/RaDQajWaHkAa4PsR9F8wsWX8Tg0f25+P3P+Su2+4ijkXOM8jkJJZIYqdKSRcyZCRUVlZQV92b8pI66uqqqSwvY8jgQSTiBlOnHYAdN/jX8tWkvBaWvPwSifIy5l55BRub21i0dD2m77H4X2/zUbaEM048hpjh05rJU1lby4Lnn+XSb8/h49UbwXexTJN8wUEYJm+8tZwXX3qXk867mLPP/y/iZoK0l2PEmNHc+IPr+MpZh/G73/2e5jx4hVba7N5UDWyiorKS+p7VTJ40nrlXfQeRb+PeeXexKZ0lTwK7rIrqngl69uxJbU0NpSVl1NaUUldXgpQQsyykr7X9mu5Ba/w1Go1Go9HsEEYMHA+SCDAs7JISLrvy+2SXvs2Pb7qJQ2dM5cijjyQWr8AyKxExG8d3qaiCBx/6Ax8sfgMpJZb4GN83qKmp5cgTTmfsASM4+tjJfPDv18GfyF///jL1BxzNrHPO4u8vLWT+U08xbfAx/HvZaqqGHMz44QNx8GmJJTn5uC/QUN3MVVddwwN/OIavX/gF8pZPheFQZq3h7XdXUtnQhzMv/BZ2zMYEfNPC8fLU9enL+Wcfx4mn3slH6yGVNHHiNbR4YFlW0S1fMnrMcMaMHc3SN19nzbqNGIkS5r/4Gpdc/nN62huwYhaJZJxsIclhhx/O6LHD8HwfW7v6aLoJLfhrNBqNRqPZIaQPnuvjmx4inyefy1NSkuKsb1zELXfdxaOP/oamkaMpFBzyuRzS9zGEiWnBk488wpNuHpDY1iY816exfz/Gf/ZwGhvLGDp0KC8tXAmFAoVCgXHjxpOqrqayopKly5ZBWxsfffQhEydOpL6+Hul+QDaTBd/nq189m9///nc88tBDfPkrR5LPF7Asi0KhwNqPP6a6upqq6mpcA5DBuVi+CdksiUQCIVxaWqCtzaFQKOB5UHAc4lKCaWILQWlpKc3NzfhSUl5Rwap33uHO634C3gcE6yCBdFKUlVew/wHDMNBuPpruQwv+Go1Go9FodghDuMTjIDwbzCqkn6Q57YLpcNE3z+WiOZdgWBaTRh2MFa8C4WGY0JqGcy+5hOMO2g/XdTCNND4WJSUl9B88AIy32X/yZO57bB5v/usdFr+f4eTvXQBOlokj+nPHvF/y+ucPYdmK1Zx28UWkEtCS8RBxSbYtRX19HT+97hrO+cZs5t3xCFOnTgURR5obqaqpJJOG9957mzE9B4IMdPCeABIpmpvTJBKlpFJgmoJEwsb1wLYs8DxwXRzfJ5PJkEqlMC2D1pYWJh38WS781sVU+hswTIHjufhGCSNGjgZA4ms/a023oQV/jUaj0Wg0O4SJEziuiAS4cby8he/aOG6OL512Ci+8+A/uuet23j1wJTlcfJnH91xkAQ4+cCxHHzUFzwMoIKwYAOskYPRgxP6fpX/iBi774U3kzRS1DeUQzzFl0hjm/fRdvnn5NaRlis9MGI6wJBYFnJbVtIlK2jyTg6Yfy1c//wfmXjyblw6fwQetSfKJShr69uXdN5fw9B/v53OTrgAgjoWPT2bTJu576BkqynvQswf4QuC3raJHrHjCwgLb4o3X3+KlV15j0uRJ9K6rIdO8nr49azj+mAMpBQpuYGHwMCi4wU+lJxE6b6emm9CLTo1Go9FoNDuE47uBA4sQkEwSj8fxpURKn7LSUr785S8zaPBAXnjheVKpFJ7rIoTA9yGfD3xsTBOQfuj5HitKKKlUilSygv/3//4fAwcOpLws+LxXnz4k4nH+8c9/UFVVRe9a8D0XzylQUVGJYZoYRrCTzx1/PKmSBI/935NkMhk8z6NpwBDKEzHm3X03859/GQyQSExHcuedd3Lf/b9lzJghlJWC9H1SyRR2LHLSnsef/vBHvLY2pk+bjm3HkVJScBzcotsQvo86o2JTEEK7+mi6D63x12g0Go1Gs0PE/DhCGBR8j5iU5NIZLN/CTFRQ8PIcNHkKN//oh1w4+1Ky5kZMHGzDJmHAjT+Zy+P334ht20jPwBEmnpQc+ZXzuGDmwUjbZb9RA7nziSXsP7qJKh8wTGr79GTi+NG8+NE79OvfSAkghURYKdoyJj2sZmLCJpvPMnzEJG686ft8//vXkS3kyGdTNA0dzm13/JTLbr+Hb190NpMnTaJnbQP/+tcSXpj/PCPHHMb5512A5ULeFWxc/k8uPe80astcpCPYuHEja99/h698/Ruc9pUvk7QEtWUx/vjU3zn95K+REJtAgCddEOUMGjqc715+KWUpA9/30K7+mu5AC/4ajUaj0Wh2CEEMPy/xYj4U0hw78ygG9O1LuuBTYiexY0mOPPZYLvnoQ/5/e3cPG8dxhnH8Pzuzt3t3PB4pxZYMM4kNOB+yiyRAAtgJ7CKxA8VOAAcw5DiAS/UqBLWCEXcujHSCK5Xq1QUgkCIBota2GkG2YdCCBVogQ4nHvf2YmRTHW8hBxEYHMeI+v4YL3mAww22enX3f4z+vf8KPf/Asq/du8e6f/8DVzYym+YYQDMGvcqe4hzEGnxg8KZMy8vLp05zZXOLXL/2EsSmpTYbJBrz1p3e4/dSEN15/lWGEaC1lssRLv32XUz99BjvdYsmleH+C373zR3aSik//tclxu0zWT/nN22e4duZN3v/gQ/7293/w+cdfEPqOv370ES/87Pc8exLqfxf8/Be/4vRnFdvbXzFNV7B2xHM/fJ6/vPceL7zyIgBxOuG1V37JvTphY2eHvWab4dKAxtdU3tI0DcPBrLU3xKDgL4fCxKgvkxUREZGH0AAhUvYgm37GTrVKtnx89k+7YsT6GhMDZQLR9tjc2eXkwNPb/Zo7gx9xzO1QNzWhWSEOMqyBrz08bWYlMnbvSzarNY6PI83ONn5pTGZKrN/lNk+xZIECXD4hdSmTco/oVhiHbbCO6EfE9PZsqcUqvp+R+CmZmUKYEtyA3aKiuQODYyOa3OKtgwLGBkLyFSE+iU0s06bC+wxjDMO+4e5+Cc8oNpit29TpEkW6Qi8pCdGzV0wYjJ6gqgN5P2FvMmE8zLEECGn7AOANLKr0//z585w7d461tTUAYowLKzFaX19nfX2dixcvkmXZQuZ83Mz/nmVZcvnyZc6ePduWlT1qIQSSJOHKlStcunSJq1evMhqNHjheJ/4iIiLycByAIQPIn2Oc3/eZMbDfsDuPid8bL88uVlf5DgDHyOx9A4Bn7k/Bg+/z5GB2aVee2P9lD+yIk/MxSwBDAJaz/WJ8uzpbQgJmf2Svvz/e5kAOyazhcbkPfPe/9rU/NmGtbYrsp/1vDVlu9+ng+BopkN6320E+W/jAzWbIh8P5Ar7Vaal+X3kU1NwrIiIiItIBCv4iIiIiIh2g4C8iIiIi0gEK/iIiIiIiHaDgLyIiIiLSAQr+IiIiIiIdoOAvIiIiItIBCv4iIiIiIh2g4C8iIiIi0gEK/iIiIiIiHaDgLyIiInKEhBCIMQLQNE17vQje+3a+GCMhBEIIC5v/cTDfv7UW7z1A+/NRM8YQYyRJEpIkIcZ44P1W8BcRERE5QvI8xxgDgHOOEAJFUSxk7l6vhzGmnX8eOLtkvl/nHL1ejyRJsNYe2nq891hrqeuaJEnae/O/dOtOiYiIiBxx3nuqqmIymbQn0f1+fyFzxxhpmoamaaiqihDCoZ12H5a6rtt9z/feNM2hrMUY0z58hRBwzh04/uBPRUREROSxMi+9GQ6HhBAwxiysHCeEwHg8JsZImqYYY/DeH3jKfNTMT/fLsiRNU5qmad+sPGpVVZHneXvqX5YleZ4/cLyCv4iIiMgRsrW1xYULFxiPxxRFQa/X4+7du4tIH14AAADlSURBVIxGo4eee3Nzk42NDa5fv45zrq0v75J5wE+ShBs3bnDt2jWm0+mhrGU6nWKMYWNjo30QO4iJi+z4EBEREZFDdfPmTW7dusXu7i7OOZxzWGsXUo6SpikhhPaUuygKsixbaAPx/7sYI8YY8jynrmvSNKUoikN565FlGWVZEmPkxIkTnDp1CufcA9ei4C8iIiIi0gHdejcjIiIiItJRCv4iIiIiIh2g4C8iIiIi0gEK/iIiIiIiHaDgLyIiIiLSAQr+IiIiIiIdoOAvIiIiItIBCv4iIiIiIh2g4C8iIiIi0gH/AXmRM3UtQcAaAAAAAElFTkSuQmCC\"/></strong></em></p>\n<p>Then, continue by setting the “next” field/pointer in the newly created node to <code>NULL</code>;</p>\n<p>Set the “previous” pointer in the newly created node to the current <code>LAST</code> / to point to Lavender/ to point to the node detected by <code>LOCATION</code>;</p>\n<p>Change/Set/Update the Lavender’s “next” pointer to point to the new node / to link with the <code>NEWNODE</code> pointer (delete <code>NULL</code> in the field and link to the existing <code>NEWNODE</code> pointer);</p>\n<p>Update the <code>LAST</code> pointer to point to the newly created node;</p>\n<p>Eventually the final doubly linked list looks like this (<strong>Figure 2</strong>);</p>\n<p><strong>Figure 2</strong></p>\n<p><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p><em><strong>Note</strong>: Award<strong> [4 max] </strong>for responses that return one or more drawings without any explanation at all, for evidence of these features</em>:<strong><br/><em>[1] </em></strong><em>Evidence of creation of an initial new node for Primrose out of the list;<strong><br/>[1] </strong>The order of nodes Aster/Camellia/Lavender/Primrose is eventually correct;<strong><br/>[1] </strong>The two unidirectional links between Lavender and Primrose are (eventually) correctly displayed, from-to the appropriate fields;<strong><br/>[1] </strong>LAST points correctly to the appropriate field in the new node Primrose, <strong>and</strong> NULL fills the last field of the new node;</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each one in the correct order</em>.<br/>Apple;<br/>Broom;<br/>Camellia;<br/>Day Lily;</p>\n<p><strong>Note</strong>: Solution for the Spanish version (in this order):<br/>Aster; Camelia; Lavanda; Lirio;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award marks as follows up to <strong>[3 max]</strong></em>.</p>\n<p><em><strong>Example answer 1</strong></em><br/>Create an empty stack <code>(FLOFRU)</code>;<br/><strong>pop</strong> all elements from <code>FRUITS</code> and <strong>push</strong> them onto <code>FLOFRU</code>;<br/>Then <strong>pop</strong> all elements from <code>FLOWERS</code> and <strong>push</strong> them onto <code>FLOFRU</code>;</p>\n<p><em><strong>Example answer 2</strong></em><br/>Create an empty stack (<code>FLOFRU</code>);<br/>While <code>FRUITS</code> is not empty<br/><strong>        pop</strong> an element from <code>FRUITS</code> and <strong>push</strong> it onto <code>FLOFRU</code>;<br/>While <code>FLOWERS</code> is not empty<br/><strong>        pop</strong> an element from <code>FLOWERS</code> and <strong>push</strong> it onto <code>FLOFRU</code>;</p>\n<p><em><strong>Note</strong>: Award <strong>[2 max]</strong> for generic descriptions that do not use appropriate terminology on data structures and their operations</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "17N.1.HL.TZ0.13",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A supermarket chain has identified four potential locations to build a distribution centre. The distribution centre will store goods and distribute them to a large number of supermarkets in the region.</p>\n<p>A computer simulation will be run to determine the best location to build the distribution centre.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> reasons for using a computer simulation in this scenario.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> data inputs for this computer simulation model.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> criteria that may be used to determine the best location to build the distribution centre.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss how the simulation model will use the data inputs in (b)(i) and the criteria identified in (b)(ii) to generate recommendations.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why the use of a computer simulation may not be beneficial.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 mark]</strong> for reason and <strong>[1 mark]</strong> for expansion <strong>[4 marks max]</strong></em>.<br/>Cost;<br/>Building a distribution centre is expensive so it is impractical to build four centres to see which one is the best location / build the centre in a poor site location;</p>\n<p>Time;<br/>The time to build and run the simulation is much less than building a distribution centre;</p>\n<p>Amendments to input data / extra variables added;<br/>The simulation could be adjusted to factor in unforeseen variables / run worst case scenario e.g. roadworks, changes in traffic congestion, new housing being built in the area;</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Road networks to and from the distribution sites;<br/>Location of the supermarkets / distance to the supermarkets;<br/>Location of the port / airport / station that delivers the products to the distribution centre;<br/>Cost of the land for each of the sites;<br/>Average labour costs in the area;<br/>Time taken to drive from the site to each supermarket at different times of the day;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Overall costs to build the distribution centre for each site;<br/>Running costs to receive shipments / distribute goods;<br/>Time taken to build the distribution centre at each of the sites;<br/>Time taken to distribute goods / shipments to arrive;</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p><strong>Overall costs to build the distribution centre for each site</strong>;<br/>Inputs of labour rate, time taken to build, cost of land are inputted;<br/>Simulation is run with a variety of scenarios (e.g. number of workers, rate of pay for labour; cost of materials);</p>\n<p><strong>Running costs to receive shipments / distribute goods</strong>;<br/>Inputs of fuel cost, distance to each supermarket / sites, fuel economy, number of trips, etc.<br/>Simulation is run with a variety of scenarios (eg fuel price increases, congestion reduces fuel economy, more trips needed to ship goods)</p>\n<p><strong>Time taken to build the distribution centre at each of the sites</strong>;<br/>Inputs for number of diggers, number of workers, building materials delivery schedule;<br/>Simulation is run with scenarios (delay in window deliveries, change in the number of builders, etc.)</p>\n<p><strong>Time taken to distribute goods / shipments to arrive</strong>;<br/>Inputs for time taken to travel to destination, number of vehicles; length of time to load / unload goods, etc.;<br/>Simulation is run with scenarios (e.g. Increased traffic congestion, number of vehicles shipping goods, etc.)</p>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The cost outweighs the benefit;<br/>It may be impossible to simulate a simulation / too many variables / too many confounding factors;<br/>No positive reasons such as time/danger/modification;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.5",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-2-simulations",
      "b-1-the-basic-model"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline the need for a translation process from high level language to machine code.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>(The program written in HLL must be translated into machine code) so that the computer can <span style=\"text-decoration:underline;\">execute</span> the program;<br/>as the computer only understands machine language / as code written in HLL can only be understood by humans and cannot be interpreted by the computers (which work in binary);</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were mostly able to offer good responses as to why a translation process is required from high level language to machine code.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.5",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Draw the truth table for the following logic circuit.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>1 mark for every two correct rows</em>;</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>The majority of candidates achieved high marks for drawing and completing an appropriate truth table for the given logic circuit. Some marks were lost by candidates not including all possible combinations of inputs in their responses, or missing out some of the interim stages, therefore giving inaccurate outputs.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.6",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The following matrix has non-zero elements on the diagonal, on the super-diagonal (the first diagonal above the main diagonal) and on the sub-diagonal (the first diagonal below the main diagonal). All the rest of the elements are zeros.</p>\n<p>The following two-dimensional array named <code>MAT</code> of dimensions 6 × 6 is an example of such a matrix.</p>\n<p style=\"text-align: center;\"><math style=\"font-family: 'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>MAT</mi></math></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>Method <code>isValidMatrix(N,A)</code> accepts an integer <code>N</code> and a two-dimensional array <code>A</code> of dimensions <code>NxN</code>. It returns <code>True</code> if all elements below the subdiagonal and all elements above the superdiagonal are zeros and all elements on three diagonals are non-zeroes; otherwise it returns <code>False</code>.</p>\n<p>For example, <code>isValidMatrix(6,MAT)</code> returns <code>True</code> for the matrix <code>MAT</code> given above.</p>\n</div><div class=\"specification\">\n<p>Given the following recursive method <code>mystery()</code> with two formal parameters:<br/><code>A</code> (a two-dimensional array) and <code>R</code> (an integer).</p>\n<pre>mystery(A,R)<br/>   if R &gt; 0 then<br/>      return A[R][R-1] + mystery(A,R-1)<br/>   else<br/>      return 0<br/>   end if<br/>end mystery</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the value of <code>MAT</code>[3][4].</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an efficient algorithm for the method <code>isValidMatrix()</code>.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Determine the value of variable <code>X</code> after execution of the following method call:</p>\n<p><code>X = mystery(MAT,5)</code></p>\n<p>where <code>MAT</code> is the two-dimensional array given. You must show your working.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Deduce the purpose of the method <code>mystery(A,R)</code>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>4;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[8 max]</strong></em><br/><em>Award <strong>[1]</strong> for initialization, correct changing and returning of a flag.</em><br/><em>Award <strong>[1]</strong> for the nested loops.</em><br/><em>Award <strong>[1]</strong> for correct initial and for correct terminal value and changing the value of the control variable in <strong>outer</strong> loop.</em><br/><em>Award <strong>[1]</strong> for correct initial and for correct terminal value and changing the value of the control variable in <strong>inner</strong> loop.</em><br/><em>Award <strong>[1]</strong> for<strong> efficiency</strong> (terminating execution when a zero or non-zero element is not at the correct position).</em><br/><em>Award <strong>[1]</strong> for correct condition in if statement.</em><br/><em>Award <strong>[1]</strong> for checking elements in the lower/upper triangle.</em><br/><em>Award <strong>[1]</strong> for checking elements on the three diagonals.</em><br/><em>Award <strong>[1]</strong> for the correct logical expression.</em></p>\n<p><em><strong>Example 1</strong></em>:</p>\n<pre>INVALID=False<br/>R=0<br/>loop while R&lt;N and not INVALID<br/>   C=0<br/>   loop while C&lt;N and not INVALID<br/>      if abs(R-C)&gt;=2 and A[R][C]!=0 or abs(R-C)&lt;2 and <br/>A[R][C]==0 then<br/>         INVALID=True<br/>      endif<br/>      C=C+1<br/>   endwhile<br/>   R=R+1<br/>endwhile<br/>return not INVALID</pre>\n<p><em>Please note that instead the logical expression given in the Example answer 1 several if statements could be used and award 1 mark for each correct if statement (1 mark for checking elements in the lower triangle, 1 mark for checking upper triangle, 1 mark for checking three diagonals)</em>.</p>\n<pre>if R&gt;C and R-C&gt;1 then //lower triangle<br/>   if A[R][C]!=0 then<br/>      INVALID=True<br/>   endif<br/>endif<br/>if R&lt;C and C-R&gt;1 then //upper triangle<br/>   if A[R][C]!=0 then<br/>      INVALID=True<br/>   endif<br/>endif<br/>if R&lt;C and C-R=1 or R&gt;C and R-C==1 or R==C then //three <br/>diagonals<br/>   if A[R][C]==0 then<br/>      INVALID=True<br/>   endif<br/>endif</pre>\n<p><em><strong>Example 2</strong></em>:<br/><em>Award <strong>[7 max]</strong> (no ‘efficiency’ mark).</em><br/><em>Award <strong>[1]</strong> for initialization, correct changing and returning of a flag</em><br/><em>Award <strong>[1]</strong> for the nested loops.</em><br/><em>Award <strong>[1]</strong> for correct initial and for correct terminal value of the control variable in <strong>outer</strong> loop.</em><br/><em>Award <strong>[1]</strong> for correct initial and for correct terminal value of the control variable in <strong>inner</strong> loop.</em><br/><em>Award <strong>[1]</strong> for correct condition in if statement.</em><br/><em>Award <strong>[1]</strong> for checking elements in lower/upper triangle.</em><br/><em>Award <strong>[1]</strong> for checking elements on the three diagonals.</em><br/><em>Award <strong>[1]</strong> for the use of correct logical expressions.</em></p>\n<pre>INVALID=False<br/>loop R=0 to N-1 do<br/>   loop C=0 to N-1 do<br/>      if abs(R-C)&gt;=2 and A[R][C]!=0 or abs(R-C)&lt;2 and <br/>      A[R][C]==0 then<br/>         INVALID=True<br/>      endif<br/>   endloop<br/>endloop<br/>return not INVALID</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<pre><em>Award <strong>[4 max]</strong></em><br/>X = mystery( MAT, 5)= 5+ mystery( MAT, 4);<br/>5 + 7 + mystery( MAT, 3);<br/>5 + 7 −5 + mystery( MAT, 2);<br/>5 + 7 −5 + 9 + mystery( MAT, 1);<br/>5+7-5+9+1+mystery(MAT, 0)= 5+7-5+9+1+0 = 17;</pre>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Calculates the sum of R elements;<br/>Starting from A[R][R-1] to A[1][0];<br/>on the sub-diagonal (of the two-dimensional array A);</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Parts (a) and (b) have been attempted with some repetition. The reasonable general knowledge of when and how data can be lost and backup strategies. However, many responses were vague enough not to describe the situation with reference to the basic technical terminology of the devices/systems. </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was reasonably well answered with many gaining at least 2 or 3 of the marking points.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Quite a few candidates did not attempt the recursion question. However, those who did generally answered well.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Quite a few candidates did not attempt the recursion question. However, those who did generally answered well.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.1.HL.TZ0.13",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:left;\">Calculate, showing your working in each case: the binary (base 2) value of the denary (base 10) number: 105.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p style=\"text-align:left;\">Calculate, showing your working in each case: the hexadecimal (base 16) value of the denary (base 10) number: 200.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for showing workings</em>.</p>\n<p>(0)1101001;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for showing workings</em>.</p>\n<p>C8;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates were able to correctly convert a binary number to its denary equivalent.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to correctly convert a hexadecimal number to its denary equivalent.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "22M.1.SL.TZ0.7",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>ShowTime</em> is a local theatre that allows online booking for its various shows.<br/>All customers are able to access the database to book a movie of their choice.</p>\n<p>However, some of the customers have received incorrect information when their booking has been made. This has been caused by update anomalies.</p>\n</div><div class=\"specification\">\n<p><em>ShowTime</em> have introduced a database recovery system in case the database becomes corrupted.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> types of update anomaly.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> methods of database recovery that can be used to restore the system.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> tasks that are carried out by the database administrator (DBA).</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Customers can choose a credit card as their method of payment. However, some customers are concerned that their personal information could get shared with unauthorized third parties.</p>\n<p>Explain <strong>one</strong> way that the DBA at <em>ShowTime</em> can ensure the anonymity of the customers is maintained.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Insertion;<br/>Deletion;<br/>Modification;<br/>Lost update;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each method of recovery followed by <strong>[1]</strong> for brief explanation up to a maximum of <strong>[2]</strong>.</em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong>.</em></p>\n<p><em>Do not accept generic back up responses. It must be DBMS related</em>.</p>\n<p><strong>System log</strong>;<br/>In the event of a disk crash (any catastrophic failure);<br/>The recovery method recovers a past copy of the database;<br/>Backed up to archival storage and reconstructs a more current state by redoing the operations of committed transactions, up to the time of failure.</p>\n<p><strong>Deferred Update</strong>;<br/>To support ABORT and machine failure scenarios;<br/>While a transaction runs, no changes made by that transaction are recorded in the Database;<br/>On a commit, new data is recorded in a log file and flushed to the disk;<br/>New data is then recorded in the database itself;<br/>On abort, do not do anything (the database has not been changed);<br/>On a system restart after a system failure, REDO the log;<br/>Data on disk is not updated until after a transaction commits fully;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Authorizing/managing access to the database;<br/>Coordinating and monitoring its use;<br/>Capacity planning;<br/>Routine backups to safeguard from data loss during crash;<br/>Tried and tested strategies to recover the database after any crash; <br/>reinstallation/overwrite of data; // Do not accept installation of new software alone<br/>Manage security;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[3 max]</strong></em>.<br/>Important sensitive information like personal details such as names, social security;<br/>Number and addresses should be separated;<br/>A unique identifier should be assigned to each customer;<br/>This identifier is then used to access the booking details in the <em>ShowTime</em>;</p>\n<p><em><strong>Note</strong>: No generic access rights issues like authorisation should be accepted.</em><br/><em>Do not accept Encryption as it is a general point and not specific to DBMS</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18M.2.SL.TZ0.2",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-1-basic-concepts",
      "a-3-further-aspects-of-database-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A biotechnology company owns a resource centre which collects and classifies organisms for use in research.</p>\n<p>Only authorized employees are allowed access to some laboratories in the resource centre.</p>\n<p>These laboratories are protected by locked doors. Each door is controlled by a separate microprocessor. A digital camera is used to scan the iris of an employee who wishes to enter the lab. If the employee is authorized the doors are unlocked.</p>\n</div><div class=\"specification\">\n<p>The company is planning to use a centralized computer system to secure the resource centre’s building.</p>\n</div><div class=\"specification\">\n<p>The operating system has an important role in this system.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> benefits of using a digital camera as an input device in this control system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the use of a microprocessor in this control system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the function of an output transducer.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare a centrally controlled system with the system described above.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> functions of the operating system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Polling and interrupt are two operating system management techniques.</p>\n<p>Suggest with reasons which of these two techniques is the most appropriate for this centrally controlled system.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/><em>(<strong>[1]</strong> for each of the <strong>two</strong> benefits)</em>.</p>\n<p>Digital cameras are relatively cheap;<br/>Robust;<br/>No need for AD conversion;<br/>Generally very high quality pictures (useful to prevent malpractice);<br/>Generally quick;</p>\n<p><em><strong>Example answer 1</strong></em><br/>No need for conversion because image is in digital format;<br/>Thousands/millions of photos of eyes could be taken before needing replacement;</p>\n<p><em><strong>Example answer 2</strong></em><br/>It is directly connected to a microprocessor for image comparison;<br/>It could be fitted (purchased), with a macro lens so that a close up scan of the Iris is possible;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award up to <strong>[2 max]</strong></em>.<br/>Processor compares the inputted image/pattern with the images stored in memory;</p>\n<p>If a match is found, it sends a signal to unlock doors /<br/>(if match is not found, it sends error message/the doors remain locked);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Output transducer is a device (an actuator) which converts;<br/>an electrical signal into physical quantity (a physical action);</p>\n<p>Output transducer is a device which converts energy from one physical form to another;<br/>e.g. electrical energy (signal) into electro-mechanical or kinetic energy / to produce action (lock/unlock door);</p>\n<p><em><strong>Note</strong>: Award one mark only for an answer that just says that the output transducer can be used to lock/unlock doors</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for the meaning of centrally controlled system;</em><br/><em><strong>[1]</strong> for the meaning of a distributed system, and;</em><br/><em><strong>[up to 2 max]</strong> for an expansion/comparison addressing both kind of systems <strong>[up to 4 max]</strong>;</em></p>\n<p><em><strong>Example answer</strong></em><br/>A centrally controlled system involves a central computer which controls all labs/doors;<br/>A distributed system can have only a dedicated microprocessor with memory to control one of the labs/doors;</p>\n<p>A centrally controlled system is more versatile;<br/>Could be used in solving other business tasks (accept specific examples);<br/>Can unlock all doors easily during an emergency;<br/>Access rights can be updated easily;<br/>Data is stored centrally and therefore easier to update;<br/>A failure in a central system would affect all doors;</p>\n<p>A distributed system can be programmed with ad hoc OS depending on the technologies used;<br/>It may be practical choice when dealing with legacy systems/ specific devices/ old infrastructure/while updating the facilities;<br/>It contributes a higher sense of partition of the physical space (territory) to groups / individuals;<br/>Data are stored locally, so there is local consistency (and smaller size mean easier to manage);<br/>Changes to data may be done locally, and if some data are common in two or more different systems, global inconsistencies across different systems may be introduced;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Memory management;<br/>Resource allocation / Resource and Hardware management (printer, disk drives, etc.);<br/>Booting / bootstrapping;<br/>Loading and execute / provide service for applications software;<br/>Disk/File system management;<br/>Data security;<br/>Provides a user interface to other levels of the machine;<br/>etc.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for choosing “interrupt”, <strong>[1]</strong> for explaining “interrupts” and <strong>[1]</strong> for justifying the choice in this context, up to <strong>[3 max]</strong></em>.</p>\n<p><em><strong>Example answer</strong></em>:<br/>Interrupt;<br/>A signal sent from an input device to a computer causes the processor, and the main program that operates the computer (the operating system), to stop and figure out what to do next;<br/>Interrupt is better in this situation because it does not waste central computer’s time (other tasks could be performed);<br/>(<em>example of emergency – it must give quick response</em>);</p>\n<p><em><strong>Note</strong>: Award <strong>[1 max]</strong> for an answer of “polling”, but only if reasonably justified</em>.</p>\n<p>Polling – the continuous checking of all input devices by processor to see what state they are in/to see whether they are still connected/want to communicate;<br/>So a faulty device that is polled will not reply;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "17N.1.HL.TZ0.12",
    "topics": [
      "topic-7-control",
      "topic-6-resource-management"
    ],
    "subtopics": [
      "topic-7-1-control",
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p style=\"text-align:left;\">Identify <strong>one</strong> function of a single-user operating system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>memory management;</p>\n<p><em>Note to examiners: allow any other correct function of a single-user operating system</em>.</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Many correct alternative functions of a single-user operating system were identified by candidates.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.8",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The QT Railway and QT Highway have recently been built to connect Qinghai with Tibet. This has made the mountainous region of Tibet accessible to the rest of China.</p>\n<p>The endangered Tibetan antelope migrates over 300 km each year to give birth to a single calf. The QT Railway and QT Highway cross this migration route, see <strong>Figure 2</strong>.</p>\n<p style=\"text-align: center;\"><strong>Figure 2: Map of the study site and habitat of Tibetan antelope</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: center;\">[Source: adapted (simplified redraw of the nature reserve data map) from Karina Manayeva, Buho Hoshino, Hiromasa Igota, Takashi Nakazawa and Ganzorig Sumiya, 2017. Seasonal migration and home ranges of Tibetan antelopes (Pantholops hodgsonii) based on satellite tracking. <em>Int. J. Zool. Res</em>., <strong>13</strong>: 26–37. © 2017 Karina Manayeva et al.]</p>\n<p style=\"text-align: left;\">The building of the QT Railway and QT Highway presents potential dangers to both the antelopes and the public travelling through this nature reserve.</p>\n<p style=\"text-align: left;\">To understand more about the antelopes’ migration patterns, some antelopes have tracking devices attached. Using the information from these tracking devices, a 2D visualization model can be developed and updated in real time.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>four</strong> items of data that would be included to create the 2D visualization model.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why a 2D visualization model would be used rather than a 3D visualization model in this scenario.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the development of a visualization model was necessary in this scenario.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Continuously collected data (from satellite, electronic trackers, observation);<br/>Data on current speed of antelopes / length of time for the entire herd to pass a certain point;<br/>Previous migration paths stored and used to provide additional data;<br/>Predicted path calculated to determine when the antelopes will hit the railway line / highway;<br/>Model may show the expected time of highway / railway inception updated in real time;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>2D visualization gives a rapid way of seeing what is happening relative topositions on the map and updates quickly;<br/>A 3D model may have delays updating providing information too late;</p>\n<p>The antelope is always on the ground / in a 2D space<br/>So there is no need to map in 3D space</p>\n<p>2D visualization will update rapidly because it is less complex;<br/>A 3D model that requires more powerful graphics cards (GPU);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award<strong> [2 max]</strong> for technical difficulties of collecting data;</em><br/><em>Award <strong>[2 max]</strong> for a reasonable solution to the problem</em></p>\n<p>Difficult to put people on the ground to keep pace with the antelope;<br/>Because the area is inhospitable / mountainous;<br/>The antelope may cross the road at any point over a vast distance so the number of people required is prohibitive;<br/>Poor visibility may affect visuals of antelopes / too dark for cameras;<br/>Transmission may get disrupted in a remote place like Tibet;<br/>So satellite transmissions as a back up system;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.6",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-3-visualization"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p style=\"text-align:left;\">Define the term <em>usability</em>. </p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>The extent to which a device can be used by specific/its users;<br/>To achieve specific goals effectively/efficiently/satisfactorily;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were mostly able to define the term usability, but some candidates found it difficult to do so without including the word usable, or similar, in their response.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.9",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Marble Reading Book Stores</em> (<em>MRBS</em>) is a chain of bookstores based in London. The stores want to keep information about the books they sell, the authors of the books and the publishers they work with. The assumptions made when the database was created were:</p>\n<ul>\n<li>a publisher can publish books from one or more authors</li>\n<li>an author can write one or more books.</li>\n</ul>\n</div><div class=\"specification\">\n<p>Three of the tables in the <em>MRBS</em> database are shown below:</p>\n<p style=\"text-align: center;\"><strong>PUBLISHER</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: center;\"><strong>AUTHOR</strong></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: center;\"><strong>BOOK</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"specification\">\n<p>The <em>MRBS</em> database undergoes many transactions.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the entity-relationship diagram (ERD) for this scenario.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why data validation is difficult for the Book_Title attribute.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the result from the following query:</p>\n<pre>SELECT Book_Title<br/>FROM BOOK<br/>WHERE Genre = \"Non-fiction\"<br/>AND ISBN = '0-98124-612-2'</pre>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a query to find the titles of the books published by “Orlando Crux”.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why atomicity is important within a database.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how data consistency can be maintained in transactions in this database system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Some data in the <em>MRBS</em> database is redundant.</p>\n<p>Outline <strong>one</strong> problem caused by redundant data.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for correct relationship “publishes books from”</em>.<br/><em>Award <strong>[1]</strong> for correct relationship “writes”</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>As Book_Title attribute is String/Varchar;<br/>It does not allow many validation checks (like range check etc.);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Seeking the truth;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for selection of both Publisher_Name and Book_Title from the two respective tables.</em><br/><em>Award <strong>[1]</strong> for making INNER JOIN of the Publisher_Name from both tables-Publisher and Author.</em><br/><em>Award <strong>[1]</strong> for the correct test ON Author_Num from both tables-Author and Book.</em><br/><em>Award <strong>[1]</strong> for the correct test of equity of Publisher_Name.</em></p>\n<pre>SELECT Publisher.Publisher_Name, Book.Book_Title FROM Author<br/>INNER JOIN Book ON PUBLISHER. Publisher_Name = <br/>Author.Publisher_Name, ON Author.Author_Num = Book.Author_Num <br/>WHERE Publisher.Publisher_Name = \"Orlando Crux\";</pre>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Helps the searching of a family name for example rather than a “like” name search;<br/>So, making data into the smallest possible unit helps quicker searches;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Each user sees a fixed view of the data;<br/>This may include visible changes made by the user / the user's own transactions and transactions of other users;</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>When a field value changes multiple occurrences must be updated;<br/>For example, if a publisher moves, we’ll need to change the values for City and Country in multiple records;<br/>Problem occurs if we forget to change the values in any of the records;<br/>The database would then have data inconsistency;</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students knew how to sketch an ER diagram though some of them don’t seem to know the various relationships in it and how to apply. Some of them can draw the entities but setting the relationship was not done correctly.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most of the students understood the validations in String attributes and its challenges.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The query was well understood.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students were able to get the basic structure of the query well though some of them could not set the two tables and their relationship properly while writing the query. Also many students searched for the publisher or author code not the publisher name.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students were able to explain atomicity though only very few students were able to refer to rollback as well.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>‘Data consistency’ was understood though they could not relate to the given scenario of the bookstore database.</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students gave generic answers and most of them incorrectly referred to the space redundancy that is not appropriate to this question clearly.</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.1",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model",
      "a-1-basic-concepts"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p style=\"text-align:left;\">Outline <strong>one</strong> reason for the use of standards in the construction of networks.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Standards ensure compatibility between nodes on the network;<br/>Through the use of common techniques/protocols/language;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Most candidates recognised that the use of standards in the construction of networks was related to the need for compatibility, with many answers based on that concept seen.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.10",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p style=\"text-align:left;\">Explain how cache memory affects system performance.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Cache is high-speed memory;<br/>Located between CPU and RAM;<br/>Frequently used data/instructions are (temporarily) stored in cache memory;<br/>To reduce the access time needed/Speed up retrieval time/To improve the speed of processing;<br/>CPU searches as instruction’s address first in the cache, and if not found, in RAM;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates were able to explain the positive impact of cache memory on system performance, with many high scoring responses.</p>\n</div>",
    "question_id": "22M.1.SL.TZ0.11",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>OrderYourFoods</em> is an online company that allows customers to order food and get it delivered to their location. It operates in a number of cities in India.</p>\n<p>On the home page of the company’s website www.orderyourfoods.com, the customer has to enter the city name and the address the food needs to be delivered to. Based on this information, the website displays a list of restaurants.</p>\n</div><div class=\"specification\">\n<p>The website has a pop-up advert that encourages customers to subscribe for discount offers, see <strong>Figure 3</strong>.</p>\n<p style=\"text-align: center;\"><strong>Figure 3: An example of a pop-up advert</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: left;\">The following code is executed when the customer clicks on the subscribe button.</p>\n<pre style=\"text-align: left;\">&lt;? php<br/>$to = 'info@orderyourfoods.com';<br/>$from = '$_POST['email']';<br/>$headers = \"From: \" . $from . \"\\r\\n\";<br/>$subject = \"New subscription at OrderYourFoods\";<br/>$body = \"New user subscription: \" . $_POST['email'];<br/>if( filter_var($_POST['email'], FILTER_VALIDATE_EMAIL) )<br/>{<br/>   if (mail($to, $subject, $body, $headers, \"-f \" . $from))<br/>   {<br/>     echo 'Your e-mail (' . $_POST['email'] . ') has been added to our<br/>     mailing list!';<br/>   }<br/>   else<br/>   {<br/>      echo 'There was a problem with your e-mail (' . $_POST['email'] . ')';<br/>   }<br/>}<br/>?&gt;</pre>\n</div><div class=\"specification\">\n<p>The website uses the following method to communicate with the potential customers, see <strong>Figure 4</strong>.</p>\n<p style=\"text-align: center;\"><strong>Figure 4: Banner for OrderYourFoods.com</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: left;\"><em>OrderYourFoods</em> currently displays an email address and a phone number to show customers how they can be contacted.</p>\n<p style=\"text-align: left;\"> </p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the protocol used in the company’s website.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the importance of protocols on the World Wide Web.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the type of scripting used in the php code.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> inbuilt function along with parameters used in the php code.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the processing that occurs when the subscribe button is clicked.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> additional method of online interaction that can be incorporated in this website.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p><em>OrderYourFoods</em> is concerned that it never appears close to the top of the list of search results when the term “food delivery” is typed into a search engine.</p>\n<p><em>OrderYourFoods</em> has decided to use black hat search engine optimization techniques to be more visible in search engines.</p>\n<p>Evaluate this decision.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Hypertext transfer protocol secure/https;<br/><em>Do not accept http</em>.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>To allow successful communication to take place;<br/>Ensure data integrity such as error checking;<br/>Regulate flow control such as prevent a fast sender from overwhelming a slow receiver;<br/>Managing deadlock when two processes are each waiting for the other to complete before proceeding;<br/>Manage congestion;<br/>Manage error correction such as enabling reliable delivery of digital data over unreliable communication channels;<br/>Manage packet switching;<br/>Manage security / encryption / authentication ……;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Server-side scripting;</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>filter_var($_POST['email'], FILTER_VALIDATE_EMAIL);<br/>mail($to, $subject, $body, $headers, \"-f \" . $from);</p>\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The script reads the email id;<br/>Checks if the email address entered is valid/executes the <strong>filter( ) function</strong>;<br/>If correct, then the <strong>mail( ) function</strong> of PHP adds the email address and displays that your email address has been added to our mailing list;<br/>Else, the error message will be displayed;<br/>There is no error message if the email address is invalid.</p>\n<p><em><strong>OR</strong></em></p>\n<ul>\n<li>The script gets an e-mail address via POST</li>\n<li>It checks whether it is valid via a filter function; if not, it does nothing</li>\n<li>If it is valid, it checks with the mail function, and displays either a success or an error message</li>\n</ul>\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Social Networking/Facebook/Social Media/Twitter;<br/>Forums;<br/>Online chat;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em><strong>Advantages</strong> <strong>[2 max]</strong></em><br/>The use of black hat SEO techniques may increase the visibility of the <em>OrderYourFoods</em> website;<br/>Which will bring a short term gain;</p>\n<p><em><strong>Disadvantages</strong> <strong>[2 max]</strong></em><br/>Black hat techniques are unethical;<br/>Use of these techniques may lead to blacklisting;</p>\n<p><em><strong>Evaluative comment</strong> <strong>[1 max]</strong></em><br/><em>OrderYourFoods</em> may decide that the need to increase their visibility, and increase their sales potential, so this may be a pragmatic (if unethical) compromise;<br/><em>OrderYourFoods</em> may decide that the potential long term risks that may arise from the use of an unethical technique may not be a risk that is worth taking may affect the company’s reputation;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not all candidates registered the fact that this site would need to be secure and therefore would use the HTTPS protocol.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>As this was a 4-mark question a brief definition type response would be unlikely to score highly. Most commented on the fact that protocols are needed to ensure that communication takes place, but few were able to provide the specific detail that an \"explain\" question demands.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Schools need to be aware that the candidates are expected to be practiced in the use of web software such as php, JavaScript and CSS. Answers did not show a high level of understanding even though some gained marks through answering quite generally in part (b).</p>\n<div class=\"question_part_label\">b.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A specific web element was expected here — many answered with on-line chat.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were more comfortable with this topic and provided some good discussions identifying both the short-term gains and long-term consequences of black hat SEO.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.7",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-2-searching-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A business’s computer system needs to be updated.</p>\n</div><div class=\"specification\">\n<p>The new system is now ready for implementation.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> stakeholder to be considered when planning the new system.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> consequence of not including all stakeholders in the design of the new system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> appropriate techniques to gather the information needed to find a suitable solution for the updated system.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason testing should take place at every stage of the development process.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate <strong>two</strong> methods the business could use to implement the new system. Include the benefits and drawbacks of each.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>End users/ employees/ customers/ community members/ media/ suppliers;<br/>Business owners/ managers/ shareholders/ investors;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>User dissatisfaction;<br/>because the system does not meet user requirements;</p>\n<p>Developers not being paid for the final product;<br/>as the business owner requests are not evident in the final product/ or outside of the project's scope;</p>\n<p>Unsuccessful final product;<br/>the developed system may either solve a different problem/ is not user friendly as compared to the existing system;</p>\n<p><em>Note to examiners: Reward other reasonable responses</em>.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Examining current systems (using interviews/ surveys/direct observation);<br/>To compare the existing system against possible requirements to identify missing features;</p>\n<p>Examining competing products;<br/>To compare own system with competitors to enable decisions on features to add;</p>\n<p>Review of organizational capabilities;<br/>To determine how well the organization manages resources to gain an advantage over competitors;</p>\n<p>Literature searches;<br/>To research current methods and to help inform development choices;</p>\n<p><em>Mark as 2 and 2</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Testing is important (at every stage) to make sure the system operates in line with user requirements/as intended;<br/>To prevent the end user being dissatisfied with the final system;</p>\n<p>Testing is important to enable early discovery of errors;<br/>to reduce time delay/ using more resources / avoid higher cost;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for method, award <strong>[1]</strong> for benefit and award <strong>[1]</strong> for drawback</em>.</p>\n<p>Parallel running;<br/>The old and new systems run together, so if a problem is found with the new system, it can be repaired/the old system can take over;<br/>This is expensive as duplicate systems and staff are needed;</p>\n<p>Pilot running;<br/>The new system is only implemented in one branch of the organization so disruption is kept to a minimum;<br/>It can take a long time for the new system to be fully implemented / two systems are still in operation within the organization, leading to duplication and possible errors;</p>\n<p>Direct changeover;<br/>The new system is implemented overnight so the changes happen very quickly;<br/>If the new system fails, the company has no working system to fall back on;</p>\n<p>Phased conversion;<br/>Only one area/department/part of the system is updated at a time, so the disruption is kept to a minimum;<br/>Multiple systems which may not be compatible with each other will be running at the same time;</p>\n<p><em>Note to examiners: Accept other suitable examples of benefits and drawbacks</em>.</p>\n<p><em>Mark as 3 and 3</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates identified a range of correct stakeholders who should be considered when planning a new business computer system. </p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>In general, they then correctly continued and outlined a consequence of not including all stakeholders in the design of the new system. If marks were lost, it was most likely due to not expanding on the consequence they had named, and then, had gone on to name one or more additional consequences.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates mostly misinterpreted this question as requiring methods of data collection, so talked about surveys, interviews or observations, when the question was asking about techniques to gather information, which meant, for example, examining current systems, examining competing products or literature searches. A small number of candidates did identify appropriate techniques.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Virtually all candidates were able to talk about the reasons for testing, but the question specifically asked about the reason for testing at every stage of the development process. Many good answers were seen, but there were also some answers that gave generic benefits of testing. In addition, candidates were asked to outline one reason, so candidates who gave simple answers covering more than one reason lost out on the expansion mark.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates who chose to evaluate the direct and parallel running methods of implementing a new system often scored high marks and generally scored higher marks than those who chose to write about other methods. Candidates often mixed up the meaning of pilot and phased implementation, so therefore didn’t get all the available marks if they chose either of these methods.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "22M.1.SL.TZ0.12",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics",
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Consider the following two-dimensional array, <code>MAT</code>, with dimensions 6 × 6.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAATUAAACqCAYAAADMWNgmAAAUxklEQVR4nO3dfXBUVZoG8KebBBlMIptZRzsoUBBj/NraSXoAF8tJZyhTYyhxkhWmsExDKIUtS3HdDhlg1FVnQCZdIOIHlEUkiK7opEsWsAxIEi1hTTbNun4RQtg1kqSlUDeEFvlI7tk/uhM6353k3txzbj+/qv4nCaffNG/evvd0931sQggBIiKLsJtdABGRnjjUiMhSONSIyFI41IjIUjjUiMhSONSIyFI41IjIUjjUiMhSONSIyFI41IjIUjjUiMhSLDbUgvB7XbDZbLDZXPD6g72+fxGB2pfgTrHBZkuBa/UBBLThrjFWtQJawI/dpcVwpXrh7+h/lQ6/F6k2G2w2G1K9fgzwY6M2+P1oCDZUwOu+LfT7pLjhPdCAPr9Rhx/e1NAatkF+Jx2KHfR+tIAfu//qDfeBDSnul1AbuNhrEXn6IFw1gv6NcNlS4fad7PNdOfrgJHzu1PDv0nXrp16D+8BiQw0ApqCk7iyEqIInMyHi6xqC/lJsbp6H11oFhGjGnvyj+OP2evScawnI9FRBiLOoK5liUq0X0eIrxoNvNCJx9lzkThp4hbhMDxqFwKW6EgzyY7qYVFKHS0Kg0ZOJuMhvtFfj2bvfxLiH96NTCHT6H8K4ncuwwtfU87GNy4SnUUBcqkOJ8cWi7pKAaPQgM7JYrR7bFz+Fwz/eieeaBYQ4g+r5LShevBMNPYqVoQ8iBOuwxfM+phSk9/ttKfoAADAP5a2XIIQI3xpRlnd972IN7QMLDrWB/IDa8jbMnXt9+Je2IyHzd8g9tAvV7X0O10w2HpPz1mObZyGy05LNLmYIHQh84MOnq1dhxUwH7ADsjjlYseYBNO/245TZ5fVmT0dhxV6sd8+Eww4ASUjL+yc8cu1n+OKUUcc3o9UG/5Z12Jf7JNbMTzW7GOnFzlDraMKR/XG4amLkr/w3mHrT/+DI8XOmlaW+ODjyXkRFYXqvZvo5ct2zcI1JVUXvIgKVpdgadydmXdP3uMN8GoL+1+DZdye8y3+FRLPLUUDsDDUaM1qgEpv2JeK+rMkSN1jXXtY0LKqYjrV/mofJMhYbrMMWzxuY/kgefpkgY4G97UV+Snx4Py0FruLX4e+zX2ksFR4lUogWqMSmt4Dfr8gOn97JKrxn1umH9+ajeGHzh/28aGS2i2jZvwNbpq/GM/dOVeCP9XrklTVG7Kc1Y88SDS8/vRctY/jYyv84Geo8zpz+yewiLEOdgRbB7kCmeyUeTd6J56u/M7uaXk6hZvf7OFGaj+vG2WCzxSMlfxN25Lv6fQVUPnYkpGcj96ePUDOG+5WqtN7oxU1Fxm9Oo+l0xIOrfYevv0xHxg0TzavLEjQE63dhwztXqDXQuk1AYvLPzC6iH72PfC6htXwFCsqr+r6iKCvtR7R9n4RJV45dUyjXfiOXjJlLbkXNK1Xh04yLaHn3NRzKW4CZSTH0MBhAC1Tj1cOpWP7onMsDLeCD2+1DwNTK+hHwYanbC58/EH67STsafM9g2es3YsFM2V9pltl5NJTeD1dxKSob2kNfCjbgwIZ1WHtLFpxj+DcWQ3/NdiSkL8RjWYexaJwNNlsmVjfdgz8tvhUDvDPIRBFvyIx3oshfBGe88W+sHJmTePcPD+HxpU4kRr7pMiUfO8wurT+Oe7FpTQba3l6McTYbbLZ0LKu5Cd43H0amzBvxAR/cUp9+TkBa4SvYmgNULEsP9UBiPnZiEd57IgtJY1iJjK9hG2g8HNlPoUo8ZXYhQwhvYnvMriMa4VOkMrPriJYdCWnZKFyfjcL1ZtcyDI48lAkBuR/mJKRlF2J9diHMfGglfmoaqW9Q5EwcxUdbuo6SEuEs+kb36noaba2XP7YS7yxCm87V9dZW5ET8aI4Wuz4eE+9EkfHFho5uR/wxHPbBQGTvAxtzP4nISix4pEZEsYxDjYgshUONiCxFiVc/bTab2SUQkUQGeylAiaEGDP5LyMZmsylTL2s1hkq1AmrVO9RBDk8/ichSONSIyFI41IjIUjjUiMhSoh5qQ6bVaAHUbnQjxWaDzZaD1ZUt6HNduLFKEyKimDWsI7WBU2Ta4N9UhuYFr6JVCAixC/lHXsD2hvM9FxjLNKE+OhDwLe8V3xW+yXiJHADRRfrJQwscwsaumDzX06gc48s4D0s0T8LSYB8Mhz6nn+1HUH56JuZOHh/+wiRk3j8Lh7YdRrsud6CXCSgo/ybionsCnce2IffaSbjS7NL6iDbSTxLBWmza/B0WvPZ56LHd81sc+eO/9Yqdk0WUT8JSYB8Mly5DreP4EexPvgo9rh979VTcdPAIjktzihkHR97zva4Yeh6NH9filpy/G9PrPUVHpUg/De21lTg9d87l8JKEDNyf+xW2SXeJbCj0JAywD4Yvtl8o0L7Gx75fIMcp4RVPlYr0O4fjR44i+aoJEV+Lw9VTr8bBI02SXdRSlSfhMPbBsMX0UNMaD2PXpWm4TuYrnhLRsBj313zuDE5fMGx1HZxH48cHgYX/gFTONCLL0OXPOe6GDPzmaBNOR3xN+/ZrfHl3Bm6Q9tOlp/HFh3FYeMc0hQ5XVYr003DuzA+Q+nktkvRPwpHYB4PR5+85yYklcz7FK10vi2tNeHfDZ8hb4pRwAz6s/Thqvv817kidMPTPmkGpSL+JuCFjOo42/V/E1y7i269/wN0ZU6W7aoJST8Lsg2HT6SAlCemLlyHrg8JQQs91T6KpsBiL02UdaRra66rxZZ7Mp54qRfrZkTQzH3NqdnW/J0lreQ8bDt2OJTLGzin1JMw+GDYRpUt1JSKzpE5civYfDL6YKMksEXVRLjaMMqN0WhxcuUxsO/aTzuuG6FfvBdF68F9FFiCAW0XBho9Fa6dOS4fp+dh2tu4Xq7IcAoBwFLwoalov6La2EDr3QWezOLjqLgFAwFEgNtS0Cj0fWn17ln0Qaahaow5e6fB7ke4swgkAM0rqUN/nUwVRLQJvuhNFoUVQV+9BZhSLqHStJ0CtelmrMVSqFVCr3qFqVSJNSqUHHFCrXtZqDJVqBdSqd6haZTspJyIaFQ41IrIUDjUishRl9tSIiLoMNrZke6vhgBSYvd2stOkqE9ZqHJXqZZoUEcUUDjUishQONSKyFA41IrIUXdOk/LtfRbErB15/cKBFzE+TYuCGYcwO3BgW9oFhzO4DXdKktBYflj/4Bv43MQM5udcOvICpaVIAAzcMJEHgRvTYB4aRoA90Of20T87Dlm0e/GP2jbhKjwWNwsANg8gRuBE19oFB5OiDmNpTY+CGUeQI3IgW+8AocvRBTA01IrI+DjUishQONQZuGITBK8ZhHwwmpoYaAzeMIkfgRrTYB0aRow9iaqgxcMMokgRuRIt9YBBJ+iDasIPBglcu1ZWIGUAoxCLyNmOAcBUzg1cMDtwQgsErYPAKg1dUCV6ZXeXCJyMJXOm7GLyzq+D6hMErZmOtxlCpVkCtenXNKGgrciJ+oI9JRaPrY1LxThS1jWQBIqLBKXPlWwXK7KZSvazVGCrVCqhVL9OkiCimcKgRkaVwqBGRpSizp0ZE1GWwsSXb+6cHpMDs7WalTVeZsFbjqFQv06SIKKZwqBGRpXCoEZGl6BS8oiHYUAFvV9hCihveAw3oE78iQ/AKEVmaLsEraK/Gs3e/iXEP70enEOj0P4RxO5dhha+pZziE6cErYIqQUbQA/Lvf7PHEtrE2IO9jq1QftKPBtxouW7gPin1oCMpbrVJpUv3rQOADHz5dvQorZjpgB2B3zMGKNQ+gebcfp0Z/BzpiipAxzqNhuweew51wPeeHEJ04Wz0f3xZ7JH1sVeuDbXhk92Ssb70AIVpx8LFr8P6r76NFvkawSppUHBx5L6KiML3XYj9HrnsWrhn9HeiHKUIGmYC0wjdQtf4BZDrGA7AjIe0ePPbI1fjwi9ND/usxp1QfnMOxqv/A37sXYqYjVK/dMQv3Tf0KFY2yDWELp0lpgUps2peI+7ImS/VKBFOExo4WqMLmrcD8WVI9rQFQrA8G1CzhE4ZF06S0QCU2vQX8fkU2HDJNNBoTXS8ojVv0AW5cW4x7u4+GaGQm4kbX7fi0bBdqAxdDe5e+zfBu/W983/ajhFsR5tN17HCgWYxWj9KclNCGb59bCnJK++7vxWV60CguoNV7K756oRTVY7xJbD12JGQuxeb5LShOuQK2cXfhhSYnHvTcjqYfONT6o9Po0RCs34UN71yh3kBjitDA7OkorGgNbfj2ubX2s4/aZTwcmffjiUcnYv3zH0u4T9UPqfsgCWl5a1ElBIT4HGX//CvYT7YgY/ovFPico6JpUlqgGq8eTsXyR+dcHmgBH9xuHwJ63IFOmCI0luyYmHgV4s0uox9K9UF/gp/h33dNhXvuZLMr6cUyaVIn8e4fHsLjS51IjDw9ScnHjtEvri+mCBnkJHxLl8Pr84dr1RBs8GHVsj3IWpAh32OrVB/0pAUOYePDXvxQvAxZ0vWBhdKkho1pUlFSKEXo7DFxcNvKcK0QyFopttdJnNCkTJpUrx4oeUscPHZGp7Uv0/OxVSpNKt1ZhBMAZpTUoX4kqVIdfnjTnSgKLYK6eqZJmY21GkOlWgG16h2qVmUuEqlAmd1Uqpe1GkOlWgG16mXwChHFFA41IrIUDjUishRl9tSIiLoMNrZUeKshAAavGIW1GkOlWgG16mXwChHFFA41IrIUDjUishQONSKyFJ3SpC4i4H8Pf/V2BVncBvfGQ31DQpgmRUQG0yVNSmvYicWeD/Gj6zk0CwFx9h3M/3YdFvcOCWGa1DAplCYF81OEhoV9YBiz+0CX0097WiEqqtbDnRlKk0JCOvIeK8S1H37FNKkRUylNClKkCEWPfWAYCfrAmD01rQWVm99C3PxMpkmNmEppUnKkCEWNfWAQOfpA36HWtWc2rhAVN3rwp3unSvVKhFIpQkqlScmRIhQt9oFR5OgDfWdOeM+ss/VZ3PxVKTZXy7xPQURWZMiBlN0xE+4nCpG8fquEh8hEZGXGnR1OTEKyjKkbvUmdItTbGKdJjcrYpwiNCvvAIEqmSXUg4PsXuL0++Lteug3Ww7fKg9ez5kkVEqJUipBSaVJypAhFi31gFDn6QIeJEwdH3lNYk9GGtxdNC703JXEFam5+Em8+PhMJo78D/SiVIqRSmpQkKULRYh8YRJI+iDbBhWlS0dOvXoXSpIT5KULDokyalBDsg56GqpVpUgZQqV7WagyVagXUqpdpUiZQqV7WagyVagXUqpdpUkQUUzjUiMhSONSIyFKU2VMjIuoy2NiS7a2GA1Jg9naz0qarTFircVSql2lSRBRTONSIyFI41IjIUjjUiMhSdEqTihCshdeVApvbh0DfRcxPk2LghmG0gB+7S4vhUiEpjH1gGKWCVwZKk7qsDf4t67BvigtZ/X3b9DQpBm4Y4yJafMV48I1GJM6ei1yzksKixj4wjLWCVzQE/a/Bs+9OeNf8DlP0W1g/DNwwyHhMzluPbZ6FyE6T8FJDvbEPDGK14JVgHbZ4PkKudwkyE+XcqmPgBgHsA+PIEbyi05tv2+Df8gw2TC/Ef/5yEiQL+ySiGKLLIZXWUomXt0zDi8/Mu3zYSerT6lGakxLa8O1zS0FOqaT7OhTTdDhS68Cpmv0oPbEVpde91OM7O1CO1rI8OEZ/J8Zh4MbA7OkorGhF4djdo3nYBwZRMnglDo68LaFXOrpureUoKJBvoDFwgwD2gXEsE7yiEAZuEMA+MIwlg1e+EeUFM0JhFoBAQblo7X8xBq9ERZXAjbOiriTr8v97xG2GXmE9Qq0+0LVWZfogRKngldlVLnwyksCVvovBO7sKrk8YvGI21moMlWoF1KpX14yCtiIn4of6mNRguj4mFe9EUdtIFiAiGpwyV75VoMxuKtXLWo2hUq2AWvUyTYqIYgqHGhFZCocaEVmKMntqRERdBhtbsr1/ekAKzN5uVtp0lQlrNY5K9TJNiohiCocaEVkKhxoRWYpOwSsn4XOn9rreVircvpO9FzE/eIWILE3H4JV5KG+9FHEJokaU5V3f80dMD16BYilCAKAh2FCJ0uKc8JPFbVjqa5KyZrNThIZFmT7oQMC3vO9FOvtLa5OE2aliMXb6qVKKEABoCNa/jocfqQByNuOsEBDic2zLmyrff5wEKULRU60PJqCg/Jue1yyU7FqFIXKkikn3t2EopVKEEAqzWdcM9/ZnUZidhgSz6xmQHClCUVOtD5QhR6qYjkNtL/JT4ruvX+8qfh1+yU4/lEoRgob22r2o/nU+shzjh/5xU8mRIhQttfqAhkunoXY98soaIw6Pm7FniYaXn96LFilPP1RwDsePNCHrVjvquvZ+UtzwHmhA0OzSaIydx478KZf301zFKPMHJN0DNJ9Bp592JKRnI/enj1Bzik99kbSGUuT0m85kg822AKXd+zrnceb0t/ivl/6MrXgI/k4B0boZ95w9gLf9vBid6qLvg34yQPY8gM6Xn8e7LXKdCcnCuD017Ue0fZ+ESVdKvm03xilC9rRCVEQ2aI/b2yhMizyF+x5VcfPxzIo5cNgBIAlptztQW3VCulO6vsY+RWhUpO6DXhLS8dvcC9hdw4Dd/ugwcc6jofR+uIpLUdkQ3mYNNuDAhnVYe0sWnBKFQyiVIoRkOHPuwsS/nYSegffjkJx8pWSv8MiRIhQttfqgPx0423Ye1076mdmFSEmHv40JSCt8BVtzgIpl6aHD58R87MQivPdEllzpPEqlCNmRlLUMf/luC54L16sFalH2/EdIvWOaZENNkhShaCnUB1pDKXJcxSit7NpLbUfDgVfw57XJyHFK+NjKINoEl+jSpKJejGlSUepsrRHbV4brxV1iZflRcVa31a2VIjQsyqRJdYqzxw6Kbd09AOEo2CD2Hzuj0/oh+tQrR6rYsNKk0p1FOAFgRkkd6keSKtXhhzfdiaLQIqirZ5qU2VirMVSqFVCr3qFqVeYikQqU2U2lelmrMVSqFVCrXgavEFFM4VAjIkvhUCMiS1FmT42IqMtgY0uJtxoqMHeJSBI8/SQiS+FQIyJL4VAjIkvhUCMiS+FQIyJL4VAjIkvhUCMiS+FQIyJL4VAjIkvhUCMiS/l/+2i6KBCc/F4AAAAASUVORK5CYII=\"/></p>\n<p style=\"text-align: left;\">The value <code>−1</code> is stored in <code>MAT</code> at position <code>[4][2]</code>. The position <code>[4][2]</code> means row 4 and column 2.</p>\n</div><div class=\"specification\">\n<p>A two-dimensional array in which most of the elements are zero is called a <strong>sparse matrix</strong>. A sparse matrix can be compressed by storing only non-zero elements using three one‑dimensional arrays.</p>\n<p><strong>The first array</strong>, <code>VALUES</code>, stores all non-zero elements taken from the sparse matrix in row‑major order (left-to-right then top-to-bottom order).</p>\n<p>The length of the array <code>VALUES</code> is equal to the number of non-zero elements in the sparse matrix. For the sparse matrix above, <code>MAT</code>, the array <code>VALUES</code> is:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">The <strong>second array</strong> is <code>ROWC</code>. <code>ROWC[i]</code> stores the number of non-zero elements, from row <code>0</code> to row <code>i</code> of the sparse matrix, <strong>inclusive</strong>.</p>\n<p style=\"text-align: left;\">The length of <code>ROWC</code> is equal to the number of rows in the sparse matrix. For <code>MAT</code> the array <code>ROWC</code> is:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n<p style=\"text-align: left;\">For example, <code>ROWC[2]</code> stores <code>3</code> because in <code>MAT</code> there are three non-zero elements from row <code>0</code> to row <code>2</code>, inclusive.</p>\n<p style=\"text-align: left;\">The <strong>third array</strong>, <code>COL</code>, stores the column index for each non-zero element in the sparse matrix. <code>COL[i]</code> stores the sparse matrix column index for the non-zero element stored in <code>VALUES[i]</code>. For <code>MAT</code> the array <code>COL</code> is:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>Consider the following three arrays. They hold the compressed contents of a 7 × 7 sparse matrix, <math style=\"font-family: 'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>BIGMAT</mi></math>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the total number of elements stored in <code>MAT</code>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the number of non-zero elements in <code>MAT</code>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State how many rows in <code>BIGMAT</code> contain only zeros.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the index in <code>VALUES</code> of the first non-zero element in row <code>5</code> of <code>BIGMAT</code>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">f.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the index in <math style=\"font-family:'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>VALUES</mi></math> of the first non-zero element in row <math style=\"font-family:'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mn>5</mn></math> of <math style=\"font-family:'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>BIGMAT</mi><mo>.</mo><mo> </mo></math></p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">f.i.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>36;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>7;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>1;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>5;</p>\n<div class=\"question_part_label\">f.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>5;</p>\n<div class=\"question_part_label\">f.i.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.i.</div>\n</div>",
    "question_id": "17N.1.HL.TZ0.14",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A teacher would like a simple program to store the names, marks and grades of students in a set of three parallel one-dimensional arrays called <code>NAME[]</code>, <code>MARK[]</code> and <code>GRADE[]</code> .</p>\n<p>The grade boundaries for the individual grades are shown below:</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n<p>The class has 30 students.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> components in a conditional statement.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm using pseudocode to take the marks that have been stored in <code>MARK[]</code>, convert them into the appropriate grade and store the calculated grades in <code>GRADE[]</code>.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the name, mark and grade in the three arrays correspond to the same student.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm using pseudocode to output the names and grades of all students who achieve a grade of Merit or Distinction.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how you would change your algorithm in <strong>part (d)</strong> to allow a user to choose a grade and output the names and marks of the students who have achieved this grade.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><code>if;</code><br/><code>then;</code><br/><code>else;</code></p>\n<p><em>Note to examiners: allow an alternative descriptive version such as</em>:<br/>test/condition;<br/>action/consequence;<br/>(optional) alternative action/consequence;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for an appropriate loop with correct loop parameters to cover 30 array elements/all students</em><br/><em>Award <strong>[1]</strong> for correct use of indexes in two arrays (<math style=\"font-family:'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>MARK</mi></math> <code>and</code> <math style=\"font-family:'Courier New';\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>GRADE</mi></math>)</em><br/><em>Award <strong>[1]</strong> for each if statement with correct condition and grade assignment up to <strong>[4]</strong>.</em></p>\n<p><em>Note to examiners: Award <strong>[4]</strong> if candidate has correctly used an alternative conditional statement such as switch/ case.</em></p>\n<p><strong><em>Example answer 1</em></strong>:</p>\n<pre>loop COUNTER from 0 to 29<br/>    if MARK[COUNTER] &gt;= 80<br/>      then GRADE[COUNTER] = \"Distinction\"<br/>      else<br/>        if MARK[COUNTER]&gt;˝= 60<br/>          then GRADE[COUNTER] = \"Merit\"<br/>          else<br/>            if MARK[COUNTER} &gt;= 40<br/>               then GRADE[COUNTER] = \"Pass\"<br/>               else<br/>                    GRADE[COUNTER] = \"Fail\"<br/>           end if<br/>       end if<br/>   end if<br/>end loop</pre>\n<p><em><strong>Example answer 2</strong></em>:</p>\n<pre>COUNTER = 1<br/>loop while COUNTER &lt;= 30<br/>    if MARK[COUNTER-1] &gt;= 80<br/>      then GRADE[COUNTER-1] = \"Distinction\"<br/>    end if<br/>    if MARK[COUNTER-1] &gt;= 60 and MARK[COUNTER-1] &lt; 80<br/>          then GRADE[COUNTER-1] = \"Merit\"<br/>    end if<br/>    if MARK[COUNTER-1} &gt;= 40 and MARK[COUNTER-1] &lt; 60<br/>            then GRADE[COUNTER-1] = \"Pass\"<br/>    end if<br/>    if MARK[COUNTER-1} &lt; 40<br/>               GRADE[COUNTER-1] = \"Fail\"<br/>    end if<br/>    COUNTER = COUNTER + 1<br/>end loop</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Three arrays are parallel/ they have the same number of elements/ the same length;<br/>the same array index can be used to represent name, grade and mark of the same student/ the array index makes sure that data from the three arrays lines up;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for correct loop to check all students</em><br/><em>Award <strong>[1]</strong> for correct conditional statement checking correct array</em><br/><em>Award <strong>[1]</strong> for correct output</em></p>\n<p><strong><em>Example answer 1</em></strong>:</p>\n<pre>loop COUNTER from 0 to 29<br/>    if MARK[COUNTER] &gt;= 60 then<br/>      output NAME[COUNTER], GRADE[COUNTER]<br/>    end if<br/>end loop</pre>\n<p><strong><em>Example answer 2</em></strong>:</p>\n<pre>loop C from 0 to 29<br/>  if GRADE[C].equals(“Merit”)OR GRADE[C].equals(“Distinction”)<br/>       then<br/>          output NAME[C], GRADE[C]<br/>    end if<br/>end loop</pre>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for an input statement before the loop;</em><br/><em>Award <strong>[1]</strong> for changing the conditional statement so that it checks the </em><code>GRADE[]</code><em> array for the </em><code>GRADE</code><em> input (using the same variable)</em><br/><em>Award <strong>[1]</strong> for outputting the name and marks of the student who has achieved the inputted grade</em></p>\n<p><em>Note to examiners: Accept a written explanation or an amended algorithm that corresponds to candidate’s answer to part(d).</em></p>\n<p><em><strong>Example 1</strong></em>:</p>\n<pre>G=input()<br/>COUNTER = 0<br/>loop while COUNTER &lt; 30<br/>    if GRADE[COUNTER] = G<br/>      then<br/>           output(NAME[COUNTER], MARK[COUNTER])<br/>    end if<br/>    COUNTER = COUNTER + 1<br/>end loop</pre>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates who gave an example of a conditional statement did enough to get both marks for this question. Other candidates who described the make-up of a conditional statement, were also successful.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were able to demonstrate the construction of an algorithm to take marks that were stored, convert them into a grade and then store the calculated grades. However, fully correct pseudocode solutions were rare. Errors that were seen included incorrect loop constructs; loops not covering the whole data set; incorrect use of array indexing for both retrieval and storage; incorrect data types – the data to be stored was of the string data type, but this was not always shown; incorrect use of assignment.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates recognised that a particular student’s data used the same index in each of the three arrays, therefore indicating that the data in each array corresponded to the same student. Relatively few candidates identified the fact that this was because they were parallel arrays.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Another algorithm was required here with some of the same problems as seen in question 13b, however, most candidates did achieve marks here, mostly for the correct use of conditional statements.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates were asked to explain how they would change their algorithm in question 13d to achieve some additional functionality. Most candidates did achieve some marks here for adding an input, changing the conditional statement and/or changing the output.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "22M.1.SL.TZ0.13",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-1-general-principles",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A school maintains a database of students’ details and teaching resources on a central server. This data can be accessed by all teachers in the school.</p>\n<p>Teachers may need to edit resources when preparing their lessons.</p>\n</div><div class=\"specification\">\n<p>When storing student details, data security is an important consideration.</p>\n</div><div class=\"specification\">\n<p>The school has appointed a database administrator (DBA).</p>\n<p>A DBA is required to carry out tasks such as ensuring there is a strategy to recover the database if it becomes corrupted and that the data is shared ethically.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how concurrent use of the school database is possible in this situation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> ways that data security in the school's database can be maintained.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>one</strong> strategy that could be used to ensure the data can be recovered if the database becomes corrupted.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Suggest how the privacy of student data can be ensured.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The ability of a database to allow multiple teachers to affect multiple transactions;<br/>Allowing concurrent processing while ensuring transaction isolation;<br/>Thus, ensuring the update of one teacher does not affect the update of another teacher;<br/>While one transaction (by a teacher) is accessing a resource from a shared folder, it places a lock, an access restriction, on the resource, controlling the level of access allowed by another transaction by another teacher;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for identifying a way of maintaining data security and <strong>[1]</strong> for a development up to <strong>[2 max]</strong></em><br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em></p>\n<p>Minimizing permissions;<br/>Restrict users to have permission only to the means to do their job. Some can view, modify and insert some only view etc.;</p>\n<p>Auditing changes;<br/>Log changes made to teachers and permissions through auditing. This gives a trail to follow should you have problems. Without authorization no one gets grant of permissions;</p>\n<p>Minimizing table access;<br/>Isolate the teachers from the data tables they do not need / Create views and user defined functions to support user access requirements and not give access to the tables;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for identifying a way of maintaining data security and <strong>[1]</strong> for a development up to <strong>[2 max]</strong></em>.</p>\n<p>Deferred update;<br/>does not physically update the database on disk until a transaction has reached its commit point/if a transaction fails before reaching its commit point, it will not have changed the database in any way so UNDO is not required;</p>\n<p>Shadow paging;<br/>When a page is to be modified, a shadow page is allocated in which changes are made;<br/>When it is ready to become durable, all pages that refer to original are updated to refer new replacement page;</p>\n<p>Back-up;<br/>Back-up copies of the entire database is done to ensure the database is at the most updated version of the original;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for the method, award <strong>[1]</strong> for the explanation and <strong>[1]</strong> for the example up to <strong>[3 max]</strong></em>.</p>\n<p>Data masking or data obfuscation;<br/>is the process of hiding original data with random characters;<br/>e.g.: suppressing certain characters in the student address, student id etc.;</p>\n<p>Data encryption;<br/>Conversion of data into non-readable gibberish creates highly secure results such as scrambling the student_ID;<br/>The only way to gain access to the data is to unlock it with a key or password which only those authorized can access; </p>\n<div class=\"question_part_label\">c.ii.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many students gave generic answers for isolation in this section. Few of the students gave very precise explanations as well.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students were able to do this question.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A majority of candidates were able to identify at least one way of maintaining data consistency but did not relate to the recovery part of the answer.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Methods of anonymising data was a challenge for most students.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.2",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-1-basic-concepts",
      "a-2-the-relational-database-model",
      "a-3-further-aspects-of-database-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The IB Coordinator of AB World Academy introduces the Extended Essay to the Grade 11 students in January by researching the difference between primary and secondary data on the internet.</p>\n<p>Some of the students used the Google search engine (Google.com) and others used the Ask search engine (Ask.com). These search engines gave results.</p>\n</div><div class=\"specification\">\n<p>Google uses the PageRank algorithm and Ask uses the HITS algorithm.</p>\n</div><div class=\"specification\">\n<p>The IB coordinator uploaded the assignments onto a cloud-based Learning Management Platform.</p>\n</div><div class=\"specification\">\n<p>As part of their research, students downloaded images from the internet. Most of the downloaded JPG images were compressed using lossy compression.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>search engine</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between the principles of these two algorithms.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the difference between cloud computing and local client-server architecture.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the alternative type of compression to lossy.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the advantages and disadvantages for students of using compressed images in their IB Coursework.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>A PNG image uses open standards.</p>\n<p>Distinguish between interoperability and open standards.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Software that interrogates a database of web pages;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em><strong>PageRank algorithm</strong> <strong>[2 max]</strong></em><br/>PageRank works by counting the number and quality of in links of a page to determine a rough estimate of how important the website is;<br/>The assumption is that more important websites are likely to receive more links from other websites;<br/>Pages are given a score (rank) / counts links per pages;</p>\n<p><strong><em>HITS algorithm [2 max]</em></strong><br/>Based on authorities and hubs;<br/>Authorities: A page is called an authority, if it contains valuable information and if it is truly relevant for the search query. It is assumed that such a page has a high number of in-links;<br/>Hubs: These are pages that are relevant for finding authorities. They contain useful links towards them. It is therefore assumed that these pages have a high number of out-links;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><em><strong>Local client-server architecture [1 max]</strong></em><br/>The server is the central communicator between clients (e.g. email/chat server)/allows different clients to access and manipulate data;</p>\n<p><em><strong>Cloud computing [1 max]</strong></em><br/>Cloud computing puts the focus on sharing computing resources over the internet;</p>\n<p><em><strong>Differences between the two [1 max]</strong></em><br/>Cloud computing is often offered as a service to individuals and companies whereas local client server architecture is based at an organizational level;<br/>Cloud computing can scale up or down depending on current demands more easily than local client server networks;<br/>Client / server networks are more secure than the cloud as the data transmission is carried out locally;<br/>Client / server networks have lower levels of latency than the cloud as the data transmission is carried out locally;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Lossless (Compression);</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><strong><em>Advantages of using compression [2 max]</em></strong><br/>Allows for more rapid upload/download as the compressed file is smaller than the original;<br/>Scrolling through the coursework may be quicker;</p>\n<p><em><strong>Disadvantages of using compression [2 max]</strong></em><br/>This may mean that the overall quality of the work is reduced;<br/>This may be an issue when printing the work / or the quality of the image is a key contributor to the overall quality of the work;</p>\n<p><em><strong>Overall comment [1 max]</strong></em><br/>The impact of using compression may be dependent on the context in which the work is compressed;</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Interoperability means a computer program can communicate and exchange information across a range of platforms;<br/>Open standards are publically and free standards that enable interoperability;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>All candidates are familiar with search engines but not all were able to put together a precise definition that including both the role of the user and processes involved.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a memory recall question and dependent on the schools' coverage of the course.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates did not answer this well being unable to clearly distinguish between the two types of architecture. They seemed particularly unaware of the difference in scale.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a familiar topic and was answered well. Full marks went to candidates who related their answers to the specific scenario.</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a familiar topic and was answered well. Full marks went to candidates who related their answers to the specific scenario.</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Another memory recall question at was answered quite well.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.8",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-2-searching-the-web",
      "c-4-the-evolving-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Outline what is meant by the term computer network.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[1]</strong> for identifying the nature of a network and <strong>[1]</strong> for a development of the first point up to <strong>[2 max]</strong></em>.</p>\n<p>A group of computers and other computing hardware devices that are linked together through communication channels/cables/wirelessly;<br/>To enable communication (sharing files, sharing information) between systems/among a wide range of users;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18N.1.SL.TZ0.1",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by a database management system.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of using beta testing prior to the release of a new product.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying a feature of a database management system and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong></em>.</p>\n<p>Database management system is an application software;<br/>that allows users to define/create/maintain a database / provides (controlled) access to the data;<br/>Examples of database applications are computerized library, inventory, flight reservation, etc.;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> of using beta testing and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong></em>.</p>\n<p>Beta testing is done from the user/client perspective;<br/>So, requirement mismatches can be easily identified;<br/>The real users have an opportunity to test a new product before it is (publicly) released;<br/>So, user acceptance is assured;<br/>Any bugs identified could be (easily) fixed before the public release of the product;<br/>So, quality of the product is enhanced;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18N.1.SL.TZ0.2",
    "topics": [
      "topic-2-computer-organization",
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "2-1-computer-organization",
      "1-1-systems-in-organizations"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The array <code>DATA_ARR[]</code> is a one-dimensional array of 12 integers.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><strong>Algorithm 1</strong> represents a method of searching the array <code>DATA_ARR[]</code> to see if it contains a specific value.</p>\n<p><strong>Algorithm 1</strong></p>\n<pre>input TO_FIND<br/>LIMIT = 11<br/>LOC = FALSE<br/>ITERATE = 0<br/>loop while not LOC and ITERATE &lt;= LIMIT<br/>    if DATA_ARR[ITERATE] = TO_FIND then<br/>        LOC = TRUE<br/>    end if<br/>    ITERATE = ITERATE + 1<br/>end loop<br/>if LOC then<br/>    output TO_FIND, \"is in the array\"<br/>else<br/>    output TO_FIND, \"is NOT in the array\"<br/>end if</pre>\n</div><div class=\"specification\">\n<p><strong>Algorithm 2</strong> represents an alternative method of searching the array <code>DATA_ARR[]</code> to see if it contains a specific value.</p>\n<p><strong>Algorithm 2</strong></p>\n<pre>input TO_FIND<br/>LOC = FALSE<br/>LOW_LIM = 0<br/>UP_LIM = 11<br/>loop while LOW_LIM &lt;= UP_LIM and LOC = FALSE<br/>    MID_VAL = (LOW_LIM + UP_LIM) div 2<br/>        if DATA_ARR[MID_VAL] = TO_FIND then<br/>            LOC = TRUE<br/>        else<br/>            if TO_FIND &lt; DATA_ARR[MID_VAL] then<br/>                UP_LIM = MID_VAL - 1<br/>            else<br/>                LOW_LIM = MID_VAL + 1<br/>            end if<br/>        end if<br/>end loop<br/>if LOC = TRUE then<br/>    output TO_FIND, \"is in the array\"<br/>else<br/>    output TO_FIND, \"is NOT in the array\"<br/>end if</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the trace table for <strong>Algorithm 1</strong> using <code>TO_FIND = 39</code>.</p>\n<p>The first value of the first row has been done for you.</p>\n<p style=\"text-align:left;\"><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the constant used in <strong>Algorithm 1</strong>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the trace table for <strong>Algorithm 2</strong> using <code>TO_FIND = 50</code>.</p>\n<p>The first two values of the first row have been done for you.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why <code>MID_VAL</code> could not be a constant.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Evaluate the use of a sequential search and a binary search including the advantages and disadvantages of each.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for </em><code>TO_FIND</code><em> and </em><code>LOC</code><em> columns;</em><br/><em>Award <strong>[1]</strong> for </em><code>ITERATE</code><em> column;</em><br/><em>Award <strong>[1]</strong> for </em><code>DATA_ARR[ITERATE]</code><em> column; </em><em><br/>Award <strong>[1]</strong> for the correct output;</em></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/><code>LIMIT</code></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for </em><code>LOW_LIM</code> column;<br/><em>Award <strong>[1]</strong> for </em><code>UP_LIM</code> column;<br/><em>Award <strong>[1]</strong> for </em><code>MID_VAL</code> and <code>DATA_ARR[MID_VAL]</code> columns;<br/><em>Award <strong>[1]</strong> for </em><code>TO_FIND</code><em>, </em><code>LOC</code> and Output columns;</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>The value (of <code>MID_VAL</code>) changes during the operation of the algorithm;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.</p>\n<p><em>Award <strong>[3 max]</strong> for sequential search</em><br/>The algorithm searches through every element in the array starting at the beginning and working through one after the other;<br/>…until the required item is found;<br/>This method is potentially slow if the data set is large and the required element is towards the end of the list;<br/>The data does not need to be in any particular order;</p>\n<p><em>Award <strong>[3 max]</strong> for binary search</em><br/>The data must be sorted before it is stored;<br/>After comparing the data set with the target data, half of the data can be discounted using a simple condition;<br/>…enabling the target to be more quickly located;</p>\n<p><em>Note to examiners: Big O notation is not on the syllabus so answers with reference to this are not expected, however, if this type of answer is seen, please allow, for example</em>:</p>\n<p>Sequential: main disadvantage is O(N) so inefficient;<br/>advantage is that it works with an unsorted array;<br/>Binary search: main advantage is that it’s efficient as it’s O(log n);<br/>disadvantage is that the array must be sorted (and so must be sortable – not all data has a defined sort order);<br/>Binary search cannot be performed on a linked list but a sequential search can;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally constructed the correct trace table for this question; however, marks were often lost by not providing the correct quantity of iterations for the trace, or by making errors in how the output would actually appear if the program ran.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates correctly identified LIMIT as the constant in the algorithm.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally performed less well on the trace for the second algorithm (binary search), with similar errors to the trace carried out in part (a). Also, as this algorithm required more processing, additional errors included incorrect calculations of the variables, leading to incorrect values being displayed in the trace table.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates gave an appropriate reason why the variable MID_VAL could not be a constant in the given algorithm.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Virtually all candidates were able to evaluate the use of a sequential and a binary search, with many of these candidates achieving high or full marks. One point that was often missing form candidates’ responses, however, which is fundamental to the topic, was the need for the data to be sorted/ordered for a binary search to work.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "22M.1.SL.TZ0.14",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Armour Hardware Company</em> has the following data about salespersons and the quantities of items sold.</p>\n<p>Each salesperson can sell many different products.</p>\n<p style=\"text-align: center;\"><strong>SALES_PERSON</strong></p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> reasons why databases are normalized.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why the <strong>SALES_PERSON</strong> table is not in 1st Normal Form (1NF).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the 3rd Normal Form (3NF) of the unnormalized relation shown above.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why it is necessary to ensure that referential integrity is maintained in databases.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why a primary key may consist of more than one attribute.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Applications interacting with the database are minimally affected;<br/>When a fully normalized database structure is extended, to accommodate new types of data, database structure can remain largely or entirely unchanged;<br/>So, the applications interacting with the database are minimally affected;</p>\n<p>Key dependent;<br/>Every non-key column in every table is directly dependent on the key, the whole key and nothing but the key;<br/>Thus making reduced redundancies, lesser anomalies and better efficiencies;<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>For First Normal Form, each attribute value should be atomic;<br/>In the given example product_Num, Pro_Name, Unit_Price and Qty is multivalued / All 4 tuples shown have multiple values in their first 4 attributes;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[8 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for each correct table up to <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for each correct primary key up to <strong>[4 max]</strong></em>.</p>\n<p><strong>3NF</strong><br/>Product_Number<br/>Product_Number, Unit_Price, Product_Name</p>\n<p>Sales_Person<br/>Sales_Person_Number, Sales_Person_Name, Manager_Number</p>\n<p>Manager<br/>Manager_Number, Manager_Name</p>\n<p>Purchases<br/>Purchase_ID, Date_And_Time, Product_Number, Sales_Person_Number</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>You can enter foreign keys that do not match the corresponding primary key in the related table;<br/>This could cause a lot of problems such as mismatched customer data and mismatched transaction records;</p>\n<p>Cascading update;<br/>If the primary key for a record in the Managers table changes, all corresponding records in the Employees table are modified;</p>\n<p>Cascading delete;<br/>If a record in the Managers table is deleted, all corresponding records in the Employees table are deleted;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The combination of the two provides unique identifiers for the records of the table;<br/>And that there is no single attribute that is able to uniquely identify a record;<br/>In a manner that will not lead to potential duplication of records;<br/>It identifies exactly one record of the table, then that record shows the single value of each of the non-key attributes;<br/>That is associated with the unique combination of the key attributes;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students were clear about normalisation in removing redundancy but only a few could identify reasons of key dependencies and interaction of the applications with the database.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students were able to identify why the data was not in First Normal Form but some of the students could not use the terms ‘atomic’ or ‘multi-valued’.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students are able to correctly split the tables but only a few identified the foreign keys. The primary keys were in most cases identified.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many students could not explain the referential integrity.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most students understood the composite keys and the need for them.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.3",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Direct observation is a technique used by a system analyst to determine user requirements for updating a computer system.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> advantage of direct observation.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> disadvantage of direct observation. </p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for an advantage up to <strong>[1 max]</strong></em>.</p>\n<p>Direct observation is systematic/structured process;<br/>Direct observation allows that current computer system can be studied in its natural setting;<br/>Direct observation provides a better understanding of the way computer system is used;<br/>etc.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for a disadvantage up to <strong>[1 max]</strong></em>.</p>\n<p>Direct observation is susceptible to observer bias;<br/>Direct observation also can affect the behavior of users/process being observed;<br/>Direct observation is time consuming;<br/>etc.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18N.1.SL.TZ0.3",
    "topics": [
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The growth of the Internet has led to increased levels of collaboration between different IB World Schools.</p>\n</div><div class=\"specification\">\n<p>The World World Web consisted of over 1350 million web pages in 2017. Each web page is accessed by typing its uniform resource locator (URL) into the address bar of the browser.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why a distributed network such as the World Wide Web may act as a catalyst to increase the collaboration between different IB World Schools.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> function of a web browser.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how a domain name server (DNS) functions.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the use of a distributed network, such as the World Wide Web, may lead to copyright and intellectual property issues.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The Internet is growing exponentially meaning that more and more nodes (in this case IB World Schools) can be added;<br/>This has led to greater connectivity between the different nodes;<br/>However, the number of hops required to transmit information from one node to another is growing linearly;<br/>Therefore, each node can access the increased number of nodes with no discernible latency which makes increased levels of collaboration between these nodes possible and desirable;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Browser Retrieves a Web Page;<br/>The web browser retrieves (or fetches) code, usually written in HTML (HyperText Markup Language) and other computer languages, from a web server;<br/>Then, it interprets this code and displays it as a web page for you to view;</p>\n<p>Browser acts as a bridge between URL and DNS;<br/>The user inputs the URL / website domain name on web browser’s address bar;<br/>The web browser passes this website domain name to a domain name server;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>User type the domain name into the URL search area on the web browser and press “Enter” on the keyboard.</p>\n<p>The domain name is intercepted by a “Domain Name Server” or DNS.</p>\n<p>The main function of the DNS is to look up in its database the domain name you have typed and find the matching IP address.</p>\n<p>It then forwards the request onwards, using this address.</p>\n<p>If it cannot find the IP address in its own database, it then contacts other Domain Name Servers until it finds it, or if it can't find it anywhere, it displays a “web site not found” message to you.</p>\n<p>When the request reaches the destination, the pages are sent back.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em><strong>Copyright issues [3 max]</strong></em><br/>Copyright issues may occur because of the ease of access to materials on the World Wide Web;<br/>When material is accessed on the World Wide Web it may breach copyright rules in a number of ways (such as copying / publishing/ public display), often without the user realizing they have done so;<br/>It may also be a potential issue of the user is not aware of the complicated and extensive copyright laws;<br/>The continuing technical evolution of the web may mean that new practices emerge that may inadvertently breach existing copyright laws;</p>\n<p><em><strong>IP [3 max]</strong></em><br/>Potential IP issues may occur with web sites for a number of reasons such as being linked to copyright, trademarks, patents;<br/>When material is downloaded it may breach IP rights;<br/>When material is modified without author’s permission;<br/>These IP rights may vary between countries which leads to some acts being an IP issue on one country, but not in another;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>There were a lot of vague responses with few homing in on the fact that the \"distributed\" nature of the web was the key element.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Being an \"explain\" question the answers required knowledge of \"how\" or \"why\". Not that many explained the importance of the translation feature of a browser.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates identified some of the steps involved but omitted to say that the IP address would be returned to the original browser.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This wasn't particularly well-answered with few understanding the differences between intellectual property and copyright issues. Many alluded to the problems of identification but few included the idea that regulations differ from country to country.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.9",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-3-distributed-approaches-to-the-web",
      "c-1-creating-the-web",
      "c-4-the-evolving-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a logic diagram for the following expression.</p>\n<p style=\"text-align:center;\">NOT A OR (A AND B) </p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for a logic diagram representing A OR B with 2 inputs, 1 output and 3 logic gates.</em><br/><em>Award <strong>[1]</strong> for the OR gate having 2 inputs, one of which is NOT A.</em><br/><em>Award <strong>[1]</strong> for another input to the OR gate, which is A AND B.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18N.1.SL.TZ0.4",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An international company is in the process of moving its Head Office from Europe to Asia.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> possible compatibility issues as a part of data migration.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how a virtual private network (VPN) will allow employees who are in Europe to communicate with the Head Office in Asia.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> social issue associated with this process.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each possible compatibility issue identified up to <strong>[2 max]</strong></em>.</p>\n<p>Language differences/different character set;<br/>Different conventions of representing various data/currencies, dates, etc.;<br/>Incompatible software/incompatible hardware;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying how VPN will allow employees to communicate and <strong>[1]</strong>for a development up to <strong>[2 max]</strong></em>.</p>\n<p>VPN allows secure communication with the Head Office in Asia;<br/>A VPN is the company’s private network that uses a public network (in this situation the Internet) to connect remote sites/employees together;<br/>Privacy is protected using VPN tunneling;<br/>VPN uses encrypted connections routed through the Internet from the company's private network (Europe)to the remote site in Asia (or employee);<br/>Hiding IP addresses to prevent unwanted exposure and data leaks;<br/>Data security is ensured by encryption – anyone intercepting the encrypted data cannot read it;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying an issue and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong></em>.</p>\n<p>Employees who will not be willing to move to Asia;<br/>Lost jobs / finding new job / income decreased;</p>\n<p>Employees who will move to Asia;<br/>Personal or family issues during the period of moving or in a new environment / finances and cost of living / services such as schools, hospitals, transport / language problems;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "18N.1.SL.TZ0.5",
    "topics": [
      "topic-1-system-fundamentals",
      "topic-3-networks"
    ],
    "subtopics": [
      "1-1-systems-in-organizations",
      "3-1-networks",
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The rise in global temperatures has led to the melting of the sea ice in polar regions. Scientists have developed a number of computer models that can be used to make predictions about the rate of sea ice melting in these polar regions and its effect on coastal areas globally. In order to create a computer model a number of variables are identified.</p>\n<p>A computer model of the effects of the melting of the sea ice in the Arctic Ocean may include the following variables:</p>\n<ul>\n<li>average ocean surface temperature (°C)</li>\n<li>albedo of the ocean (the proportion of light reflected from the ocean surface)</li>\n<li>precipitation (mm)</li>\n<li>salinity of the ocean (grammes of salt in one kilogramme of water)</li>\n<li>area of sea ice (km<sup>2</sup>).</li>\n</ul>\n</div><div class=\"specification\">\n<p>In this model the following rules have been determined:</p>\n<ul>\n<li>for every 0.01 °C increase in ocean surface temperature, the area of sea ice decreases by 1 %</li>\n<li>for every 1 % decrease in the area of the sea ice, the sea level rises by 20 mm.</li>\n</ul>\n<p>The initial values are:</p>\n<ul>\n<li>area of sea ice = 1 000 000 km<sup>2</sup></li>\n<li>average surface temperature of the ocean is 0.00 °C.</li>\n</ul>\n</div><div class=\"specification\">\n<p>The scientists observed when running the model numerous times using historical data there were significant differences between observed and expected results.</p>\n<p>A second model was developed that included new variables and rules.</p>\n<p>The surface of the ocean reflects the heat from the sun. The ratio between the area covered by the sea ice and the area where there is no sea ice (open ocean) affects the value of the average albedo. The lower the albedo, the quicker the sea ice will melt.</p>\n<p>The average albedo is calculated using this formula:</p>\n<p>Average albedo =</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mfrac><mrow><mo>(</mo><mi>area</mi><mo> </mo><mi>of</mi><mo> </mo><mi>sea</mi><mo> </mo><mi>ice</mi><mo> </mo><mo>×</mo><mo> </mo><mi>albedo</mi><mo> </mo><mi>of</mi><mo> </mo><mi>sea</mi><mo> </mo><mi>ice</mi><mo>)</mo><mo> </mo><mo>+</mo><mo> </mo><mo>(</mo><mi>area</mi><mo> </mo><mi>of</mi><mo> </mo><mi>open</mi><mo> </mo><mi>ocean</mi><mo> </mo><mo>×</mo><mo> </mo><mi>albedo</mi><mo> </mo><mi>of</mi><mo> </mo><mi>open</mi><mo> </mo><mi>ocean</mi><mo>)</mo></mrow><mrow><mo>(</mo><mi>area</mi><mo> </mo><mi>of</mi><mo> </mo><mi>sea</mi><mo> </mo><mi>ice</mi><mo> </mo><mo>+</mo><mo> </mo><mi>area</mi><mo> </mo><mi>of</mi><mo> </mo><mi>open</mi><mo> </mo><mi>ocean</mi><mo>)</mo></mrow></mfrac></math></p>\n<p><strong>Note</strong>:</p>\n<ul>\n<li>area of sea ice = 1 000 000 km<sup>2</sup></li>\n<li>area of open ocean = 1 000 000 km<sup>2</sup></li>\n<li>albedo of sea ice = 0.6</li>\n<li>albedo of open ocean = 0.1.</li>\n</ul>\n<p>The average albedo will change with every iteration of the model. Each iteration is 2 years after the previous.</p>\n<p>The rules for the model are:</p>\n<ul>\n<li>the initial albedo is 0.35</li>\n<li>the rate of decrease in sea ice every 2 years is <math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mfrac><mrow><mn>0</mn><mo>.</mo><mn>3</mn></mrow><mrow><mi>average</mi><mo> </mo><msup><mi>albedo</mi><mn>2</mn></msup></mrow></mfrac></math></li>\n<li>the starting year is 2019</li>\n<li>the sample rate is every 2 years.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the following table showing each variable’s data type and a suitable range of values that would represent the information shown above.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using the rules and initial values above, construct the pseudocode that would enable the area of the sea ice and the sea level rise to be calculated if there was an increase of 0.04 °C in the ocean surface temperature.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using the information above state the area of the sea ice.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using the information above state the change in sea level.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using the formula, rules and initial data given above, construct the pseudocode that would calculate the year that the area of sea ice will be less than 10 000 km<sup>2</sup>.</p>\n<div class=\"marks\">[7]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> ways that this model could be implemented.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the accuracy of the simulation in predicting the area of the sea ice is critical.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Initialization – Original area of sea ice;<br/>Initialization – Surface temperature change;<br/>New sea ice area calculation;<br/>Sea level change calculation;</p>\n<p><em>Example algorithm</em>:</p>\n<pre>OriginalSeaIceArea = 1000000<br/>SurfaceTempChange = 0.04<br/>NewSeaIceArea = OriginalSeaIceArea * (1 – SurfaceTempChange)<br/>SeaLevelChange = -(NewSeaIceArea - OriginalSeaIceArea) / OriginalSeaIceArea * 100 * 20</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>960 000 (km<sup>2</sup>);</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>80 (mm);</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[7 max]</strong></em>. <br/>Initialization – Initial areas of Ice and water, and starting year;<br/>Use of loop;<br/>Loop parameters (limits, condition, increment, end statement correct);<br/>Average albedo;<br/>Rate of decrease calculation;<br/>Area of ice update calculation;<br/>Area of open water update calculation;<br/>Output;</p>\n<p><em>Example algorithm</em>:</p>\n<pre>IceArea = 1000000<br/>OpenArea = 1000000<br/>Year = 2019<br/>Loop While IceArea &gt; 10000<br/>   TotalArea = IceArea + OpenArea<br/>   AveAlbedo = ((IceArea * 0.6) + (OpenArea * 0.1)) / (IceArea + OpenArea)<br/>   Decrease = 0.3 / (AveAlbedo * AveAlbedo)<br/>   // allow Decrease = 0.3 / AveAlbedo ^ 2<br/>   IceArea = IceArea * (1 − (0.01 * Decrease))<br/>   OpenArea = TotalArea − IceArea<br/>   Year = Year + 2<br/>End loop<br/>Output \"Date when there less than 10 000km2 of ice in the ocean <br/>is \" Year</pre>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/> Spreadsheets;<br/>IDEs using code;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>If the calculation of the rate of reduction of the sea ice is inaccurate the calculation of the amount of sea ice remaining;<br/>At a given time will turn out to be wrong;<br/>So, work to correct the problems may be too little/too great;<br/>The year in which the sea ice is predicted to be 10 000 km<sup>2</sup>;<br/>Will be wrong;<br/>So, ice coverage may be more/less than expected in the calculated year;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates recognized this as a straightforward question. However, some candidates were unable to demonstrate the concept of data types and range of values for given variables.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was reasonably well answered and most candidates were able to produce some pseudocode. However, a few candidates struggled to apply all the required formulae and couldn’t produce all the required values.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was answered correctly by most of the candidates.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was answered correctly by most of the candidates.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of candidates could not produce the correct pseudocode to calculate all the required values.</p>\n<p>The skill to apply a loop in this question was poorly demonstrated by most of the candidates.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was not well understood by the majority of the candidates. Most of the responses were generic and off course.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates did not answer the question appropriately and focused more on generic points rather than being specific to the scenario.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.4",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-1-the-basic-model",
      "b-2-simulations"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>State <strong>three</strong> operating system resource management techniques.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em><br/>scheduling;<br/>policies;<br/>multitasking;<br/>virtual memory;<br/>paging;<br/>interrupt;<br/>polling;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>A well answered question.</p>\n</div>",
    "question_id": "22M.1.HL.TZ0.8",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>To improve customer satisfaction, a supermarket chain wants to create an object-oriented program (OOP) to simulate the lines of customers at the check-outs in their point of sale (POS) system.</p>\n<p>This point of sale (POS) system consists of several check-out counters. After filling their shopping carts with items, customers line up at one of the check-out counters. In most cases, they wait in line until it is their turn to pay.</p>\n<p>Three real-world objects are implemented using the following classes:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>The UML diagram for the class <code>POSline</code> is provided below.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the relationship between the <code>POSsystem</code> and <code>POSline</code> objects.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Draw a diagram to show the relationship between the objects <code>POSsystem</code>, <code>POSline</code> and <code>Cart</code>. You are not required to draw a complete UML diagram.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>identifier</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between a class and an instantiation. You must make reference to the UML provided.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the code fragment that instantiates an array <code>line</code> of 20 <code>Cart</code> objects.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>public void joinLine(Cart newCart)</code> that adds a <code>newCart</code> to <code>line</code> in the next empty <code>array</code> location. You may assume that <code>line</code> is not full.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the method <code>public Cart leaveLine(int n)</code> that removes the cart at position <code>n</code> from <code>line</code> by shifting down all the array entries bigger than <code>n</code>, before returning the indicated <code>Cart</code> object. You may assume that a cart exists at position <code>n</code>.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>Aggregation;<br/>POSsystem has a (many) POSline(s);<br/><em>Allow 1 to many</em>;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for one POSsystem connecting to multiple POSlines;</em><br/><em>Award <strong>[1]</strong> for one POSline connecting to multiple Carts;</em></p>\n<p><em>Example answer</em>:</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p> </p>\n<p><em>Allow diagram showing aggregation for both (diamond at POSsystem end or 1 to many)</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>The (unique) name of a program element (variable / method / class);<br/><em>Must have “name” or equivalent</em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <em>[1 mark each] for definitions of a class and an object and [1 mark] </em><em>for an example relating to the UML diagram that involves both class and object for </em><em>either the POSline or the Cart classes</em>.</p>\n<p>The class is the blueprint / template of an object / defines the actions and properties of an object / an abstract representation of an object;<br/>An instantiation is the (creation of an) actual object / example of an object / object filled with data / assigned space in memory;</p>\n<p>The class <code>POSline</code> stores instantiated objects of the <code>Cart</code> class;<br/>The class <code>POSline</code> uses the constructor to set the (object’s) variables;<br/>An instance of <code>POSline</code> has values for id, active and line;<br/><em>Allow similar example for the Cart object</em>;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <em>  <br/>Award <strong>[1]</strong> for using <strong><code>new</code></strong>;<br/>Award <strong>[1]</strong> for <code>Cart[20]</code>;<br/>Allow with or without </em><code>private</code><em>.<br/></em></p>\n<p><code>private Cart[] line = <strong>new</strong> Cart[20];</code></p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <em>  <br/></em></p>\n<p>Award <strong>[1]</strong> for correct loop construction (for a while loop this includes initialization and incrementation);<br/>Award <strong>[1]</strong> for correct condition (can be within the loop construct);<br/>Award<strong> [1]</strong> for correct assignment;<em><br/></em></p>\n<pre>public void joinLine(Cart newCart){<br/>   int i=0;<br/>   while (this.line[i] != null) {i++;}<br/>   this.line[i] = newCart;<br/>}</pre>\n<p><em>Notes: Do not penalize the absence of the keyword “this” in any question.</em><br/><em>With for loops, allow variations of line.length() – mark the logic</em></p>\n<p><em>Further examples:</em><br/><em>Note: if the answer takes size of array into account then the given condition <span style=\"text-decoration:underline;\">must</span> be correct</em>.</p>\n<pre>public void joinLine(Cart newCart)<br/>{<br/>   int i=0;<br/>   boolean placed = false;<br/>   while (i&lt;20 &amp;&amp; placed = false)<br/>   {<br/>      if this.line[i]= null;<br/>      {<br/>          this.line[i] = newCart;<br/>          placed = true;<br/>      }<br/>      i = i+1;<br/>   {<br/>}<br/><br/>public void joinLine(Cart newCart)<br/>{<br/>   for (int i; i&lt;20; i++)<br/>   {<br/>     if (this.line[i] == null)<br/>     {<br/>        this.line[i] = newCart;<br/>        break;<br/>       }<br/>     {<br/>}</pre>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <em>  <br/></em></p>\n<p><em>Award <strong>[1]</strong> for declaring, initialising and returning a </em><code>Cart</code><em> variable;<br/>Award <strong>[1]</strong> for correct loop (including initialisation and incrementation);<br/>Award <strong>[1]</strong> for assigning / shifting;<br/>Award <strong>[1]</strong> for assigning </em><code>line[19]</code><em> to null;</em></p>\n<pre>public Cart leaveLine(int n){        // given in the stem<br/>   Cart result = this.line[n];<br/>   for (int i=n; i&lt;19; i++){<br/>      this.line[i] = this.line[i+1];<br/>   }<br/>   this.line[19] = null;<br/>   return result;<br/>}</pre>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was well-answered.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Those who answered part (a) correctly tended to answer this well. Most used uml diagrams to show the relationships either with words or by using the correct arrows.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Generally well-answered.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was one of several questions in which reference to the scenario was required. This is to reward the candidates' understanding as opposed to simple memory recall. Not all candidates managed to make this connection.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>These 3 question parts tended to divide the candidates into those who were comfortable with OOP construction as those who weren't. To a certain extent this seemed to be school dependent.</p>\n<p>Straight-forward for those competent with Java.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>These 3 question parts tended to divide the candidates into those who were comfortable with OOP construction as those who weren't. To a certain extent this seemed to be school dependent.</p>\n<p>Generally well-answered, helped partly by the assumption that the array was not full. It was particularly pleasing to see the answers that included a while loop as candidates seem to be reluctant to use this type of loop construction even when logically it makes sense to do so.</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>These 3 question parts tended to divide the candidates into those who were comfortable with OOP construction as those who weren't. To a certain extent this seemed to be school dependent.</p>\n<p>Few managed to achieve full marks as four separate operations were required. The assignment statement was linked to the loop conditions there were interesting variations - but is was essential not to try to access an array index that was out of bounds and therefore did not exist. Many omitted setting the last value to null.</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.10",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-1-objects-as-a-programming-concept",
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> characteristic of a queue.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> application of a queue.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>A linear (abstract) data structure;<br/>First in First out/FIFO;<br/>Elements can only be added at one end(rear/tail) and removed from the other (front/head);<br/>Once a new element is inserted into the queue (enqueue), all the elements inserted before the new element in the queue must be removed (dequeue), to remove the new element;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>Print queue;<br/>CPU task scheduling;<br/>Simulation of a real-life scenario (call centre, supermarket queue);<br/>Playlist queue;<br/>Interrupt queues;<br/>Queues in routers and switches;<br/>Mail queues;<br/>Buffers in devices like keyboard;<br/>Website traffic;</p>\n<p><em>Note to examiners: Reward other reasonable responses</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates correctly answered this question.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates correctly answered this question.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "22M.1.HL.TZ0.9",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Describe how data is transmitted by packet switching.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p>Packet switching entails data being separated into specially formatted units (packets);<br/>Each packet contains data and information such as packet number, address that identifies the sending computer and intended recipient, etc.;<br/>Packets are routed from source to destination using (different) network switches and routers;<br/>Using these addresses, network switches and routers determine how best to transfer the packet on the path to its destination;<br/>Packets are reassembled at the destination (using packet numbers);<br/>If any of packets is missing it should be retransmitted;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18N.1.SL.TZ0.6",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Construct a trace table for the following algorithm</p>\n<pre>A = 3<br/>B = 7<br/>loop while B &gt;= A<br/>  A = A + 1<br/>  output(B − A)<br/>  B = B − 1<br/>end loop</pre>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for a trace table with at least three columns</em>.<br/><em>Award <strong>[1]</strong> for each correct column (out of the four columns − A, B, B &gt;= A, output)</em>.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18N.1.SL.TZ0.7",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Define the term child in relation to a binary tree.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Child (is either null or) is a node that has up to two references /links to other nodes;<br/>and has (only) one predecessor (parent) node;</p>\n<p>“Child” is every node in a binary tree which is descendant/ the node which has a link from its predecessor (the node which is a predecessor of any node is called as parent node);<br/>The child node can have up to two descendants (child nodes);<br/>(All the nodes except the node which is the origin of the binary tree data structure (called root) are child nodes;)</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>This question generated responses that were a little vague.</p>\n</div>",
    "question_id": "22M.1.HL.TZ0.10",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between random access memory (RAM) and read only memory (ROM).</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the function of an operating system in managing primary memory.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the roles of the data bus and the address bus in the machine instruction cycle.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State how the data stored in the byte will be represented in hexadecimal.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State how many integers could be represented in this byte.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why this byte could not be used to represent characters such as those used in Chinese.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a truth table with two input variables. If the input variables are equal the value of the output variable should be True, otherwise it should be False.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p>RAM acts as temporary storage of data, instructions and programs currently running (for the operating system and for the running applications) whilst ROM is permanent memory (stores the instructions and data that won't change/stores the instructions that the computer needs in order to boot up;<br/>Memory access, both read and write operations are performed on RAM whilst ROM works with read only operation;<br/>If power failures happened during access to RAM then all data will be permanently lost/RAM is volatile memory/whilst if power failure happened during the ROM access no data will be lost/ROM is non-volatile memory;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for the answer saying that the function of OS in primary memory management is allocation of specific memory blocks to individual programs and <strong>[1]</strong> for reallocation up to <strong>[2 max]</strong></em>.</p>\n<p>A part of the OS (memory manager) assigns that block of memory to the program when a running program requests a block of memory;<br/>When the program no longer needs the data in previously allocated memory blocks, they become available for reassignment;</p>\n<p>OS ensures the availability of adequate memory for data structures/objects of each running program at all times;<br/>By allocating the memory portions to programs after freeing the space (of the computer memory);</p>\n<p>OS (memory management unit) uses virtual memory which provides secondary memory (external storage) for program that does not have enough space in RAM for execution;<br/>After execution of the program this memory is reallocated (used by other programs)/freed;</p>\n<p><em>Note to examiners: Award only <strong>[1]</strong> an answer such as “OS maintains file allocation table”</em>.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p>Bus is defined as a system that transfers data between hardware components/data bus and address bus enable a processor to communicate with the primary memory;<br/>When the computer processor needs to fetch an instruction from the memory it uses the address bus to specify the (physical) address (of the memory block it needs to access);<br/>It will get the data from (the specific) memory (block) (after checking the address bus to get the read address);<br/>And then it will place this data on to the data bus/data bus carries the data;<br/>When the processor wants to store results of execution to the memory it will set the write address on the address bus;<br/>And put the data/results/to be written to memory on to the data bus (to carry this data);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>5E;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>2<sup>8</sup>/256;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying why this byte could not be used to represent characters such as those in Chinese and <strong>[1]</strong> for an expansion up to <strong>[2 max]</strong></em>.</p>\n<p>The characters must be represented as numbers so that computer can deal with them;<br/>One byte (gives us the ability to represent only 256 characters) is enough to hold every possible character in a language which uses a limited set of text symbols, punctuation marks and special characters (for example, English, Spanish, etc.);<br/>Chinese exceeds the 256 character limit and therefore requires more bytes to represent all of the characters in this language;</p>\n<div class=\"question_part_label\">c.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for all four input combinations (A,B)</em>.<br/><em>Award <strong>[1]</strong> for all four correct output values (C)</em>.<br/><em><strong>Note</strong>: 1 == True, 0 == False</em>.</p>\n<p style=\"text-align:center;\"><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.1.SL.TZ0.8",
    "topics": [
      "topic-2-computer-organization"
    ],
    "subtopics": [
      "2-1-computer-organization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Cars have many automated features to improve the driving experience. One example is the use of headlights that automatically switch on and off.</p>\n</div><div class=\"specification\">\n<p>Some cars have adaptive cruise control. This uses RADAR technology and a processor to ensure the car stays the same distance away from the car in front.</p>\n</div><div class=\"specification\">\n<p>A system with many functions operating independently could be managed using either centralized or decentralized processing.</p>\n</div><div class=\"specification\">\n<p>As vehicles become fully autonomous (self-driving), ethical considerations must be taken into account when designing software.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> input device used by the automated headlights.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the interaction between the inputs, processing and the outputs of the feedback loop ensure the car stays the same distance away from the car in front.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Discuss the use of centralized and decentralized processing in the context of control systems such as those involved in the operation of motor vehicles.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> ethical concern that must be considered in the development of autonomous vehicles.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em><br/>photoelectric sensor;<br/>light sensor;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em><br/>Sensor (continuously) collecting data (related to the distance between the two vehicles);<br/>Data is converted to digital using ADC…;<br/>…and sent to the processor;<br/>Processor has access to pre-set data (minimum distance required between the two cars);<br/>Processor compares the input data against stored/pre-set data;<br/>If the vehicle is too close, the processor sends a signal to an actuator to apply the brakes/ an output signal (for example, warning light on the dashboard/ audible warning) to the driver to apply break;<br/>If the car has fallen back from the car in front, the processor sends a signal to an actuator to apply acceleration/ an output signal to the driver to apply acceleration;<br/>This process is constantly looped (feedback loop);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em><br/><em>Award <strong>[2 max]</strong> for centralised processing</em><br/>Centralized processing would allow the different features of the car to be controlled by a single processor;<br/>… this means that different features of the car are less likely to impact on the operation of other features, as the central processor can coordinate them;</p>\n<p><em>Award <strong>[2 max]</strong> for decentralised / distributed processing</em><br/>Decentralized processing allows each of the car’s features to have its own processor;<br/>… enabling each feature to function independently/more quickly;</p>\n<p><em>Award <strong>[1 max]</strong> for concluding statement</em><br/>Centralized processing is more complex for the manufacturer to program, but is possibly safer as the different components coordinate;<br/>Centralised processing is more expensive to achieve as the whole system needs to be coordinated in real time;<br/>Decentralised processing has a faster response (time due to the use of multiple smaller PLCs);<br/>Decentralised processing is simpler/cheaper for the car manufacturer as they can use standard parts that are already programmed, but it is more difficult to get them to coordinate with each other;<br/>Decentralised processing allows scalability, the subsystems can be easily customised;<br/>A crash in a centralized system would result in failure of the whole system / a critical system failure so would have far greater consequences than in a decentralized system where the system may be able to partially function hence are riskier than decentralized ones;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em><br/>Autonomous vehicles can be seen as more dangerous to pedestrians/cyclists;<br/>…as they have to be programmed to react to avoid unpredictable movements from them;<br/>The ethical consideration is how might the processor decide whether to avoid the cyclist to hit a pedestrian instead (trolley problem);</p>\n<p>When autonomous vehicle control systems are perfected;<br/>… they should be safer than human drivers;<br/>… it may then be an ethical consideration as to whether a human who cannot drive should be the responsible person in the autonomous vehicle?;</p>\n<p>An autonomous driving system could collect detailed information about the movements of the car;<br/>… this information could reveal where individuals go, and raise privacy concerns;<br/>… which could be exploited for commercial gain/ be considered as a form of surveillance (an ethical consideration);</p>\n<p>There may be times when an accident is unavoidable (the trolley problem);<br/>The autonomous vehicle has to make a ‘decision’ about who to hit;<br/>In this case is the driver, the manufacturer, the software engineer or the third party accountable?;</p>\n<p><em>Note to examiners: accept other reasonable answers</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was reasonably answered except by weaker candidates</p>\n<p>Some candidates failed to achieve full marks as result of incomplete and vague answers.</p>\n<p>Some candidates demonstrated poor understanding of control systems.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many responses to part (b) were verbose while missing crucial information that would award the mark.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "22M.1.HL.TZ0.13",
    "topics": [
      "topic-7-control"
    ],
    "subtopics": [
      "topic-7-1-control"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Many health agencies are using simulations in an attempt to understand how their resources could be used in the future. With many countries experiencing aging populations, health agencies have worked with computer scientists to develop simulations that will enable them to manage their resources more effectively.</p>\n<p>One of the key features of these simulations is the development of “what-if” models.</p>\n</div><div class=\"specification\">\n<p>The following variables can be considered as part of a model to be used to simulate the management of an aging population:</p>\n<ul>\n<li>quality of health education</li>\n<li>lifestyle choices such as smoking</li>\n<li>residential region.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the main features of a “what-if” model.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> other variables that could be included in this model.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the ethical issues that may arise from the collection of information for this model.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the model would be converted to a simulation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Data is entered into a rule-based environment;<br/>The rules may be kept the same and the nature of the data input may be varied;<br/>Or the data that is input may remain the same and the rules may be varied;<br/>Which allows for a range of possible scenarios to be investigated;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Life expectancy;<br/>Gender;<br/>Availability of life-extending treatments;<br/>Genetic information;</p>\n<p><em>Allow other appropriate answers</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.</p>\n<p><em>Example answer</em>:<br/>One ethical issue is invasion of privacy;<br/>to get the best quality model;<br/>sensitive personal data will need to be collected;<br/>trade-off between the benefits of this model and level of intrusion into people's lives;<br/>especially if the data is being shared with third parties;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Simulation may be used to handle uncertainty and provide ranges of expected outputs; e.g. repeatedly inputting data drawn from random samples of plausible input values;<br/>To look at the predicted spread of outcomes over time (consider the use of simulation in the analysis of queues);</p>\n<p>A simulation is considered to be a representation of a model over time;<br/>This means that the simulation can be used over a longer period of the development life cycle;<br/>It can be used to assist with stages such as the implementation stage;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates did not address the specific points related to a “what-if” model and provided a generic description of the model.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was relatively well done. Most candidates were able to identify the correct set of variables.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates were able to identify and explain suitable ethical issues. A relatively small number of candidates had difficulty in developing their responses beyond generic points.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most responses tended to be generic. Candidates were unable to explain why the model would be converted to a simulation.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.5",
    "topics": [
      "option-b-modelling-and-simulation",
      "topic-1-system-fundamentals"
    ],
    "subtopics": [
      "b-2-simulations",
      "b-1-the-basic-model",
      "1-2-system-design-basics"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The supermarket chain wants to use this OOP simulation to experiment with different ways of organizing their check-out system. For example, it is possible to have different check-out counters such as cash-only or card only, or to have check-out counters for ten items or fewer.</p>\n</div><div class=\"specification\">\n<p>It is also possible to have one line that serves a number of check-out counters.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>inheritance</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>one</strong> advantage of the OOP feature “inheritance” with reference to this scenario.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> advantages of using libraries of classes.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> reasons why the use of multiple programming teams in different locations may be problematic when developing an integrated software solution.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <em> </em><br/>A new class is derived from an existing class;<br/>The new class inherits all <span style=\"text-decoration:underline;\">variables/data/properties and methods/behaviours</span> of the other class;<br/>The derived class is called a subclass/child, and the original is called a superclass/parent; </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <em> </em><br/><em>Award <strong>[1]</strong> for an advantage;</em><br/><em>Award <strong>[1]</strong> for some elaboration;</em><br/><em>Award <strong>[1]</strong> for a reference to the context;</em></p>\n<p>Note: to a certain extent mixing and matching can take place</p>\n<p>It promotes code reuse;<br/>because the superclass <code>POSline</code>;<br/>holds common data and actions that are shared by all newly developed classes;</p>\n<p>It reduces maintenance overhead;<br/>because you only have to update the superclass;<br/>in this case <code>POSline</code>;</p>\n<p>allows extensibility / ability to create other classes easily;<br/>specific types of check-out can be created;<br/>reducing development time / costs / testing;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <em> </em><br/>Saves development time;<br/>Since classes and their methods do not need to be rewritten;</p>\n<p>Promotes abstraction;<br/>Because reusable code exists that functions without knowledge of internal working;</p>\n<p>Libraries contain error-free / robust code;<br/>because it has been used and tested many times;</p>\n<p>Promotes efficiency / organization;<br/>As code will be shorter / easier to read / develop;</p>\n<p>Familiarity with libraries;<br/>Allow for easier maintenance / modification;</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <em>Mark as 4 separate points</em>.<br/>Teams may be located in different countries;<br/>therefore have communication issues;<br/>due to different languages;<br/>or different time zones;<br/>inability to discuss face-to-face;<br/>Problems with different conventions (e.g. date format);<br/>Managing the teams in different locations may be problematic (allow example);<br/>Teams may not collaborate well due to personality issues;<br/>Development time might increase;<br/>Due to time lags between communications;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>15(a)-(e) dealt with more general aspects of OOP. Candidates should look carefully at the wording in each question as to whether an explanation or description is required and at the number of examples/reasons etc. that are requested, as these aspects affect the way they should answer.</p>\n<p>Part (a) was a straight-forward definition.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>(b) asked for an explanation related to the scenario described. It should be clear from this type of question that full marks cannot be achieved if the scenario is not considered.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates generally answered the question on libraries well. Clearly candidates who have had extensive practical experience of coding with libraries would have been better prepared for this type of question.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The candidates generally came up with at least one appropriate reason.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.11",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-2-features-of-oop"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The flowchart below represents an algorithm that allows a user to enter the number of sides, length of side and apothem (see diagram below) for a regular polygon. It then calculates and outputs the area and perimeter of this shape.</p>\n<p><strong>Worked example: a regular hexagon</strong></p>\n<p>Number of sides = 6</p>\n<p>Length of side = 10 cm</p>\n<p>Apothem = 8.66 cm</p>\n<p>Perimeter = Number of sides × Length of side</p>\n<p>               = 6 × 10               </p>\n<p>               = 60 cm</p>\n<p>Area =<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mfrac><mrow><mi>Perimeter</mi><mo> </mo><mo>×</mo><mo> </mo><mi>Apothem</mi></mrow><mn>2</mn></mfrac></math></p>\n<p><br/>        = <math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mfrac><mrow><mn>60</mn><mo> </mo><mo>×</mo><mo> </mo><mn>8</mn><mo>.</mo><mn>66</mn></mrow><mn>2</mn></mfrac></math></p>\n<p>        = 259.8 cm<sup>2</sup></p>\n<p style=\"text-align: center;\"><sup><img src=\"data:image/png;base64,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\"/></sup></p>\n<p style=\"text-align: center;\"><sup><img src=\"data:image/png;base64,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\"/></sup></p>\n<p style=\"text-align: center;\"><sup><img src=\"data:image/png;base64,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\"/></sup></p>\n</div><div class=\"specification\">\n<p>From the flowchart, pseudocode is to be created that will include sub‑programs to calculate and return the perimeter and area of the shape.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct pseudocode for the body of the following sub-program to calculate and return the perimeter of the shape.</p>\n<pre>SUB_PERIMETER(NUM_SIDES, LENGTH_SIDE)<br/>    // Pseudocode to be added<br/>end SUB_PERIMETER</pre>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a similar sub-program to the one in part (i) to calculate and return the area of the shape.</p>\n<p>You must use an appropriate name for the sub-program and appropriate names for the parameters.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct pseudocode based on the flowchart to collect input from the user, call the sub-programs created in parts (i) and (ii), and output the results.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">a.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Without using pseudocode, explain how your algorithms could be altered to <strong>also</strong> find the area and circumference of a circle.</p>\n<p><strong>Note</strong>:</p>\n<p>Area of circle = <math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>π</mi><mi>r</mi></math><sup>2</sup></p>\n<p>Circumference of circle = 2<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>π</mi><mi>r</mi></math></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/><em>Award <strong>[1]</strong> for a correct formula that uses the passed arguments;</em><br/><em>Award <strong>[1]</strong> for a return statement;</em></p>\n<p><strong>Example 1</strong>:</p>\n<pre>SUB_PERIMETER(NUM_SIDES, LENGTH_SIDE)<br/>      //Pseudocode to be added<br/>      return NUM_SIDES * LENGTH_SIDE<br/>End SUB_PERIMETER</pre>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/><em>Award <strong>[1]</strong> for a correct sub-program (function) defined with end statement;</em><br/><em>Award <strong>[1]</strong> for the correct arguments passed;</em><br/><em>Award <strong>[1]</strong> for the correct use of return;</em><br/><em>Award <strong>[1]</strong> for the correct formula used;</em></p>\n<p><strong>Example 1</strong>:</p>\n<pre>SUB_AREA(PERIM, APOTHEM)<br/>  AREA = PERIM * APOTHEM / 2<br/>  return AREA<br/>end SUB_AREA</pre>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em><br/><em>Award <strong>[1]</strong> for all three correct inputs;</em><br/><em>Award <strong>[1]</strong> for all three inputs with appropriate prompts;</em><br/><em>Award <strong>[1]</strong> for the correct definition of </em><code>PERIM</code><em>;</em><br/><em>Award <strong>[1]</strong> for the correct call of perimeter sub-program, including variables/arguments passed;</em><br/><em>Award <strong>[1]</strong> for the correct call of area sub-program, including variables/arguments passed;</em><br/><em>Award <strong>[1]</strong> for the return/output of both values;</em></p>\n<p><strong>Example 1</strong>:</p>\n<pre>//Main Program<br/>output \"Number of sides?\"<br/>input NUM_SIDES<br/>output \"Length of each side?\"<br/>input LENGTH_SIDE<br/>output \"Length of apothem?\"<br/>input APOTHEM<br/>PERIM = SUB_PERIMETER(NUM_SIDES, LENGTH_SIDE)<br/>output \"Perimeter is \", PERIM<br/>output \"Area is \", SUB_AREA(PERIM, APOTHEM)</pre>\n<div class=\"question_part_label\">a.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em></p>\n<p><em>Award <strong>[1]</strong> for determining whether the shape is a polygon or a circle</em><br/><em>Award <strong>[1]</strong> for inputting the radius (if a circle);</em><br/><em>Award <strong>[1]</strong> for the creation of new subprogram (for area) <strong>OR</strong></em><br/><em>                changing values of input parameters for the existing one (SUB_AREA ) <strong>OR</strong></em><br/><em>                the calculation using correct formula (as given in the question paper PI * RADIUS * RADIUS)</em><br/><em>Award <strong>[1]</strong> for the creation of a new subprogram (for circumference) <strong>OR</strong></em><br/><em>                appropriately changing values of input parameters to the existing one (SUB_PERIMETER ) <strong>OR</strong></em><br/><em>                the calculation using formula (as given in the question paper 2*PI * RADIUS)</em><br/><em>Award <strong>[1]</strong> for calling both subprograms / outputting both values</em><br/><em>Award <strong>[1]</strong> if after the alteration of the algorithm, it should still be able to calculate the area and the perimeter of the polygon as well as the area and the circumference of the circle</em></p>\n<p><strong>Example answer 1 (with existing subprograms, after inputting the three values: NUM_SIDES, LENGTH_SIDE, APOTHEM):</strong><br/>check if the inputted value for the NUM_SIDES is 1 or 0 (then the shape is a circle);<br/>then inputted value for LENGTH_SIDE is to be used as radius;<br/>2*3.14 should be set as NUM_SIDES and LENGTH_SIDE as APOTHEM;<br/>call already written sub-programs(SUB_AREA and SUB_PERIMETER) so that algorithm calculates/finds correct area of circle and circumference;<br/>if the shape is not a circle then code (as in Part(a)) for the polygon ( no other changes needed);</p>\n<p><strong>Example answer 2</strong> <strong>(with new subprograms for area and circumference created)</strong>:<br/>construct two new sub-programs (for example, getCircleArea and getCircumference) ;<br/>(in the main program) ask the user if the shape is a polygon or a circle;<br/>if it is a polygon then code is as written in part a (no changes);<br/>if it is a circle then ask the user to input the radius;<br/>call both new subprograms to output area and circumference/ call the getCircleArea(radius) to find area and getCircumference(radius) sub-program to find circumference;</p>\n<p><strong>Example answer 3</strong> <strong>(with simple if statement and formulas given in the question paper)</strong>:<br/>ask user to input if the shape is circle and use if statement to check this input value;<br/>if not circle then code for the polygon (as written in part a);<br/>otherwise (it is circle!) input radius;<br/>calculate area;<br/>and circumference using formulas given in the question;</p>\n<p><em>Note to examiners: there may be other examples of correct answers</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates constructed excellent algorithms in part (a).</p>\n<p>Some candidates showed little or no ability at algorithm construction.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates did not correctly call subprograms in part (a)(iii).</p>\n<div class=\"question_part_label\">a.iii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "22M.1.HL.TZ0.14",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An old set of 2D animated cartoons from the 1940s has been discovered and it is decided to modify them to turn them into 3D animation.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>visualization</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the need for rendering in the creation of the animated 3D characters.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>two</strong> technical implications of implementing a 3D animation in this way.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The creation of a human interpretable image from data;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>The characters are defined as mathematical models;<br/>They will need rendering to give a 3D effect;<br/>Each movement will need to be fluid;<br/>The animation is less interesting with flat figures;</p>\n<p><em>Allow mark for explanation of rendering</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/>Rendering for non-interactive media such as animations is a much slower process than for interactive media/games;<br/>Rendering times for individual frames may vary from a few seconds to days depending on the complexity of the scene;<br/>Rendered frames are stored to a hard disk and then transferred to other media for playback;<br/>3D rendering is computationally resource hungry;<br/>Computer processing power has increased rapidly and greatly over the years;<br/>Allowing a much higher degree of photorealism / realistic rendering;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was reasonably well answered by majority of the candidates.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most answers tended to be superficial and generic in nature. The responses were mostly focused on a description of rendering rather than why it was important in the process.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates focused on explaining the process of rendering and creating 3D animation rather than technical implications of doing so.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.6",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-3-visualization"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>When the number of customers in the supermarket is low, some check-out counters will close. When there are many customers waiting, a new check-out counter will open.</p>\n</div><div class=\"specification\">\n<p>When a new check-out counter opens, some customers from the nearest line will choose to move their carts to this check-out counter.</p>\n<p>For this simulation, the assumption is made that every second cart in the old line will move to the new line and the other carts will remain in the original line.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why the variable <code>active</code> in the UML of the class <code>POSline</code> was defined as a boolean data type.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the code for a method <code>split</code> (part of the class <code>POSsystem</code>), that takes an existing, non-empty <code>POSline</code> as a parameter. It should copy every second cart from the original line into a new line. Afterwards it should delete every second cart from the original line.</p>\n<p>An example call in <code>POSsystem</code> would be: <code>POSline number2 = split(number1)</code>, where <code>number1</code> is an existing <code>POSline</code>.</p>\n<p>You may use any method declared or developed.</p>\n<div class=\"marks\">[8]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>As a result of the simulation, the company has decided to create a new POS system for their supermarkets.</p>\n<p>There have been discussions about adapting existing open source code when developing modules of the new POS system.</p>\n<p>Describe <strong>two</strong> ethical issues that may arise if modules of the new POS system are developed from open source code.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><code>active</code> indicates that a POS counter is either open (true) or closed (false);<br/>boolean uses the least memory space / one bit to represent this data;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[8 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for a correct method header;</em><br/><em>Award <strong>[1]</strong> for correctly declaring and returning a new </em><code>POSline</code><em> variable;</em><br/><em>Award <strong>[1]</strong> for attempting to use modulo division / declaring and initialising a flag;</em><br/><em>Award <strong>[1]</strong> for a loop with one correct terminal condition, including increment of i;</em><br/><em>Award <strong>[1]</strong> for the second correct loop condition;</em><br/><em>Award <strong>[1]</strong> for correctly checking that i is odd / checking and setting the flag;</em><br/><em>Award <strong>[1]</strong> for copying a cart from the original line;</em><br/><em>Award <strong>[1]</strong> for correctly adding every second cart to the new line;</em><br/><em>Award <strong>[1]</strong> for a reasonable attempt at removing every second cart;</em><br/><em>Award <strong>[1]</strong> for correctly removing every second cart from the original line;</em></p>\n<pre><strong>public</strong> POSline split(POSline oldLine){<br/>   POSline newLine = <strong>new</strong> POSline();<br/>   Cart temp;                            // optional, see below<br/><strong>   int</strong> i=0;<br/><strong>   boolean</strong> remove=false;<br/><strong>   while</strong>((i&lt;20)&amp;&amp;(oldLine.getLine(i)!=<strong>null</strong>)){<br/><strong>      if</strong> (remove) {<br/>         temp = oldLine.getLine(i);      // can be combined<br/>         newLine.joinLine(temp);<br/>      }<br/>      remove = !remove;<br/>      i++;<br/>   }<br/>   remove=false;<br/><strong>   while</strong>((i&lt;20)&amp;&amp;(oldLine.getLine(i)!=<strong>null</strong>)){<br/><strong>      if</strong> (remove) {<br/>         temp = oldLine.leaveLine(i)); // shifts carts<br/>         remove = !remove;<br/>      }<br/><strong>      else</strong>{<br/>        i++;<br/>      }<br/>   }<br/><strong>   return</strong> newLine;<br/>}</pre>\n<p><em>Example answer using modulo division (MOD):</em></p>\n<pre><strong>public</strong> POSline split(POSline oldLine){<br/>   POSline newLine = <strong>new</strong> POSline();<br/><strong>   int</strong> i=0;<br/><strong>   while</strong>((i&lt;20)&amp;&amp;(oldLine.getLine(i)!=<strong>null</strong>)){<br/><strong>      if</strong> (i%2==1) {<br/>         newLine.joinLine(oldLine.getLine(i));//combined<br/>      }<br/>      i++;<br/>   }<br/><strong>   while</strong>(i&gt;0){                  // must be reverse order<br/><strong>      if</strong> (i%2==1) {<br/>         temp = oldLine.leaveLine(i);<br/>      }<br/>      i--;<br/>    }<br/><strong>    return</strong> newLine;<br/>}</pre>\n<p><em>Example answer using a flag that combines both copy and remove actions (not anticipated):</em></p>\n<pre><strong>public</strong> POSline split(POSline oldLine){<br/>   POSline newLine = new POSline();<br/><strong>   int</strong> i=0;<br/><strong>   boolean</strong> flag=false;<br/><strong>   while</strong>((i&lt;20)&amp;&amp;(oldLine.getLine(i)!=<strong>null</strong>)){<br/><strong>      if</strong> (flag) {<br/>         newLine.joinLine(oldLine.leaveLine(i));<br/>      }<br/><strong>      else</strong> i++;<br/>      flag = !flag;<br/>   }<br/><strong>   return</strong> newLine;<br/>}</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>(Any) software can be considered as a form of Intellectual property;<br/>Company should consider whether there is a need to acknowledge the work of other programmers (don’t accept answers talking about plagiarism);</p>\n<p>Open source software may have undergone many changes from the original;<br/>So adequate testing of the final product must take place;<br/>to prevent any malfunction that will affect their customers;</p>\n<p>The company will be promoting the spread of open source software;<br/>Helping to break the monopolies of the major IT companies;<br/>To the benefit of people / countries with reduced financial resources;</p>\n<p>The use of open-source software means that the code can (more easily) be read/understood/ hacked by others;<br/>Leading to possible data breaches for their customers;</p>\n<p>Code may contain malware;<br/>Leading to possible data breaches for their customers;</p>\n<p>Using open-source software results in less work for programmers;<br/>This affecting their livelihood;</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p><span style=\"text-decoration:underline;\">Notes</span>:<br/>- <em>Award 2 marks for a valid issue if a detailed discussion has been provided</em><br/><em>- Only give credit for issues that have some ethical connection</em></p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most referred to the fact that only two states were possible but did not link this into saving of memory.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The most difficult questions on the paper for SL candidates. This was a good test for the better programmers, but many candidates struggled with the logic. Two separate methods of processing every other value were seen in the answers: the use of the modulo function and incrementing the loop by 2. The use of previously defined methods was suggested, and many candidates did just that.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The candidates appeared unprepared for this question but most described at least one valid ethical issue.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19N.2.SL.TZ0.12",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-1-objects-as-a-programming-concept",
      "d-3-program-development"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the need for higher level languages.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>two</strong> benefits of using sub-procedures within a computer program.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>three</strong> characteristics of a collection.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Collection <code>NUMBERS</code> already exists and stores real numbers.</p>\n<p>Construct in pseudocode an algorithm, using the access methods of a collection, which will iterate through the collection <code>NUMBERS</code> and count how many elements stored in the collection are in the interval [−1,1].</p>\n<p>The final answer should be output.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for the answer identifying improved programmer productivity and <strong>[1]</strong> for making reference to machine independence up to <strong>[2 max]</strong></em>.</p>\n<p>High-level language(HLL) provides statements (for example, high level <code>if</code>(…), <code>while</code>(…) , etc.) which are not dependent on the specific machine / and ability to create various data structures;<br/>Which saves the programmer’s time;</p>\n<p>Higher level languages are closer to human language;<br/>So programmers find them easier to understand/work with than lower level languages;</p>\n<p>HLL saves programmer from knowing details of computer architecture (and using all the specific (machine) instructions);<br/>So giving more time to creating/developing the best way of coding a problem/process of coding is simpler and more understandable;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for an answer stressing the usefulness of sub-procedures in any of the following</em>:<br/><em>Program organization;</em><br/><em>Program coding;</em><br/><em>Program testing;</em><br/><em>Maintenance;</em><br/><em>etc.</em></p>\n<p><em>Award <strong>[1]</strong> for the expansion up to <strong>[2 max]</strong></em>.</p>\n<p>Problem could be divided into smaller/easier parts;<br/>Which means solving easier/smaller parts of the problem for one programmer;<br/>Or for a team of programmers, each programmer could work on different smaller parts;</p>\n<p>Simpler testing;<br/>Each part of the program could be separately tested;<br/>By the programmer who created the code or someone else in the team of programmers;</p>\n<p>Reusable code;<br/>Sub-procedures already written/tested could be used in various programs;</p>\n<p>Simpler maintenance and changes;<br/>Could be done only on required sub-programs;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for data structure implementation/objects/elements of collections.</em><br/><em>Award <strong>[1]</strong> for algorithms/methods/callback functions.</em><br/><em>Award <strong>[1]</strong> for showing that collection reduces programming effort / increases performance (by providing efficient implementations of data structures and algorithms).</em></p>\n<p><em>Award marks for description of a specific example collections (in Java or any other programming language) such as arrays, dictionaries, sets, lists, trees (they have some characteristics in common, but also each of them has different way of organizing the data elements/objects they contain).</em></p>\n<p>Collection is a container of discrete values;<br/>Usually of the same type (primitive data values and also some other data structures);<br/>(But) collection objects can be of different types (pointers afford a flexibility and thus collection objects permit references to any data structure as well as to primitive values);<br/>Collections have a set of methods that define operations performed on the elements/objects of that collection;<br/>Such as adding/removing elements to/from collection, comparing elements of collection, searching, etc.;<br/>Which reduces programming effort (because implementations of data structures and algorithms are provided);<br/>Which increases performance of the program (because efficient implementations are provided);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong>.</em></p>\n<p><em>Award <strong>[1]</strong> for initialization and for outputting <strong>correct</strong> result (COUNTER).</em><br/><em>Award <strong>[1]</strong> for using collection methods.</em><br/><em>Award <strong>[1]</strong> for correct loop.</em><br/><em>Award <strong>[1]</strong> for retrieving a number (<code>ELEMENT</code>) from the collection.</em><br/><em>Award <strong>[1]</strong> for if statement within the loop.</em><br/><em>Award <strong>[1]</strong> for correct condition in if statement.</em><br/><em>Award <strong>[1]</strong> for increasing <code>COUNTER</code> if needed.</em></p>\n<p><em>Example answer</em>:</p>\n<pre>COUNTER = 0<br/>NUMBERS.resetNext()<br/>loop while NUMBERS.hasNext()<br/>  ELEMENT = NUMBERS.getNext()<br/>  if ELEMENT &gt;= −1 and ELEMENT &lt;= 1 then // abs(ELEMENT) &lt;= 1<br/>    COUNTER = COUNTER + 1<br/>  end if<br/>end loop<br/>output COUNTER</pre>\n<p><em><strong>Note</strong>: be flexible over the method names. For example, NUMBERS.getData() is acceptable instead of <code>NUMBERS.getNext()</code></em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "18N.1.SL.TZ0.9",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Reverse Polish notation (RPN) is a method used to represent mathematical expressions so they can be evaluated without the need for parentheses.</p>\n<p>An expression written in this form is known as postfix notation, whereas an expression written the traditional way is known as infix notation.</p>\n<p>For example:</p>\n<p style=\"text-align: center;\">Infix notation: <code>(8 − 5) * 7</code></p>\n<p style=\"text-align: center;\">Postfix notation: <code>8 5 − 7 *</code></p>\n<p>Both the infix and postfix expressions have the same result: 21</p>\n<p>RPN expressions are evaluated from left to right as follows:</p>\n<ul>\n<li>Each character is checked,<br/>\n<ul>\n<li>if it is a digit, it is pushed onto a stack.</li>\n<li>if it is a mathematical operator, the last two digits are popped from the stack and evaluated as though the current operator was between them. The result of this operation is then pushed back onto the stack.</li>\n</ul>\n</li>\n<li>The process is repeated until all the characters in the RPN expression have been used.</li>\n<li>The value left in the stack is the result of the expression.</li>\n</ul>\n<p>A collection named RPN already stores an expression formatted in Reverse Polish notation.<br/>The algorithm reads the values from the collection and, using a stack data structure, evaluates it.</p>\n<pre>RPN.resetNext()<br/>loop while RPN.hasNext()<br/>    VALUE = RPN.getNext()<br/>    loop while not (VALUE = \"+\" or VALUE = \"-\" or VALUE = \"*\" or VALUE = \"/\")<br/>        stack.push(VALUE)<br/>        VALUE = RPN.getNext()<br/>    end loop<br/>    OPERAND2 = stack.pop()<br/>    OPERAND1 = stack.pop()<br/>    if VALUE = \"+\" then<br/>      NEW_VAL = OPERAND1 + OPERAND2<br/>      stack.push(NEW_VAL)<br/>    end if<br/>    if VALUE = \"-\" then<br/>      NEW_VAL = OPERAND1 - OPERAND2<br/>      stack.push(NEW_VAL)<br/>    end if<br/>    if VALUE = \"*\" then<br/>      NEW_VAL = OPERAND1 * OPERAND2<br/>      stack.push(NEW_VAL)<br/>    end if<br/>    if VALUE = \"/\" then<br/>      NEW_VAL = OPERAND1 / OPERAND2<br/>      stack.push(NEW_VAL)<br/>    end if<br/>end loop<br/>RESULT = stack.pop()<br/>output \"The result is: \", RESULT</pre>\n</div><div class=\"specification\">\n<p>An alternative data structure in which the expression used in <strong>part (a)</strong> may be stored is a binary tree. If the tree is traversed using postorder tree traversal, the output is formatted in RPN.</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the trace table for the algorithm using the RPN collection data:</p>\n<p style=\"text-align:center;\"><code>5 2 + 25 16 − * 3 /</code></p>\n<p style=\"text-align:left;\"><img src=\"data:image/png;base64,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\"/><code></code></p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why a stack is used in the process of evaluating the expression in the algorithm.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline the steps involved in traversing the given tree using postorder tree traversal.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the output from the given tree using inorder tree traversal.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max] <br/></strong></em><em>Award <strong>[1]</strong> for correct values in </em>VALUE column;<em><br/>Award <strong>[1]</strong> for correct values of </em>OPERAND2, OPERAND1 and NEW_VAL when VALUE is ‘+’;<em><br/>Award <strong>[1]</strong> for correct values of </em>OPERAND2, OPERAND1 and NEW_VAL when VALUE is ‘-’;<em><br/>Award <strong>[1]</strong> for correct values of </em>OPERAND2, OPERAND1 and NEW_VAL when VALUE is ‘*’;<em><br/>Award <strong>[1]</strong> for correct values of </em>OPERAND2, OPERAND1 and NEW_VAL when VALUE is ‘/’;<em><br/>Award <strong>[1]</strong> correct output: </em>‘The result is: 21’;</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em><strong>Note</strong>: The trace table may be differently presented.</em></p>\n<p><em><strong>Note</strong>: Allow Follow Through.</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max] <br/></strong></em>A stack is a last in first out (LIFO) / first in last out (FILO) data structure;<br/>…which means data is popped off the stack in the reverse order to which it was pushed;<br/>In the expression, it is important to evaluate some of the values in a certain order(to obtain the correct result);<br/>For example, 25 − 16 would give the wrong value if it was evaluated as 16 − 25;</p>\n<p>Pushing items onto a stack and then popping them off reverses the order;<br/>To evaluate e.g. “10 2 /” we must treat it as “do the operation on the operands 10 and 2”/ i.e. we have to access the operator before we can apply it to the operands;<br/>A stack achieves the reversing of the order of the operators and their operands as a group (but it also reverses the operands which must be fixed);</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max] <br/></strong></em>Start from the root node;<br/>If the root is null, return immediately;<br/>Traverse left subtree;<br/>Traverse right subtree;<br/>Visit root;</p>\n<p>start from the root node (for example traverse (root)) ;<br/>terminate traversal (and backtrack) when/if root equals null;<br/>(before visiting the root node) traverse (and visit/process/output) each node in the left subtree (i.e., traverse(root.left));<br/>(before visiting the root node) traverse (and visit/process/output) each node in the right subtree (i.e., traverse(root.right));<br/>visit/output/process root (for example, output(root));</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max] </strong><br/>Award <strong>[1]</strong> if the answer contains no more than one error.<br/></em></p>\n<p><code>(5+2)*(25−16)/3</code><em><br/></em></p>\n<p><em>Note to examiners: Ignore the brackets – these represent the completely correct mathematical expression, but they are not read from the tree. Some candidates might include them because they realise that they may be needed so the expression works correctly.<strong><br/></strong></em></p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates had difficulty tracing the algorithm in Part (a).</p>\n<p>There were some candidates who did not attempt question 15.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Part (b) was reasonably well answered with many gaining at least 1 or 2 of the marking points.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Only a few candidates earned full marks in Part (c).</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "22M.1.HL.TZ0.15",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming",
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Sestra.com is a website maintained by a company who sell items made by local craftspeople.</p>\n<p>The website is compatible with different screen sizes and formats ranging from desktop computers to mobile smartphones. All the site’s pages contain the following code fragment:</p>\n<p><code>&lt;link rel = \"stylesheet\" href = \"../css/default.css\"&gt;</code></p>\n</div><div class=\"specification\">\n<p>Visitors to the site can search categories of products (for example “Toys”, “Bags”, “Dresses” etc.) selected from a drop-down menu. The menu is populated from the records stored in the <code>CATEGORY</code> table of the site’s database.</p>\n<p>Parts of the code of the file <code>search.php</code> is shown below:</p>\n<pre>// Other code present here<br/>&lt;?php<br/>  $categoryquerytext = 'SELECT 'category_id', 'category_name' FROM<br/>  'CATEGORY' ORDER BY 'category_name'';<br/>  $categoryqueryresult = mysqli_query($con, $categoryquerytext);<br/>?&gt;<br/>// Other code present here<br/>&lt;form action = \"showresults.php\" method = \"post\"&gt;<br/>  &lt;select name = \"category\"&gt;<br/>  &lt;?php<br/>    while($row = mysqli_fetch_array($categoryqueryresult))<br/>    {<br/>      echo '&lt;option value = \"'. $row['category_id']. '\"&gt;'.<br/>      $row['category_name']. '&lt;/option&gt;';<br/>    }<br/>  ?&gt;<br/>  &lt;/select&gt;<br/>  &lt;button type = \"submit\"&gt;Search&lt;/button&gt;<br/>&lt;/form&gt;<br/><br/>// Other code present here</pre>\n</div><div class=\"specification\">\n<p>The owners of the company have noticed that Sestra.com does not appear very prominently in search engine results.</p>\n</div><div class=\"specification\">\n<p>The Sestra.com site includes:</p>\n<ul>\n<li>images of each product</li>\n<li>pdf documents giving background information about the craftspeople who produced the products.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> ways that a cascading style sheet (CSS) can be used to ensure web pages are compatible with different screen sizes and formats.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the processing this code enables on the server before <code>search.php</code> is sent to the client.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>two</strong> ways in which the site developers could use white hat optimization to improve the site’s search engine ranking.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between lossy and lossless compression.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why the developers at Sestra.com would use lossless compression for the pdf documents.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Change/re-flow the layout of the page to suit different screen sizes/formats;<br/>adjust font sizes;<br/>adjust image sizes;<br/>provide alternative menus / link options;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>The code:</p>\n<ul>\n<li>uses SQL to query the <code>CATEGORY</code> table in a database, selecting all pairs of <code>&lt;category_id, category_name&gt;</code> ordered by category_name;</li>\n<li>stores the results of the query in a variable / array <code>$categoryqueryresult</code>;</li>\n<li>prepares HTML for a drop-down menu which consists of a list of option tags derived from iterating through each pair in $categoryqueryresult;</li>\n<li>sets the <code>“value”</code> attribute of each menu item to <code>category_id</code>;</li>\n<li>sets the<code> “text”</code> attribute of each menu item to <code>category_name</code>.</li>\n</ul>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Include authoritative / high quality website content;<br/>To attract other reputable sites to link to it / promote it in the search engine ranking;</p>\n<p>Use appropriate use meta tags (e.g. keywords/descriptions);<br/>To provide clear data for web-crawlers/robots to use when categorising the page;</p>\n<p>Separate content from formatting (e.g. use of CSS etc.);<br/>To allow search engines to index the content of the site more effectively;</p>\n<p>Use site maps / site diagrams;<br/>To aid indexing by web-crawlers/robots;</p>\n<p>Include a robots.txt file in the page header;<br/>To give instructions to web-crawlers/robots as to how to index and describe the various pages on the site;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><strong>Lossy compression</strong><br/>Reduces the file-size by removing some of the data in the file;<br/>Once removed, the data cannot be recovered;<br/>This generally results in a loss of quality (e.g. picture resolution, audio frequency range);<br/>Files compressed using lossy compression are used in their compressed form (e.g. images, video, audio);</p>\n<p><strong>Lossless compression</strong><br/>Reduces file size by looking for repeated patterns of data / redundant data and replacing those with a single shorter \"token\";<br/>The tokens are associated with the data they represent by using a dictionary added to the file;<br/>Flies must be decompressed before they can be used;<br/>Decompression software reads the dictionary and replaces all the tokens with the original data They represent;<br/>Files do not lose any of the data they contain when compressed / decompressed;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Lossless compression reduces file size while preserving all the data;<br/>Once decompressed, the pdf document will contain all the data in the original document;<br/>Some pdf software allows for the automatic lossless compression of the document / the developers would not have to apply compression retrospectively;<br/>It would be hard for a lossy compression algorithm to distinguish between data that can be removed (e.g. reducing the resolution of the images) and data that is necessary for the pdf documents to make sense (e.g. parts of the text);<br/>Using lossy compression may result in an unusable/unreadable document;<br/><em>Accept answers that focus on why lossy compression would be unsuitable for the pdf files</em>.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates reasonably answered this question. However, some candidates were unable to demonstrate the understanding of how style sheets could be coded differently to tackle different screen sizes.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The vast majority of students didn’t write the expected response. Most of the responses were superficial and generic in nature.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was answered correctly by most of the candidates.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was reasonably answered by majority of the candidates.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was reasonably answered by majority of the candidates. However, some responses lacked the details.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.7",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-2-searching-the-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The collection, storage and sharing of data is becoming increasingly important for organizations who have a choice about which type of database to use to store their data. Two examples of database types are relational and object-oriented.</p>\n</div><div class=\"specification\">\n<p>The 2016 US presidential election was seen to be a victory for data analytics. Companies that specialize in analytics use data warehouses.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>two</strong> advantages of using a relational database rather than an object-oriented database.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>two</strong> characteristics of a data warehouse.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why data needs to be transformed before it can be loaded into the data warehouse.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why opinion poll data and other election data are timestamped when added to the data warehouse.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why analytics companies use link analysis.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why analytics companies use deviation detection.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Once data has been loaded into a data warehouse it can be mined. The use of data analytics is believed to have been important to the outcome of the US election campaign.</p>\n<p>Discuss whether the advantages of data mining techniques in this scenario outweigh the disadvantages.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Standards and support are available for RDB…<br/>…make it more stable / easier to resolve issues / easier to recruit staff;</p>\n<p>More user tools exist for RDBs…<br/>…such as report generators / mail merge / security level permissions / concurrent access.</p>\n<p>Easier to visualise data and relationships…<br/>…so more likely to have a correctly modelled database i.e. has no redundancy / improved integrity</p>\n<p>RDB tables and relationships are simple to implement…<br/>…an OODB requires an understanding of concepts of OOP;</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Repository data stored are historical/time variant;<br/>Data is collected from different sources;<br/>OLAP systems for reporting and data analysis (e.g. data mining) / provides businesses with information for informed decisions;<br/>Provides tools so that data can be validated, reformatted, reorganized, summarized, and restructured;<br/>Optimised for data retrieval;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Data is from my different external sources and (therefore) in many <span style=\"text-decoration:underline;\">different formats</span>;</em><br/><em>For example, dates may be dd/mm/yy or mm/dd/yy or yy/mm/dd (allow any valid example);</em><br/><em>To allow meaningful <span style=\"text-decoration:underline;\">analysis</span>, it must be in the same format/standardised;</em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Notes: maximum marks only if reference is made to the scenario</em><br/><em>Do not award marks just for a description of time-stamping</em></p>\n<p>The usefulness of information is often time dependent;<br/><em>Example relating to the US presidential election, such as</em><br/>Electoral opinions before a public debate may have less value than those after the debate;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Notes: don’t accept the word “link” on its own as a descriptor</em>.<br/><em>maximum marks only if reference is made to the scenario</em>.</p>\n<p>They use link analysis in order to establish relationships / associations between different data sets / different entities in the same data set;</p>\n<p><em>Examples relating to the US presidential election, such as</em>:<br/>How people voted in relation to some other factor, e.g. the level of use of social media / where they took their vacations / size of family …;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Note: maximum marks only if reference is made to the scenario</em></p>\n<p>They look for any unusual activity (anomaly pattern) in transactions;</p>\n<p><em>Examples relating to the US presidential election, such as</em><br/>Unusual switch in pre-electoral voting opinions;<br/>Sudden pro-candidate or anti-candidate sentiment in a particular state;</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p><span style=\"text-decoration:underline;\">Mark as follows</span>:<br/>Award <strong>[1]</strong> for a generic advantage of data mining;<br/>Award <strong>[1]</strong> for expanding on this advantage;<br/>Award <strong>[1]</strong> for linking this to the scenario;<br/>Similarly for a disadvantage;<br/>Award <strong>[1]</strong> for a valid conclusion;</p>\n<p><em>Advantages of data mining <strong>[3 max]</strong></em>.<br/>Clustering / cluster analysis allows objects to be treated as one group enabling the uncovering of previously hidden patterns;<br/>For example, cluster analysis may search groups by race or gender to discover if a candidate is unpopular with a demographic;<br/>Classification methods (e.g. genetic, rough set, fuzzy set) can be used to recognize patterns that describe the groups to which an item belongs;<br/>For example, classifying voters by income may provide useful information that can affect future publicity strategies;<br/>Association analysis allows a series of statistical relationships to be further explored or tested;<br/>Associations look for If-then rules that predict a particular stance on a controversial topic (e.g. abortion) may influence the religious voters;</p>\n<p><em>Disadvantages of data mining <strong>[3 max]</strong></em>.<br/>Data mining is based on the data collected from individuals;<br/>This data may be sensitive personal information that the individual concerned may not want to be shared;<br/>This personal data may be reaggregated to compromise the privacy and/or anonymity of the data subjects;</p>\n<p><em>Conclusions <strong>[1 max]</strong></em>.<br/>The development of more sophisticated processing algorithms is inevitable, so although there are potential concerns about the invasive nature of data mining, providing sufficient safeguards are put in place, there is nothing inherently wrong with this;<br/>Data mining is the start of the slippery slope of the state or multinational companies holding inappropriate quantities of personal data about citizens that is of limited value. Therefore, unless the privacy and/or anonymity of the data subjects can be guaranteed, this is an unethical practice;</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to identify at least one advantage but were unable to clearly explain them.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to identify the characteristics of a data warehouse.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not well answered as answers were very generic. Candidates were aware that data is from different sources but were not aware of it having many different formats which need to be standardized to allow for a meaningful analysis.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not well answered. Candidates were not aware of the reason of having data timestamped when added to the data warehouse, and therefore very few candidates were able to connect the answer to the given scenario.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Not well answered. A few candidates mentioned the use of link analysis to establish relationships between data sets but were unable to make a good reference to the given scenario.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates were aware of the definition of deviation detection, and answered the question from this perspective, but failed to give actual examples related to the scenario and make appropriate connections.</p>\n<div class=\"question_part_label\">f.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates were aware of the definition of deviation detection, and answered the question from this perspective, but failed to give actual examples related to the scenario and make appropriate connections.</p>\n<div class=\"question_part_label\">g.</div>\n</div>",
    "question_id": "19N.2.HL.TZ0.4",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-4-further-database-models-and-database-analysis"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by a heuristic algorithm.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> reasons why premature convergence may occur.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>A heuristic does not guarantee an optimal solution/finds an approximate solution;<br/>Heuristics produce a solution relatively quickly/sacrifices optimisation accuracy for speed;<br/>Used when exact algorithms (e.g. brute force) are impractical/used for computationally intractable problems;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em><br/>Small (initial) population/Lack of diversity in (initial) population;<br/>Failure to preserve population diversity/Genes of high rated individuals dominate the population early in the process; Low/zero mutation rate;<br/>Stopping condition occurs too soon;<br/>Selection strategy that reduces diversity/Elitism;<br/>Crossover strategy that reduces diversity;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "22M.3.HL.TZ0.1",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"specification\">\n<p>The following example shows two tours of ten cities (A–J) that are to be used to produce a new tour using the order crossover (OX) method.</p>\n<p><strong>Parent 1</strong></p>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAj4AAAAuCAYAAAAoexMeAAAFqklEQVR4nO3d61HbWBjG8YedfFcKYE4KYOQCduQCFrkB5BSASAHYFIBNAbEoIHYasNOANSnAmhSwmhQQVeD9ADIYX8JCsHR0/r8Zf7E8w3tGt0fnIo6Wy+VSAAAADvir6gIAAAAO5Z0kGXNcdR0AAACvluc/925/V/7ImOPf/thmtM9+TW8j7bNf09tI++zW9PZJz+vIYagLAAA4g+ADAACcQfABAADOIPgAAABnEHwAAIAzCD4AAMAZBB8AAOAMgg8AAHAGwQcAADiD4AMAAJxxkOBTFIWMOd76CcNTTSbjQ5RxEIPBtYLg77U29nqXyrJF1aW9Shyfy5hjFUWxdXsYnsr3Tw5c1Z8zGFzvPEbj+Nz6/Sc97MN9nya0M0lGiqKzjXMwz/OqS3sT5bE7m02rLuXVkmS09/i0+Rqzje+fKAxPqy7jzfR6l6v7fJ0ctMcnDDvK859rH9/31etdWn/SZtlCvn+iNE11cfFp1b40/a48zxsX8JoqSW7Xjs80/a6iKBSGp0rTedXl/RFZ9mPjPHw4H1tVl/cqUXSm0eiz2u32qk2z2bf7c/CfRgQ7Fzw9D8tPlv2oujQ8U1EUms2m8v2WsmxRq3Ov8qGu4fBGnudpOrU7+Nw9TX/QZPJVUdRdfW+M0WTyVb7f0mj0ucIK8RLl/jPGqNe7rLoc7NHrXSpN50qSW8Xxxep7328pSW7vf9OrqjzAKZPJWEVRqN/v1+4eX3nwKRljqi7hxZJkpDzP1e125Xne1t8Mh8P7Hq9mdrc3Xbf7UXme02tXU+W+CYK2gqC9sd3zPPX7VwqCoILqAPdMp1MZYxQEbfl+q1ajOpUHnzg+l++3dHHxqepSXmw+n8vzvLWenqd8v6V+/8rqgOcy3/clSYtFfbpr8aAchmy3N0NPKYq66vevDlUS4KxyaKvb/ShJ6nQ6tXpwfHfIPzabTWXM8cb3QdBWURQ7e0vqrk5jl29t3+RCW/ffc5Rt2zW52ya79mEYdlZDQrYp90uTj0GXxPH5xnee5zHHxxLlsFYYdiTdPXQMBteaTqd7OwgO5aDBZ9uFNUlGGgyuFcfnms2+HbKcN+f7Jxs3yn7/am3+gW2y7MfWm0sYnirP/62gIvxfu/YhUBdJcru6acI+5bDz4xGOMOxoMhkrz/PKRz5qMNR1oSBoK8sW1s5/2bUS5vHqmaaFOteUAbbqExbbNalHDrBZOak5TedrryIoh7nG4y8VV1iD4CM9vmj9qriSl2m326ule2imLMskSa2W3cu9myoMO/I8T/P5/lcODAbX1j5gSQ/v7NkW8OjFQx2Uk5q3vY7AGFOL+2Qtgk+WLeR5nrXvEImirowxGo1GO584bb7Y4u4pxRhD93tNlYsL0nS+831LUXR2P8/Q3l679+/fS1qfV/jQG/mhkpqAUp7nStP5zutkuTq26vBTefApl4LbvKrL8zwlya2K4pei6ExJMlpty/Ncvd7lavUay2ntkmULRdGZiqKwduKvK+6Wq7cVx+dr52CWLVYvoBwObyqs8PXKnq3x+G7YoLzR+H7L6kCHZigDTbma66ko6tbinT6Vr+oql3nbPOFXumtHmn5Xkow0nU41GFyvtoVhpxFtdMG21SRR1NVweNOYG8u+lXlxfGH1ku/J5KuSZKTx+MvaORhFXSXJrfX70Bij4fBmNeQl3V1fbA90aIbx+MvGpObHPM9TELSVpvNKV3IfLZfLpSQZc6w8/1lJEYdA++zX9DbSPvs1vY20z25Nb5/0vDZWPtQFAABwKAQfAADgDIIPAABwBsEHAAA4g+ADAACcQfABAADOIPgAAABnEHwAAIAzCD4AAMAZBB8AAOCMo+VyuXz6/7MAAABs9Lt/WbH6X10AAABNx1AXAABwBsEHAAA4g+ADAACcQfABAADOIPgAAABnEHwAAIAzCD4AAMAZ/wG2bIlakvOW0AAAAABJRU5ErkJggg==\"/></p>\n<p><strong>Parent 2</strong></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Copy and complete the tables below to calculate the offspring.</p>\n<p>The initial sub-sequence of the offspring has been completed in the first row.</p>\n<p><strong>Offspring</strong></p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the initial population size <strong>and</strong> the mutation rate will affect the probability that an implementation of a genetic algorithm will provide an optimal solution.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em><br/><em>Award <strong>[1]</strong> for two values correctly entered;</em><br/><em>Award <strong>[2]</strong> for three values correctly entered;</em><br/><em>Award <strong>[3]</strong> for four values correctly entered;</em><br/><em>Award <strong>[4]</strong> for five values correctly entered;</em></p>\n<p style=\"text-align:center;\"><em><img src=\"data:image/png;base64,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\"/></em></p>\n<p style=\"text-align:left;\"><em>Award full marks for a correct last stage, even if the stages are not shown.</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em></p>\n<p><strong>Initial population size</strong><br/>A large population size gives enough diversity (that makes an optimum solution more likely)/ A small population size lacks diversity (that makes an optimum salutation unlikely);<br/>A large population takes too long to calculate (very long conversion) / A small population is calculated quickly (converges early);</p>\n<p><strong>Mutation rate</strong><br/>A very low mutation rate gives little diversity / A high mutation rate provides high diversity;<br/>A low mutation rate tends toward exploitation (narrow search and local optima) / A high mutation rate tends towards exploration (search for too long and never converge);</p>\n<p><strong>Example answers with mutation and population size interaction</strong><br/>A low initial population size will give limited diversity/fewer good routes;<br/>So the mutation rate will need to be higher/more cities swapped;</p>\n<p>A high initial population size gives plenty of diversity/many good routes;<br/>So a low mutation rate will be needed;</p>\n<p>A mid-point sized initial population size and low(ish) mutation rate is necessary;<br/>To stop the GA from converging on a local optima/to ensure the GA converges to a near-global optimum;</p>\n<p>To explore the search space a balance between exploration and exploitation is needed;<br/>A medium-sized population size with a low(ish) mutation rate will achieve this;</p>\n<p><em>Mark as <strong>[2]</strong> for population size + <strong>[2]</strong> mutation.</em><br/><em>You can also award up to <strong>[4]</strong> if comparisons only are made.</em></p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "",
    "question_id": "22M.3.HL.TZ0.2",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>The following method, <code>calcBMI()</code> accepts person’s height (H) in metres (m) and weight (W) in kilograms (kg) and returns their Body Mass Index (BMI).</p>\n<pre>calcBMI(H, W)<br/>  X = H * H<br/>  B = W / X<br/>  return B<br/>endcalcBMI</pre>\n<p>Boris weighs 104 kg and is 2.00 m tall. His BMI can be calculated by calling method <code>calcBMI()</code> as follows</p>\n<p style=\"text-align: center;\"><code>BorisBMI = calcBMI(2.00, 104)</code>.</p>\n</div><div class=\"specification\">\n<p>A person can belong to one of the following four weight categories:</p>\n<p style=\"text-align: left;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>The data about a group of adults and their height measurement (in metres) and weight measurement (in kg) is held in three one-dimensional arrays.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Where<br/><code>NAME</code> is a one-dimensional array holding names (currently sorted in alphabetical order).<br/><code>WEIGHT</code> is a one-dimensional array holding weight measurement in kilograms.<br/><code>HEIGHT</code> is a one-dimensional array holding height measurement in metres.</p>\n<p>For example,<br/><code>NAME[0]</code> is Annie.<br/>Her weight measurement is 52.40 kg and can be found in <code>WEIGHT[0]</code>.<br/><code>HEIGHT[0]</code> is 1.56 which represents Annie’s height measurement in metres.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the value of variable <code>BorisBMI</code>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Use pseudocode to construct an algorithm which accepts a person’s BMI and outputs the weight category the person belongs to.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the name of the person whose height is held in <code>HEIGHT[3]</code>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> reason why a binary search algorithm cannot be used to find the name of person whose height is given.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how the name of person whose height is given could be output.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct an algorithm which will output the names of all the people whose BMI is greater than this group’s average BMI.</p>\n<p>You should call method <code>calcBMI()</code> in your answer.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>26.0;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong>.</em></p>\n<p><em>Award <strong>[1]</strong> for outputting 'underweight' and correct condition in if statement.</em><br/><em>Award <strong>[1]</strong> for outputting 'normal weight' and correct logical expression (18.5 &lt; B &lt; 25.0).</em><br/><em>Award <strong>[1]</strong> for outputting 'overweight ' and correct logical expression (25.0 &lt; B &lt; 30.0).</em><br/><em>Award <strong>[1]</strong> for outputting 'obese' (30.0 and greater than 30.0).</em><br/><em>Award <strong>[1]</strong> for using if-else.</em></p>\n<p><em>Example answer 1</em>:</p>\n<pre>category(B)<br/>   if B &lt; 18.5<br/>     output('underweight')<br/>   else if B &lt; 25.0<br/>     output('normal weight')<br/>   else if B &lt; 30.0<br/>     output('overweight')<br/>   else<br/>     output('obese')<br/>   end if<br/>end category</pre>\n<p><em>Example answer 2</em>:</p>\n<pre>category(B)<br/>  if B &lt; 18.5<br/>    output('underweight')<br/>  end if<br/>  if B &gt;= 18.5 and B &lt; 25.0<br/>    output('normal weight')<br/>  end if<br/>  if B &gt;= 25.0 and B &lt; 30.0<br/>    output('overweight')<br/>  end if<br/>  if B &gt;= 30.0<br/>    output('obese')<br/>  end if<br/>end category</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>Paul;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.</p>\n<p>Binary search can be applied only on sorted array / array <code>HEIGHT</code> is not sorted so binary search cannot be used;</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying each stage in the process of printing out a person’s name up to <strong>[2 max]</strong></em>.</p>\n<p>Linear (sequential) search could be used to find the position (array index) of a given height measurement in array <code>HEIGHT</code>;<br/>And the name in array <code>NAME</code> at found position should be outputted;</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p><em>Award <strong>[3 max]</strong> marks for calculating average.</em><br/><em>Award <strong>[1]</strong> for initialization and changing the sum (of all BMIs) within the loop.</em><br/><em>Award <strong>[1]</strong> for a correct loop.</em><br/><em>Award <strong>[1]</strong> for correct parameters in the call of method <code>calcBMI()</code>.</em><br/><em>Award <strong>[1]</strong> for calculating average for the 30 persons (sum of all BMIs over 30).</em></p>\n<p><em>Award <strong>[3 max]</strong> marks for displaying appropriate names <strong>after</strong> calculating the group’s average BMI.</em><br/><em>Award <strong>[1]</strong> for an if statement within the correct loop.</em><br/><em>Award <strong>[1]</strong> for condition in if (comparing the BMI of the person with the average BMI).</em><br/><em>Award <strong>[1]</strong> for the correct output <strong>[1]</strong> for correct parameters in the </em><em>method call </em><em><br/>(<code>calcBMI(HEIGHT[K], WEIGHT[K]</code>).</em></p>\n<p><em>Example answer</em>:</p>\n<pre>sum = 0<br/>loop for K from 0 to 29<br/>  sum = sum + calcBMI(HEIGHT[K], WEIGHT[K])<br/>end loop<br/>average = sum/30<br/><br/>loop for K from 0 to 29<br/>  if calcBMI(HEIGHT[K], WEIGHT[K]) &gt; average then<br/>    output(NAME[K])<br/>  end if<br/>end loop</pre>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18N.1.SL.TZ0.10",
    "topics": [
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "4-2-connecting-computational-thinking-and-program-design",
      "4-3-introduction-to-programming"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The Large Hadron Collider at CERN in Switzerland produces an average of 15 petabytes (15 million gigabytes) of experimental data every year. This data must be accessed and analysed by scientists around the world.</p>\n</div><div class=\"specification\">\n<p>CERN has established the <em>Worldwide LHC</em> <em>Computing Grid</em>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the URL https://home.cern/topics/large-hadron-collider</p>\n<p>State the protocol used.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to the URL https://home.cern/topics/large-hadron-collider</p>\n<p>Identify the steps taken by the domain name server when the scientist enters a URL such as https://home.cern into their web browser.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>two</strong> reasons why CERN would use grid computing to support its research.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Instead of copyrighting its experimental results, CERN has decided to publish its experimental results using Creative Commons licensing.</p>\n<p>Explain <strong>two</strong> reasons why CERN would publish its experimental results using Creative Commons licensing.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>HTTPS / hypertext transport protocol secure;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>The DNS looks up the domain name “home.cern” in its database;<br/>If it doesn’t have this string, it passes the query to another DNS according to defined rules;<br/>This process continues until either an IP address is passed back to the starting DNS or an error message is returned;<br/>The IP address (or error message) is sent back to the client that initiated the call to the DNS;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><em>Mark as <strong>[3]</strong> and <strong>[3]</strong></em>.</p>\n<p>Multiple copies of all or part of the data can be kept at different sites;<br/>This ensures that there is no single point of failure;<br/>and the redundant data helps to ensure against data loss;</p>\n<p>Different computers on the grid can use different analysis and data visualization tools;<br/>This allows scientists to run whatever analysis tools best suit their own specialism/area of interest;<br/>Rather than being limited to the tools provided by CERN;</p>\n<p>Analysis can be performed using distributed processing time/capacity;<br/>This reduces load and/or reliance on a centralized system;<br/>Allowing a greater number of processes to be run concurrently;</p>\n<p>Computers on the grid can be in multiple time zones;<br/>This gives scientists more equitable access to data;<br/>And facilitates round-the-clock monitoring and the availability of expert support;</p>\n<p>Resources can be distributed across the world rather than being held in one country;<br/>This may attract funding from governments for their own locally-based research;<br/>As they may see the benefits of international cooperation;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><strong>Benefits of CC licensing</strong>:<br/>CERN want their experimental results to be freely accessible (within specified limits);<br/>Allows for the more rapid dissemination of data/information while preventing people from repackaging them and selling them as a commercial product;<br/>May further the advance of scientific knowledge / be seen as an altruistic gesture;<br/>No need to contact CERN about using the work / allows CERN to focus on their primary function, i.e. scientific research;</p>\n<p><strong>Limitations of copyright</strong>:<br/>May be impossible to enforce;<br/>Enforcement would require significant costs associated with hiring of lawyers etc.;<br/>It may not be possible to find all cases where work has been used without copyright permissions;<br/>Plagiarism may occur outside of Switzerland where different copyright laws may exist;</p>\n<p><em>Candidates are not required to make comparisons with copyright, however credit should be given where valid limitations of copyright are explained.</em><br/><em>Mark as <strong>[3]</strong> and <strong>[3]</strong></em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>The question was answered correctly by most of the candidates.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates reasonably answered this question. However, majority of the candidates failed to score full marks due to lacking specific details of the way the DNS server handles the URL.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most responses tended to be generic and focused on explaining what grid computing is. Candidates were unable to explain why the grid computing would be useful to support the research. The connection with research was not addressed.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The responses were mostly focused on description of creative common licensing rather than why it was useful for publishing the results.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.8",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-3-distributed-approaches-to-the-web",
      "c-4-the-evolving-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>Two selection methods used in genetic algorithms are:</p>\n<ul>\n<li>roulette wheel</li>\n<li>truncation.</li>\n</ul>\n<p>Compare and contrast these two selection methods.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[6 max]</strong></em></p>\n<p><strong>Roulette Wheel Selection (RWS)</strong><br/><em>Award <strong>[1 max]</strong></em><br/>Solutions are mapped to a roulette wheel / occupying space that is proportional to their fitness;<br/>Better solutions have a greater probability of being selected / Worse solutions have a lower probability of being selected;</p>\n<p><strong>Truncation</strong><br/><em>Award <strong>[1 max]</strong></em><br/>The best / top N solutions are selected (for entry into the mating pool)/The worst / other solutions are not selected (for entry into the mating pool);<br/>The number chosen for N (truncation point) affects the speed of convergence;</p>\n<p><strong>Comparison</strong><br/><em>Award <strong>[4 max]</strong></em><br/>Both solutions rank/sort the solutions from best to worst OR truncation must be sorted but RWS can be implemented without sorting (e.g. rejection sampling);<br/>Thus, the chosen sorting algorithm OR approach affects the solution time (because it is done every cycle);</p>\n<p>In truncation, weakest solutions will never be chosen/only strongest solutions can be chosen;<br/>Whereas in RWS every solution has a chance of being selected/even the worst solution has a chance of being selected;</p>\n<p>In truncation, once selected the top N solutions have a probability equal to that of any of the other top N solution (e.g. best and 4th best have an equal chance of being selected);<br/>whereas in RWS proportionality is used so the top N percent are differentiated (e.g. best has a high chance of being selected than 4th best);</p>\n<p>RWS is more likely to preserve diversity than truncation;<br/>Because roulette wheel is more likely to avoid local minima than truncation;</p>\n<p>Both truncation and roulette wheel make their selections based on fitness scores;<br/>So need to consider the processing required to calculate fitness;</p>\n<p>Both methods work well with large populations;<br/>So if population diversity is necessary they are good choices/should not be chosen if population sizes are small;</p>\n</div>",
    "Examiners report": "",
    "question_id": "22M.3.HL.TZ0.3",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Refer to the Paper 3 Case study: Genetic algorithms, available under the \"Your tests\" tab &gt; supplemental materials.</p>\n</div><div class=\"question\">\n<p>To what extent do the characteristics of genetic algorithms make them an appropriate approach to solving the route optimization problems?</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[12 max]</strong></em></p>\n<p>Better essays will be structured to include the following areas:</p>\n<ul>\n<li>An introduction of genetic algorithms (GAs) and route optimization and the problem of vast search space / computational intractability.</li>\n<li>Advantages and disadvantages of GAs.</li>\n<li>Discussion of the characteristics that influence GA outcomes (population size, population diversity, selection method, crossover technique, mutation rate, stopping conditions, and hyperparameters).</li>\n<li>How GAs overcome limitations of premature convergence.</li>\n<li>A final measured conclusion in which the candidate links together the various points (good and bad) in evaluating the extent GAs can solve route optimization problems.</li>\n</ul>\n<p><strong>Introduction</strong></p>\n<ul>\n<li>A GA is a search heuristic that a near optimal solution.</li>\n<li>Based on the principle of natural selection.</li>\n<li>Designed for non-polynomial (NP) / computationally intractable combinatory optimization problems.</li>\n<li>GAs sacrifice accuracy for speed by using a combination of exploration and exploitation.</li>\n<li>GAs are useful in problems where an optimal solution is not critical (like route optimization).</li>\n<li>The route optimization in the scenario is the kind of problem where brute force is unsuitable.</li>\n<li>That is because the search space is factorial 20 / 2 or 1.216451 x 10<sup>18</sup> different routes.</li>\n</ul>\n<p><strong>Discussion points</strong><br/>GAs are suitable when:</p>\n<ul>\n<li>An optimal solution is not required.</li>\n<li>A deterministic algorithm is unavailable or impractical.</li>\n<li>Other heuristic methods are difficult to implement.</li>\n</ul>\n<p><strong>Advantages</strong></p>\n<ul>\n<li>Relatively easy to understand and implement.</li>\n<li>Can be easily configured using initialisation parameters.</li>\n<li>Can find near-optimal solutions in a much shorter time than other methods.</li>\n<li>Can use parallel sampling of the search space.</li>\n<li>Variety of approaches can be used including selection method, crossover technique, simulated annealing, and novelty search.</li>\n<li>Different techniques can be combined to improve performance (e.g. truncation selection may be used to provide an initial mating pool then switched to roulette wheel selection).</li>\n<li>A terminating condition can be used (e.g. time, number of generations) to limit the algorithms to use only the available time (e.g. before Lotte leaves).</li>\n<li>Techniques such as simulated annealing can combine early exploration and later exploitation of the problem space.</li>\n<li>Many implementations of GAs are readily available and require no specific algorithm development.</li>\n</ul>\n<p><strong>Disadvantages</strong></p>\n<ul>\n<li>Not guaranteed to find the optimum solution.</li>\n<li>Prone to converge prematurely on a local optimum, exploring only a small part of the solution space.</li>\n<li>The population may not converge at all.</li>\n<li>Because of the random aspect, the algorithm will miss good solutions and, even when it finds them, may destroy them with mutation.</li>\n<li>The performance of the algorithm may be highly sensitive to initial conditions (initial population, mutation rate) or other arbitrary choices, such as selection or crossover methods.</li>\n<li>The selection of these initial conditions is mostly by trial and error.</li>\n<li>They only take consider the quantitative data which is available (in this case distance between cities) and do not consider qualitative data (e.g. driving conditions, road closures etc.).</li>\n<li>They require some expertise to run effectively (i.e. correctly set up initialisation parameters, choose appropriate selection strategy etc.).</li>\n<li>Due to the “trial and error” nature, a GA is not able to “explain” how it arrived at the near optimum solution.</li>\n</ul>\n<p><strong>Characteristics that might be discussed</strong></p>\n<ul>\n<li>Since GAs are so dependent on initial conditions, it often happens that they are run with a range of different sets of initial conditions, in order to find fruitful ones.</li>\n<li>All of the initial conditions affect the others so the balance between them is difficult to determine.</li>\n<li>One GA can be used to find a set of initial conditions for another.</li>\n<li>Initial populations can be 'seeded' with better-than-average solutions.</li>\n<li>It is difficult to choose the ideal population size (too large and the algorithm runs slowly but too small and it lacks diversity).</li>\n<li>The mutation rate can be varied during the run. For example, mutation can be high at the beginning (exploration) and decrease as the algorithm progresses (exploitation), or it can be tied to an indicator of the current diversity of the population.</li>\n<li>The method of calculating the fitness value can slow down GAs.</li>\n<li>The method of terminating the algorithm may affect the solution.</li>\n<li>Stopping conditions include a set number of iterations/specified run time/ minimum target distance/frequency of better solutions found or a combination of these.</li>\n<li>Novelty search can be used to preserve diversity in the population.</li>\n<li>The balance between exploration and exploitation is the key to a successful algorithm;</li>\n<li>Both necessary but fundamentally opposed to each other.</li>\n<li>Functional Programming.</li>\n</ul>\n<p><strong>Conclusion</strong><br/>Any reasoned conclusion is fine. For example:</p>\n<p>In this scenario with only 20 cities, a GA is not appropriate because alternative methods such as the route inspection algorithm would be more suitable. Besides, the distance covered may not be the most important aspect of the tour. Particular sites of interest or events occurring on a particular day might be more important than a near-optimal route based on distance only.</p>\n<p>A GA is a suitable solution because a brute force approach on a home laptop would be computationally intractable and an optimal route is not required, any near-optimal one is fine. Since GAs are likely to be easier to implement than other heuristic approaches, a high school student may find it the best solution for their purpose.</p>\n<p><em>Please see markband below</em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div>",
    "Examiners report": "",
    "question_id": "22M.3.HL.TZ0.4",
    "topics": [],
    "subtopics": []
  },
  {
    "Question": "<div class=\"specification\">\n<p>Optical character recognition (OCR) is a method where printed text or handwritten text is converted to machine-encoded text.</p>\n</div><div class=\"specification\">\n<p>Recognizing handwritten characters presents more of a problem because people have different handwriting styles.</p>\n<p>For example, the digitized handwritten letter X in <strong>Figure 3</strong> does not exactly match the digitized letter X in <strong>Figure 4</strong>.</p>\n<p><img src=\"data:image/png;base64,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\" style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n<p>Artificial neural networks (ANNs) can be used to assist with recognizing handwritten characters that do not exactly match the expected pattern.</p>\n</div><div class=\"specification\">\n<p>Predictive text, where the computer predicts the next word in the sentence, can be programmed to utilize a neural network.</p>\n</div><div class=\"specification\">\n<p>The sentence “The child is feeling” is entered into an application that uses predictive text and three options are suggested: <em>better</em>, <em>like</em>, <em>a</em>. Upon entering the two characters “hu” the word <em>hungry</em> is suggested.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> problem that may lead to printed text characters not being detected correctly.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why an ANN can be used to overcome the challenges outlined in this scenario.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how ANN pattern recognition techniques are applied to ensure that the handwritten letter X in <strong>Figure 3</strong> is recognized as a letter X.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>two</strong> features that would be required by the ANN to predict the next word in the sentence.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the application uses a neural network to suggest suitable words.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline<strong> two</strong> potential problems with training the ANN to suggest appropriate words.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Printed text quality may be poor or faint/may use an unusual typeface e.g. BrushScript/In certain typefaces some characters can look similar e.g. sans serif;<br/>For example, 5 and S may be confused/Numbers may be confused with letters 1 or I; </p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>ANN can be <span style=\"text-decoration:underline;\">trained</span> to recognise handwriting styles / supervised learning;<br/>ANN can learn to apply existing knowledge to new handwriting styles / unsupervised learning;<br/>ANN can recognize parts of the image to determine if it is a match / doesn’t require the entire image to be a match;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>The ANN accesses a database of correctly stored characters;<br/>ANN breaks the image into smaller parts / applies a filter (2 or 3 pixels) to a section of the image;<br/>Calculate whether the pixels match / multiple each image pixel to the feature pixel, add them up, divide by number of pixels / perfect match will be 1;<br/>Apply convolution / repeat the application of the filter over and over;<br/>Apply a ReLU (Rectified Linear Units) layer (remove negative values) to reduce the mathematical calculations;<br/>Apply pooling to shrink the image stack;<br/>Use the stack of filter images / convolution layer to see if the image is a match;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>Dictionary of words;<br/>Predictive text algorithms;<br/>The ability to recall the previous words in the sentence / memory;<br/>An understanding of language structure e.g. nouns, verbs, adverbs, etc.;<br/>Previous words must be added back into the neural network;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>If they have used a diagram should include:</em><br/><em>Award <strong>[1]</strong> Input for previous words;</em><br/><em>Award <strong>[1]</strong> Input for new characters the user enters;</em><br/><em>Award <strong>[1]</strong> Hidden layers;</em><br/><em>Award <strong>[1]</strong> Weights inputted into the network;</em><br/><em>Award <strong>[1]</strong> Combining the two inputs;</em><br/><em>Award <strong>[1]</strong> Non-linear regression / sigmoid function;</em><br/><em>Award <strong>[1]</strong> Merge layers to produce output;</em><br/><em>Award <strong>[1]</strong> Back propagation / Output that re-enters the ANN;</em></p>\n<p style=\"text-align:center;\"><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/>Unsupervised learning may have a poor set of words / text-speak is used;<br/>So the ANN database doesn’t contain text-speak words so doesn’t learn;</p>\n<p>Vanishing gradient problem / Exploding gradient problem;<br/>Gradient signal is multiplied many times by weight matrix / The gradient signal can become smaller at every training step (vanishing) / can become excessively large at every training step (exploding) / This can make learning very slow or stops it completely;</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19N.2.HL.TZ0.8",
    "topics": [
      "option-b-modelling-and-simulation"
    ],
    "subtopics": [
      "b-4-communication-modelling-and-simulation"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Brownsville Council</em> run several public libraries in different areas of the city. The libraries use an Integrated Library Management System (ILMS) to manage all items (for example books, DVDs, etc.) held by the libraries. Details of the items are stored in a database on a central server.</p>\n<p>Below is part of the extensible markup language (XML) code used to describe an item.</p>\n<pre>&lt;item id = \"97812\"&gt;<br/>  &lt;category&gt;Book&lt;/category&gt;<br/>  &lt;author&gt;Stark, Elizabeth&lt;/author&gt;<br/>  &lt;title&gt;Handheld Device Usability&lt;/title&gt;<br/>  &lt;genre&gt;Computer Science&lt;/genre&gt;<br/>  &lt;publisher&gt;Taylor &amp; Orams Inc.&lt;/publisher&gt;<br/>&lt;/item&gt;</pre>\n</div><div class=\"specification\">\n<p>XML is based on open standards.</p>\n</div><div class=\"specification\">\n<p>Library users interact with the ILMS through a web page that includes a form to search for items stored on the database.</p>\n</div><div class=\"specification\">\n<p>The library managers have decided to extend their web pages to include a blog and a forum, maintained by the head librarian, in order to increase engagement with library users.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>extensibility</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of XML for sharing data on the web.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between open standards and interoperability.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the role of the common gateway interface (CGI) in processing search requests made via the web form.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Distinguish between a blog and a forum.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>To what extent has the use of social media, blogs and forums enabled the head librarian to be a more effective decision maker?</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>The ability of something to be extended or expanded from its initial state, e.g. software / file Formats / programming languages;<br/>The ability to add custom / user-defined elements (e.g. XML tags, plug-ins, add-ons);</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>In the real world, computer systems and databases contain data in incompatible formats;<br/>XML data is stored in plain text format. This provides a software- and hardware-independent way of storing data / makes it much easier to create data that can be shared by different applications;<br/>XML makes it easier to expand or upgrade to new operating systems / applications / browsers;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Interoperability is the ability of different computer systems (including operating systems and applications) to work cooperatively / share data;<br/>Open standards are standards that are publicly available and (normally) free to use;<br/>Open standards are one factor aiding interoperability;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>The Common Gateway Interface (GGI) is part of the Web's Hypertext Transfer Protocol (HTTP);<br/>CGI is a method or convention for passing data back and forth between the server and the application;<br/>A programmer can write a CGI application in a number of different languages. CGI provides a more efficient mechanism for data to be passed from the user's request to the application program (and back to the user);<br/>CGI is not dependent on the operating system used by the server. The methods / conventions remain the same;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>In a blog, only the owner can post an article / open a thread of discussion / start a theme;<br/>In a forum all registered participants can post an article / open a thread;</p>\n<p>In a blog, registered visitors may be allowed to comment but the blog owner may moderate the comments before displaying them;<br/>in a forum all registered users are allowed to comment (without moderation);</p>\n<p>In a blog users cannot edit or delete posts;<br/>in a forum there may be moderators who can edit or delete posts after they have been made;</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><em><strong>Note</strong>: do not award a tick for each idea, use the markband and use best-fit to determine the level of the response</em>.</p>\n<p><strong>Blog</strong></p>\n<ul>\n<li>allows the head librarian to post articles/entries that give readers a better idea of the issues/decisions affecting the library/the background context that affect decisions / may result in better informed and focused comments from readers;</li>\n<li>may foster a greater sense of participation / spirit of community / allow the head librarian to aggregate majority views;</li>\n<li>the ability of the head librarian to moderate posts may allow off-topic/ unhelpful/offensive comments to be filtered-out / however this might also allow the head librarian to censor/modify comments that are legitimate but critical of library services/contrary to decisions that the library want to push-forward;</li>\n</ul>\n<p><strong>Forum</strong></p>\n<ul>\n<li>allow library users to raise issues that they find important rather than only commenting on issues raised by the head librarian / may allow greater head librarian to develop a greater awareness of issues affecting/affected by decisions;</li>\n<li>lack of moderation may allow users to raise controversial issues / however may also reduce ability of head librarian to filter-out off-topic/unhelpful/ offensive posts;</li>\n<li>may oblige head librarian to engage in greater discussion / justification of actions / however excessive time might be spent clarifying issues / dispelling myths;</li>\n</ul>\n<p><strong>Both blog and forum</strong></p>\n<ul>\n<li>the comments/posts may not be reflective of the general/majority view / may be restricted to a biased/self-selecting sub-set of library users. This may influence the head librarian to make decisions that cater for the \"vocal\" minority;</li>\n</ul>\n<p><strong>[1–2 marks]</strong><br/>A limited response that indicates very little understanding of the topic or the reason is not clear. Uses little or no appropriate subject specific terminology. No reference is made to the scenario in the stimulus material. The response is theoretical and descriptive.</p>\n<p><strong>[3–4 marks]</strong><br/>A superficial analysis of why the increased engagement with library users through the blog and forum will assist the head librarian with decision making. There is some use of appropriate subject specific terminology in the response.</p>\n<p><strong>[5–6 marks]</strong><br/>A discussion of why the increased engagement with library users through the blog and forum will assist the head librarian with decision making. Explicit and relevant references are made to the scenario in the stimulus material. There is appropriate use of subject specific terminology throughout the response.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most answers were incorrect as the candidates were of the course in addressing ‘extensibility’ in this context.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates did not answer this correctly as the sharing data on the web using XML was not addressed specifically.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most answers were generic in nature though candidates were able to distinguish between open standards and interoperability.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Though this was a straightforward question, many candidates had difficulty in answering this.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to write at least one difference between a blog and a forum.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Though most of the responses lacked the details about how using blogs and forums could make the librarian more effective decision maker. The part where most candidates lost in this question was because they failed to address why the increased engagement with library users through the blog and forum will assist the head librarian in decision making. Explicit and relevant references had to be made to the given scenario that most candidates missed doing.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.9",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-1-creating-the-web",
      "c-3-distributed-approaches-to-the-web"
    ]
  },
  {
    "Question": "<div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline what is meant by the term computer network.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe <strong>one</strong> problem resulting from low bandwidth in a computer network.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for identifying the nature of a network and <strong>[1]</strong> for a development of the first point up to <strong>[2 max]</strong></em>.</p>\n<p>A group of computers and other computing hardware devices that are linked together through communication channels/cables/wirelessly;<br/>To enable communication (sharing files, sharing information) between systems/among a wide range of users;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for a definition of the term bandwidth and <strong>[1]</strong> for a description of an issue caused by low bandwidth up to <strong>[2 max]</strong></em>.</p>\n<p>Bandwidth indicates the maximum amount of data that can be transferred from one point to another in a unit of time;</p>\n<p>Low bandwidth means slow network performance / extended duration when transferring large amounts of data / loss of users’ time (money) / the whole area (of the users) is not covered;</p>\n<div class=\"question_part_label\">b.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div>",
    "question_id": "18N.1.HL.TZ0.1",
    "topics": [
      "topic-3-networks"
    ],
    "subtopics": [
      "3-1-networks"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Explain the importance of the method <code>isEmpty()</code> when constructing an algorithm which performs operations on a stack data structure.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p>Method <code>isEmpty()</code> returns True if there are no elements on the stack, False otherwise;<br/>It is important to call this method in logical expression/condition in algorithm constructs such as branches and loops (<code>if/while</code>);<br/>Before popping an element from the stack / <code>popStack()</code>;<br/>To prevent errors/stack underflow/program crash;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18N.1.HL.TZ0.6",
    "topics": [
      "topic-5-abstract-data-structures"
    ],
    "subtopics": [
      "5-1-abstract-data-structures"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p style=\"text-align: left;\">An international organization, OBI, has three offices in Europe, see <strong>Figure 7</strong>.</p>\n<p style=\"text-align: center;\"><strong>Figure 7: The location of OBI’s offices in Europe</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n<p style=\"text-align: center;\">[Source: adapted (recoloured, cropped and annotated) Europe Map by Erin Dill 0, www.freevector.com.<br/>Under copyright and CC 4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/)]</p>\n<p style=\"text-align: left;\"> </p>\n<p style=\"text-align: left;\">Office A and Office B report to Office C, the head office.</p>\n<p style=\"text-align: left;\">Office A and Office B collaborate on many projects. Office C is not involved in the collaboration between Office A and Office B.</p>\n</div><div class=\"specification\">\n<p>OBI has projects that involve large numbers of contributors from all over the world. These projects are managed by OBI staff in Office A.</p>\n<p>The contributors often include non-text based information.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Draw a directed graph based on the scenario.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe the role of graph theory in determining the connectivity of the World Wide Web.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> issues that may arise from using a non-text based search.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>OBI has decided to gather information for one project by utilizing collective intelligence.</p>\n<p>Evaluate this decision.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for bi-directional arrow between A &amp; B.</em><br/><em>Award <strong>[1]</strong> for uni-directional arrow between A &amp; C as well as B &amp; C.</em></p>\n<p style=\"text-align:center;\"><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Connectivity is a basic concept in Graph Theory. Graph theory can be used to model or analyse the nature of the connectivity on a selected area, the web;<br/>In World Wide Web, each web page is represented by vertex/node;<br/>The hyper links between web pages are represented by edges/lines in the graph;<br/>Arrows would indicate the direction of the hyperlinks (1-way / 2-way);<br/>In Graph Theory, a graph is said to be connected if there is a path between every pair of vertex. From every vertex to any other vertex, there should be some path to traverse. That is called the connectivity of a graph;<br/>Similar concept is used in connectivity of World Wide Web, where each vertex representing a type of web page is connected to any other vertex representing other kind of web pages;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Multimedia are assigned meta-tags;<br/>If the appropriate tag is not assigned, the media will not appear in the search;</p>\n<p>Searched by image similarity;<br/>Google search the web using the image inputted by the user;</p>\n<p>Non-text based searches are more difficult;<br/>Because there a no specific content to search for;</p>\n<p>Fewer search engines support multimedia searches;<br/>Therefore the search engine may not be as efficient;</p>\n<p><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>. <br/><em><strong>Advantages of using collective intelligence [2 max]</strong></em><br/>Collective intelligence costs significantly less per unit / pro rata than hiring a professional;<br/>A single task is being worked on simultaneously by numerous individuals so it can be completed much more rapidly than using a single person;<br/>It allows access (maybe part time or at very short notice) to a large number of contributors for specific events or tasks;</p>\n<p><em><strong>Disadvantages of using collective intelligence [2 max]</strong></em><br/>It may be hard to ensure sufficient quality control occurs as collective intelligence is a very deregulated environment;<br/>It may be hard to verify the originality of the work, or which work was originally completed by who;<br/>There may be many ideas, but it may be difficult evaluate each idea thoroughly as there may be a lack of centralised planning and/or a lack of a clearly defined hierarchy;</p>\n<p><em><strong>Conclusions [1 max]</strong></em><br/>Collective intelligence can help organizations in solving a problem in certain contexts;<br/>These may include where there is a need to provide information quickly such as a health related requirement, or where the final information is not critical to the wellbeing of others, for example a site such as Wikipedia;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Graph theory is quite a complex area of the course. Many correctly sketched a diagram.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Answers to (b) were quite vague and did not go further that identifying the function of nodes and links.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates identified at least one of the reasons.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates gained marks here, particularly in identifying both the diversity of opinions that could be gathered and the difficulties involved in analysing them.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19N.2.HL.TZ0.12",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-5-analysing-the-web",
      "c-6-the-intelligent-web"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Distinguish between the use of time slicing and priorities in the scheduling of processes by an operating system.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p><strong>Prioritizing</strong> enables execution of the (highest priority) process until a higher priority task enters;<br/>The OS/scheduler put processes (jobs) in the right place in a queue in order of priority (accept examples, an I/O operation has higher priority than calculations because it uses less CPU time);</p>\n<p><strong>Time slicing</strong> allows process to execute for a fixed time/each process is given a fixed period of time (time slice) for which the process is allowed to run/;<br/>The scheduler is run once every time slice to choose the next process to run;</p>\n<p><em>Note: Award <strong>[1 max]</strong> if evident that the scheduler software is responsible for organizing all of the processes that need servicing/responsible for looking at what resources are available (CPU time and peripheral devices) /responsible for making decisions about what order to put all the processes in (when to start any particular process, and when to finish it)</em>;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n[N/A]\n</div>",
    "question_id": "18N.1.HL.TZ0.7",
    "topics": [
      "topic-6-resource-management"
    ],
    "subtopics": [
      "6-1-resource-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An oil and gas company has a networked computer system for use of their employees in the Head Office.</p>\n</div><div class=\"specification\">\n<p>The company also uses the internet to enable communication with employees working on exploration and production in many remote geographical areas.</p>\n</div><div class=\"specification\">\n<p>The sub-sea oil and gas exploration and production unit of the company relies on thousands of kilometres of pipeline which are monitored by a computer control system which can detect leaks.</p>\n</div><div class=\"specification\">\n<p>The process of detecting leaks is carried out by sensors which are continuously monitoring changes in the flow and pressure of the liquids in the pipes.</p>\n<p>This data is processed on a computer in the office.</p>\n<p>If any of sensor values are outside of the acceptable parameters stored on a disk in the office, the following error routines are performed:</p>\n<ul>\n<li>an alarm is sounded on the computer at the office</li>\n<li>an email message is sent to managers in the Head Office.</li>\n</ul>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> hardware security measure that will ensure that confidential data from the Head Office cannot be accessed.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> software security measure that will ensure that confidential data from the Head Office cannot be accessed.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Identify <strong>one</strong> network security measure.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the environmental benefit of using a computer control system to monitor the pipeline.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the relationship between sensors, output transducers and processor in this situation.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct a system flowchart to represent the process described above.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each hardware security measure identified up to <strong>[1 max]</strong></em>.</p>\n<p>Retina scans;<br/>Locked doors;<br/>Alarms;<br/>Protection of equipment within the building;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each software security measure identified up to <strong>[1 max]</strong></em>.</p>\n<p>Use of passwords;<br/>Different access rights;<br/>Encryption;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1]</strong> for each network security measure identified up to <strong>[1 max]</strong></em>.</p>\n<p>Encryption;<br/>UserID;<br/>Trusted MAC addresses;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.</p>\n<p>In case of leaks (depending on severity of problem) which could cause pollution (any environmental problem);<br/>Computer control system can react quickly/turn on and off appropriate devices immediately/many times in a short interval of time/(increased efficiency);<br/>Computer control systems are very reliable (will not be tired / will not lose concentration);<br/>(can continue to operate reliably 24 hours a day, 7 days a week) (increased level of safety);<br/>Control systems are reasonably cheap to run comparing to the cost involved in situations which can harm environment (and human health) (reduction of costs);</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.</p>\n<p>Input from flow/pressure sensors is analog;<br/>AD convertors are used to convert this analog data into digital form;<br/>Processor performs / logical and arithmetical / operations;<br/>The result of processing is in digital form so it should be converted (by AD convertors) into analog form;<br/>This signal is sent to output transducers (a device that converts energy from one form to another) (for example, alarm sounds in the office);</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.</p>\n<p><em>Award <strong>[1]</strong> for sensor input.</em><br/><em>Award <strong>[1]</strong> for manual input and validation/configuration.</em><br/><em>Award <strong>[1]</strong> for a disk (which is holding data).</em><br/><em>Award <strong>[1]</strong> for a monitoring process / checking whether input values exceed the allowed range or not.</em><br/><em>Award <strong>[1]</strong> for error routines performed.</em></p>\n<p>Example 1</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>Example 2</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n[N/A]\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "18N.1.HL.TZ0.10",
    "topics": [
      "topic-3-networks",
      "topic-7-control",
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "3-1-networks",
      "topic-7-1-control",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An international school organizes a regional swimming competition for students from 10 different schools. Each school will send a team of 5 to 15 swimmers.</p>\n<p>Each swimmer can enter up to 5 events (such as the “50 m freestyle” or “100 m butterfly”).</p>\n<p>Each event consists of one or more races. A race can be a qualifying heat, or a final. The final has the best 8 swimmers from all the qualifying heats in the event.</p>\n<p>Each race has a maximum of 8 swimmers.</p>\n<p>The UML diagrams for the classes <code>Swimmer</code> and <code>Race</code> are provided below.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>In this scenario, <code>Swimmer</code> objects are aggregated in a <code>Race</code> object.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>mutator method</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State <strong>one</strong> additional instance variable of type <code>boolean</code> which could be added to the class <code>Race</code> as indicated above.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>With reference to both class UMLs provided above, distinguish between a class and an instantiation.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> advantage of using aggregation in this context.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> disadvantage of using aggregation in this context.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct code for the constructor of the class <code>Swimmer</code> that instantiates an object with parameters <code>name</code> and <code>school</code>. The event IDs should be set to “empty” and the times to 0.0</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Many swimmers in the event have names that cannot be represented using basic character sets such as ASCII.</p>\n<p>Describe <strong>one</strong> feature of modern programming languages that allows the wide range of students’ names to be represented correctly.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/>A method that allows/controls changes to a private (hidden) variable;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>.<br/><code>boolean isFinals</code>;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>The class <code>Swimmer</code> is the blueprint of a <code>Swimmer</code> object;<br/>An instantiation is the actual object filled with data;<br/>The class <code>Race</code> stores up to 8 different instantiated objects of the <code>Swimmer</code> class;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying an advantage and <strong>[1]</strong> for an elaboration of the advantage up to <strong>[2 max]</strong></em>.</p>\n<p>Aggregation reduces dependencies;<br/>and therefore reduces maintenance overhead;</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for identifying a disadvantage and <strong>[1]</strong> for an elaboration of the disadvantage up to <strong>[2 max]</strong></em>.</p>\n<p>Aggregation of swimmers in a <img src=\"data:image/png;base64,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\"/> object can cause issues with processing;<br/>e.g. when searching for all heats that a particular swimmer participates in;</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for correct signature including parameters</em>.<br/><em>Award <strong>[1]</strong> for assigning name and school</em>.<br/><em>Award <strong>[1]</strong> for a (any) loop</em>.<br/><em>Award <strong>[1]</strong> for assigning empty to <code>eventID[i]</code></em>.<br/><em>Award <strong>[1]</strong> for assigning <code>0</code> (or <code>0.0</code>) to <code>time[i]</code></em>.</p>\n<p><em>Example answer</em>:</p>\n<pre><strong>public</strong> Swimmer(String name, String school)<br/>{<br/>  this.name = name;<br/>  this.school = school;<br/><strong>  for</strong>(<strong>int</strong> i = 0, i &lt; 5; i++)<br/>  {<br/>    eventID[i] = <strong>\"</strong>empty<strong>\"</strong>;<br/>    time[i] = 0;<br/>  }<br/>}</pre>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Modern programming languages use Unicode to encode characters.<br/>Which uses 16 bits / has about 64 000 characters.<br/>As opposed to ASCII which uses 8 bits / has 256 characters.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was answered well. As this is an OOP course, some reference to objects, classes or encapsulation was expected which most students gave.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Any sensible and non-trivial answer was accepted.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates experienced difficulties in clearly distinguishing between the two terms. For the purpose of this course, a class is the template for objects which are created by instantiation.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates struggled to give a clear advantage (e.g. better organization) for aggregation.</p>\n<div class=\"question_part_label\">d.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates struggled to give a clear disadvantage (e.g. dependencies) for aggregation.</p>\n<div class=\"question_part_label\">d.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was well-answered.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>It was clear from the various guesses that this topic hadn't been covered in some schools.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.10",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-3-program-development",
      "d-1-objects-as-a-programming-concept"
    ]
  },
  {
    "Question": "<div class=\"question\">\n<p>Southend Council in England is considering whether to use Pepper, a humanoid robot, to assist some elderly residents that live in care homes. These elderly residents suffer from memory loss and would rely on Pepper to direct them them to and from the local shops. Pepper uses ambient intelligence and is connected to the Internet of Things (IoT).</p>\n<p>Explain why Southend Council may have concerns about introducing Pepper for this purpose.</p>\n</div>",
    "Markscheme": "<div class=\"question\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/>The journey from the care home to the local shops may lead to Pepper encountering a large range of environments that may go beyond the ambient intelligence that Pepper posseses;<br/>Which may mean that the information that Pepper can obtain from the environment by using ambient intelligence may be limited and lead to sub-optimal decisions being taken, some of which could have serious consequences;<br/>Some of the residents may have trust issues associated with relying on Pepper, or they have be too trusting and delegate all decision making to Pepper when it is not appropriate;<br/>Families and carers for these residents may have concerns about what information is being collected and shared by Pepper;<br/>There may be issues of accountability that have not been resolved should an accident occur when Pepper is acting as a decision maker;<br/>There may be technical issues such as black spots where Pepper is unable to communicate with the IoT that need to be addressed;<br/>The trialing of Pepper may not have been sufficiently rigorous to train Pepper to act “correctly” in any given situation;</p>\n</div>",
    "Examiners report": "<div class=\"question\">\n<p>Candidates came up with a variety of reasons for not continuing with this project they clearly did not think it was a good idea.</p>\n</div>",
    "question_id": "19N.2.HL.TZ0.13",
    "topics": [
      "option-c-web-science"
    ],
    "subtopics": [
      "c-6-the-intelligent-web"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The array <code>line</code> in <code>POSline</code> is now replaced by a singly linked <code>list</code> of <code>CartNode</code> objects. The class <code>CartNode</code> has been defined as follows.</p>\n<pre><strong>public class</strong> CartNode<br/>{<br/><strong>   private</strong> Cart myCart;<br/><strong>   private</strong> CartNode next;<br/><strong>   <br/>   public</strong> CartNode(Cart aCart)<br/>   {<br/>      this.myCart = aCart;<br/>      this.next = null;<br/>   }<br/><strong>   public</strong> Cart getCart(){ return this.myCart; }<br/><strong>   public</strong> CartNode getNext(){ return this.next; }<br/><strong>   public</strong> void setNext(CartNode nextNode)<br/>   {<br/>      this.next = nextNode;<br/>   }<br/>}</pre>\n</div><div class=\"specification\">\n<p>The class <code>POSlist</code> has been implemented with the standard list methods <code>addLast</code> and <code>removeFirst</code> that act on <code>list</code>.</p>\n</div><div class=\"specification\">\n<p>A method <code>leaveList(int n)</code> is required in the class <code>POSlist</code>, similar to the method <code>leaveLine(int n)</code> that was added to the class <code>POSline</code>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>object reference</em>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using object references, construct the method <code>public Cart removeFirst()</code> that removes the first <code>CartNode</code> object from <code>list</code>. The method must return the <code>Cart</code> object in that node or <code>null</code> if <code>list</code> is empty.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Sketch the linked list <code>list</code> after running the following code fragment where <code>cart1, cart2, cart3, cart4</code> and <code>cart5</code> have been instantiated as objects of the class <code>Cart</code>.</p>\n<pre>POSlist queueList = new POSlist()<br/>queueList.addLast(cart2);<br/>queueList.addLast(cart1);<br/>queueList.addLast(cart4);<br/>queueList.removeFirst();<br/>queueList.addLast(cart5);<br/>queueList.addLast(cart3);<br/>queueList.removeFirst();</pre>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> feature of the abstract data structure <em>queue</em> that makes it unsuitable to implement customers waiting in line.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Using object references, construct the method <code>public Cart leaveList(int n)</code> that removes the nth <code>CartNode</code> object from <code>list</code>. The method must return the <code>Cart</code> object in that node.</p>\n<p>You may assume that the nth <code>CartNode</code> object exists in the list.<br/>You may use any method declared or developed.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the importance of using coding style and naming conventions when programming.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>A pointer to a memory location;<br/>where the object is stored;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for declaring a </em><code>result</code><em> variable (or similar)</em><br/><em>Award <strong>[1]</strong> for correct use of </em><code>.getCart()</code><br/><em>Award <strong>[1]</strong> for reassigning the head of the list</em><br/><em>Award <strong>[1]</strong> for returning the correct result (either the Cart or null)</em></p>\n<pre>public Cart removeFirst()<br/>{<br/>    Cart result = null;<br/><br/>    if (head != null)<br/>    {<br/>       result = head.getCart();<br/>       head = head.getNext();<br/><br/>    }<br/>    return result;<br/>}</pre>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for a three nodes in order.</em><br/><em>Award <strong>[1]</strong> root pointer (with or without identifier) <span style=\"text-decoration:underline;\"><strong>and</strong></span> null pointer.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The abstract data structure queue is a FIFO structure / only allows addition at the end and removal from the front;<br/>this is not sufficient because customers can change lines at any time;</p>\n<p>Has no fixed length;<br/>Which could lead to unmanageable/very long queues;</p>\n<p><em>Note: do not award marks for responses that focus on dynamic vs static, since queues can be implemented either way</em>.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>. <br/><em>Award <strong>[1]</strong> for declaring all variables used (including loop variable </em><code>i</code><em>);</em><br/><em>Award <strong>[1]</strong> for initialising variable(s) for list traversal;</em><br/><em>Award <strong>[1]</strong> for dealing separately with the case where n equals 1;</em><br/><em>Award <strong>[1]</strong> for attempted looping through the linked list until the correct position;</em><br/><em>Award <strong>[1]</strong> for correct loop using </em><code>curr = curr.getNext()</code><em>; (or similar)</em><br/><em>Award <strong>[1]</strong> for correctly rearranging references to unhook the nth node;</em><br/><em>Award <strong>[1]</strong> for returning the correct result;</em></p>\n<p><em>Example answer using two <code>CartNode</code> variables:</em></p>\n<pre><strong>public</strong> Cart leaveList(<strong>int</strong> n){<br/>   Cart result = <strong>null</strong>;        // explicit default result<br/>   CartNode prev = <strong>null</strong>;<br/>   CartNode curr = head;<br/><strong><br/>   if</strong> (n==1){<br/>      result = removeFirst();<br/>   }<br/>   else{<br/><strong>      int</strong> i=1;<br/><strong>      while</strong> (i&lt;n){           // no need to check for null<br/>         prev = curr;<br/>         curr = curr.getNext();<br/>         i++;<br/>      }<br/>      result = curr.getCart();<br/>      prev.setNext(curr.getNext());<br/>   }<br/><strong>   return</strong> result;<br/>}</pre>\n<p><em>Example answer using one <code>CartNode</code> variable:</em></p>\n<pre><strong>public Cart</strong> leaveList(<strong>int</strong> n){<br/>   Cart result = <strong>null</strong>;<br/>   CartNode curr = head;<br/><strong><br/>   if</strong> (n==1){<br/>      result = removeFirst();<br/>   }<br/>   else{<br/><strong>      int</strong> i=1;<br/><strong>      while</strong> (i&lt;n-1){           // no need to check for null<br/>         curr = curr.getNext();<br/>         i++;<br/>      }                        // curr points to node (n-1)<br/>      result = curr.getNext.getCart();<br/>      curr.setNext(curr.getNext().getNext());<br/>   }<br/>   <strong>return</strong> result;<br/>}</pre>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong> for including any of the following elements on the discussion</em></p>\n<p>Indentation / Use of white space<br/>Annotations<br/>Meaningful identifiers<br/>Capitalization conventions</p>\n<p><em>Award <strong>[2 max]</strong> for including any of the following reasons in the discussion</em><br/>Helps understanding / reading;<br/>For easer maintenance / extension;<br/>For better team-work / collaboration / development;<br/>To help with de-bugging;</p>\n<p><em>Mark as <strong>[2]</strong> and<strong> [2]</strong></em>.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a straight-forward definition.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Required candidates to show their understanding of the use of pointers for moving through a linked list. Many were unprepared for this, although it is an established part of the course, and incorrectly attempted to make use of the Linked List library class along with associated methods.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many missed out the use of both head and null pointers in sketching the linked list.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was well-answered.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Required candidates to show their understanding of the use of pointers for moving through a linked list. Many were unprepared for this, although it is an established part of the course, and incorrectly attempted to make use of the Linked List library class along with associated methods.</p>\n<div class=\"question_part_label\">e.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was a question that demanded specific examples which were not always given.</p>\n<div class=\"question_part_label\">f.</div>\n</div>",
    "question_id": "19N.2.HL.TZ0.17",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-4-advanced-program-development"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>A generic <code>Event</code> class is defined as follows:</p>\n<pre><strong>class</strong> Event<br/>{<br/><strong>  private</strong> String eventID;<br/><strong>  private int</strong> numberOfRaces;<br/><strong>  private</strong> Race[] races;<br/><strong>  private</strong> Race finals;<br/><strong>  <br/>  public</strong> Event(String ID, int numberOfRaces)<br/>  {<br/>    eventID = ID;<br/>    races = <strong>new</strong> Race[numberOfRaces];<br/><strong>    for</strong>(<strong>int</strong> i = 0; i &lt; numberOfRaces; i++)<br/>    {<br/>      races[i] = <strong>new</strong> Race();<br/>    }<br/>    finals = <strong>new</strong> Race();<br/>  }<br/><strong><br/>  public void</strong> addSwimmers()<br/>  {<br/>     // fills the qualifying heats with swimmers<br/>  }<br/><strong><br/>  public void</strong> fillFinals()<br/>  {<br/>    // fills the finals race with the best 8 from the qualifying heats<br/>  }<br/>  // more methods()<br/>}</pre>\n</div><div class=\"specification\">\n<p>The <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>Event</mi></math> class above assumes that the event has more than 8 swimmers and requires qualifying heats. However, an event with less than 9 swimmers has no qualifying heats, so the original <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>Event</mi></math> class was inherited by a new class <math style=\"font-family:'Courier New'\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>FinalsOnlyEvent</mi></math>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>The same method identifier <code>addSwimmers</code> is used in both classes <code>Race</code> and <code>Event</code>.</p>\n<p>Explain why this does not cause a conflict.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> advantages of the OOP feature “inheritance”.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how method overriding can help to create the new class <code>FinalsOnlyEvent</code>.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/><em>Each method is defined within its own class.</em><br/><em>Each method is called within an object of that class.</em><br/><em>Therefore the compiler knows which method to use.</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for an advantage and <strong>[1]</strong> for an elaboration up to <strong>[2 max]</strong></em>.<br/><em>Mark as<strong> [2]</strong> and <strong>[2]</strong></em>.</p>\n<p>It promotes code reuse;<br/>Because the parent object holds common data and action;</p>\n<p>It reduces maintenance overhead;<br/>Because you only have to update the parent;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for a clear understanding of method overriding and <strong>[1]</strong> for relating it to this situation up to <strong>[2 max]</strong></em>.</p>\n<p>Method overriding redefines/replaces a method from the inherited class;<br/>The constructor could only instantiate the <code>finals</code> object;<br/>The method <code>addSwimmers()</code> could fill <code>finals</code> directly;<br/>The method <code>fillFinals()</code> could do nothing;</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates wanted this to be about polymorphism and overriding, but the two occurrences of this methods were quite simply in different classes and would be called and distinguished between by their respective objects.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>A variety of reasons were accepted with most candidates going for code-reuse and the extensibility features.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This wasn't well-answered, probably because candidates were unsure of the exact structure of the <em>FinalsOnlyEvent</em> class.</p>\n<div class=\"question_part_label\">c.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.11",
    "topics": [
      "option-d-object-oriented-programming"
    ],
    "subtopics": [
      "d-2-features-of-oop"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>The <em>Driving Licensing Agency</em> stores information about individuals who hold a driving license and/or own vehicles.<br/>The following rules apply:</p>\n<ul>\n<li>Each individual may only hold one driving license.</li>\n<li>Each individual may own more than one vehicle.</li>\n<li>Each vehicle may be owned by one individual only.</li>\n</ul>\n</div><div class=\"specification\">\n<p>When an individual applies for a driving license, they have to complete a license application form. The following is an extract from that form:</p>\n<p style=\"text-align: center;\"><img src=\"data:image/png;base64,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\"/></p>\n</div><div class=\"specification\">\n<p>The data in the form shown above is stored in the <strong>Person</strong> table. The license application form also requires an individual’s medical information. This is stored in a table called <strong>PersonMedical</strong>.</p>\n<p>The following extract is a sample of the medical questions that are asked.</p>\n<p style=\"text-align: center;\"><strong>Figure 2: A sample of the medical questions asked on the license application form</strong></p>\n<p style=\"text-align: center;\"><strong><img src=\"data:image/png;base64,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\"/></strong></p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the entity-relationship diagram (ERD) that shows the relationship between the individual, their driving license, and their vehicle(s).</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain why <em>Date of birth</em> has been separated into three fields.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain <strong>two</strong> reasons why medical information should not be stored in the <strong>Person</strong> table.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> issues caused by storing redundant data.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> situations where data stored by the <em>Driving Licensing Agency</em> may need to be open to interrogation by other parties.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p><em>Accept either diagram. License, Individual, and Vehicle correctly positioned</em><br/><em>Award <strong>[1]</strong> for 1 to 1.</em><br/><em>Award <strong>[1]</strong> for 1 to m.</em></p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>The order that a date is written varies by country, e.g. dd/mm/yyyy in UK, mm/dd/yyyy in US;<br/>Some dates, such as 01 June 2018 (01/06/2018) could be confused with 06 January 2018 (06/01/2018);<br/>leading to the incorrect information being stored and consequent decisions, e.g. license expired at the wrong time due to incorrect age;</p>\n<p>The Date/String datatype will make analysing slower;<br/>integers can be analysed more quickly than Date/String datatypes;<br/>because functions will be used to split/extract the data;</p>\n<p>The Date datatype will make validating data more difficult.<br/>because it is easier to validate separate integer fields;<br/>rather than one entire data that will need to be split by functions;</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.</p>\n<p><strong>Normalisation</strong><br/>Medical information is only stored if the person has a medical condition / some people will have no medical information stored / some people may have more than one medical conditions;<br/>this requires an additional table / normalisation requires a separate table / one to many relationship;<br/>leaving in the person table will increase storage capacity / empty fields take up space;</p>\n<p><strong>Privacy</strong><br/>All employees will need access to the person table and that would include sensitive medical information;<br/>Protects people’s privacy / data needs to comply with the Data Protection Act / private medical data seen by non-authorised personnel may cause harm to the licence applicant / potentially result in legal action;<br/>having confidential data in a separate table allows that table to be only available to people with a certain permission level;</p>\n<p><strong>Updates</strong><br/>Person table is also likely to be less permanent and need updating more often;<br/>medical information rarely gets updated so is more permanent;<br/>the person table may be useful in other applications, as it offers a way of identifying citizens/the medical data is not likely to be used by other applications;</p>\n<p><em>Mark as [3] + [3]</em></p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>May lead to update/deletion anomalies;<br/>Address changes may result in duplicate addresses;<br/>so letters / fines may go to the wrong address;<br/>Storing data multiple times wastes storage space;<br/>and may slow down data retrieval / data entry; </p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/><em>Accept any suitable example</em></p>\n<p><strong>Police access</strong><br/>It is a legal requirement for the VDLA to give access to the police;<br/>e.g. a speeding vehicle / an accident / linked to a police investigation;<br/>so the police will need to look up the details of the person that owns the car;<br/>facial scanning software may be cross-referenced with the car owner’s driving license photo;<br/>so that police have a way to check the identity of the person driving the car;</p>\n<p><strong>Insurance company access</strong><br/>A person taking out car insurance signs a consent form to give VDLA access to their records;<br/>this would allow the insurance company to build up a profile e.g., check for driving offences, see how long that person had owned a car;<br/>thus, a quote could be created quickly / minimal effort;<br/>provides proof of driver eligibility (i.e., not serving a driving ban);</p>\n<p><strong>Medical access</strong><br/>Doctors may require access to an accident victim’s records;<br/>To check blood type etc.;</p>\n<p><em>Mark as [2] + [2]</em></p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most of the students clearly identified the main entities, their correct position and cardinality in the ERD diagram. Few failed to establish and correctly label the relationships.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was not well answered with many students giving vague generic answers when explaining the separation of the Date of Birth into three fields. Very few students included reasons related to data type making easier analysis / validation / correct formatting.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question was not answered well with most candidates struggling to explain reasons for having a separate table for medical information. However, the majority of students were able to explain privacy issues related to the question, but not able to explain other reasons such as normalization, updates or user access rights.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Candidates tended to comment on the waste of storage space and to mention update/deletion anomalies, but few were able to elaborate on these aspects or outline others.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This question explicitly asked for two different situations in which the database had to be open to interrogation by other parties. Successful candidates were able to combine knowledge and real-world context and provide situations with concrete examples of third parties and the reasons for interrogating the DB.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.1",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-2-the-relational-database-model",
      "a-1-basic-concepts",
      "a-3-further-aspects-of-database-management"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p>An <code>Event</code> has been instantiated with 2 qualifying heats for a total of 11 swimmers.</p>\n<p><code>Event free100 = <strong>new</strong> Event(\"100 m free style\",2);</code></p>\n<p>The swimmers were added to the two <code>Race</code> arrays and after the races, their times were recorded as shown in the table.</p>\n<p>(For the purpose of this question, the name represents the full swimmer object.)</p>\n<p><img src=\"data:image/png;base64,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\"/></p>\n<p>The method <code>fillFinals()</code> will select the 8 fastest swimmers, in ascending order of time, from both <code>swimmer</code> arrays and copy them to the <code>swimmer</code> array in the <code>finals</code> race.</p>\n</div><div class=\"specification\">\n<p>To help with this selection, all entries from <code>races[0]</code> and <code>races[1]</code> will be copied into two new parallel arrays of size 16, one array for swimmers and one array for their times.</p>\n</div><div class=\"specification\">\n<p>The two temporary arrays will be sorted using the following code.</p>\n<pre><strong>int</strong> i,j;<br/>Swimmer swapSwimmer;<br/><strong>double</strong> swapTime;<br/><strong>for</strong>(i = 0; i &lt; 15; i++)<br/>{<br/><strong>  for</strong>(j = 0; j &lt; 15; j++)<br/>  {<br/><strong>    if</strong>(tempTime[j] &gt; tempTime[j + 1])      // if wrong order then…<br/>    {<br/>      swapSwimmer = tempSwimmer[j];        // swap the swimmer and…<br/>      tempSwimmer[j] = tempSwimmer[j + 1];<br/>      tempSwimmer[j + 1] = swapSwimmer;<br/>      swapTime = tempTime[j];              // swap the time<br/>      tempTime[j] = tempTime[j + 1];<br/>      tempTime[j + 1] = swapTime;<br/>    }<br/>  }<br/>}</pre>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Sketch the resulting<code> swimmer</code> array in <code>finals</code>.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the code fragment for the given situation that will copy swimmers and times into two parallel arrays named <code>tempSwimmer</code> and <code>tempTime</code>.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>State the name of this sorting algorithm.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>two</strong> improvements to this code that would make the algorithm more efficient.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Construct the code fragment that will copy the names of the 8 fastest swimmers in ascending order of time from the array <code>tempSwimmer</code> to the array <code>swimmers</code> in the race <code>finals</code>.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for array of 8 slots with array name.</em><br/><em>Award <strong>[1]</strong> for correct entries (in any order).</em><br/><em>Award <strong>[1]</strong> for correct order.</em></p>\n<p><em><img src=\"data:image/png;base64,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\"/></em></p>\n<p>Do not penalize the lack of subscripts.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for correctly declaring 2 arrays of size 16.</em><br/><em>Award <strong>[1]</strong> for correct outer loop.</em><br/><em>Award <strong>[1]</strong> for correct inner loop.</em><br/><em>Award <strong>[1]</strong> for copying swimmer object.</em><br/><em>Award <strong>[1]</strong> for copying time.</em><br/><em>Award <strong>[1]</strong> for incrementing the index of the new arrays.</em></p>\n<p><em>Example answers:</em></p>\n<pre>Swimmer[] tempSwim = new Swimmer[16];<br/><strong>double</strong>[] tempTime = <strong>new double</strong>[16];<br/><strong>int</strong> newIndex = 0;<br/><strong>for</strong>(<strong>int</strong> i = 0; i &lt; 2; i++){<br/><strong>  for</strong>(<strong>int</strong> j = 0; j &lt; 8; j++){<br/>    tempSwim.swimmer[newIndex] = races[i].swimmer[j];<br/>    tempTime.time[newIndex] = races[i].time[j];<br/>    newIndex++;<br/>  }<br/>}</pre>\n<p><em><strong>Note</strong>: that the question asks for all entries to be copied. However, do not penalize \"efficient\" solutions that avoid copying the null and 0 entries.</em></p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Bubblesort;</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for stating an improvement and <strong>[1]</strong> for an elaboration up to <strong>[2 max]</strong></em>.<br/><em>Mark as <strong>[2]</strong> and <strong>[2]</strong></em>.</p>\n<p>Include a flag “swapped”;<br/>That can help stop the outer loop if there is a pass through the inner loop with no swap;<br/>Limit the inner loop by deducting the outer loop counter;<br/>So that the sorted elements are no longer compared;</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for correctly instantiating variables.</em><br/><em>Award <strong>[1]</strong> for correct loop condition (<code>count</code> &lt; 8).</em><br/><em>Award <strong>[1]</strong> for checking for a 0 time (or null entry).</em><br/><em>Award <strong>[1]</strong> for correctly copying.</em><br/><em>Award <strong>[1]</strong> for incrementing <code>count</code> in the right place.</em><br/><em>Award <strong>[1]</strong> for incrementing <code>k</code> in the right place.</em></p>\n<p><em>Example answers:</em></p>\n<pre><strong>int</strong> k = 0;<br/><strong>int</strong> count = 0;<br/><strong>while</strong>(count &lt; 8) &amp;&amp; (k &lt; 16){<br/><strong>  if</strong>(tempSwim[k] != 0){<br/>    finals.swimmer[count] = tempSwim[k];<br/>    count++;<br/>  } <br/>  k++;<br/>}</pre>\n<p><em><strong>Note</strong>: Do not penalize for not including loop condition (<code>k &lt; 16</code>)</em></p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was well-answered.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>This was the first significant piece of coding on the paper and, as in previous papers, divided the students into those who were comfortable with coding and those who weren't. From the former came a variety of solutions, some of which were quite innovative. The complexity came from ensuring that the correct indices were used for the different array items and for dealing correctly with private variables outside of their class.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Almost all correctly gave <em>Bubble Sort</em>, but clear details of improvements that could be made were limited to the candidates who were adept at programming.</p>\n<div class=\"question_part_label\">c.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Almost all correctly gave <em>Bubble Sort</em>, but clear details of improvements that could be made were limited to the candidates who were adept at programming.</p>\n<div class=\"question_part_label\">c.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates failed to realise that the 8 fastest swimmers were not the first 8 entries in the sorted array as this would give null entries.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "19M.2.SL.TZ0.12",
    "topics": [
      "option-d-object-oriented-programming",
      "topic-4-computational-thinking-problem-solving-and-programming"
    ],
    "subtopics": [
      "d-3-program-development",
      "4-2-connecting-computational-thinking-and-program-design"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>Billetmania</em> is an online company that sells tickets for theatre performances and music concerts. After a customer has chosen their seats, they can pay for the tickets through a secure online payment system. Once the transaction has been completed, the customer receives an email receipt.</p>\n</div><div class=\"specification\">\n<p>An information system and a database are used for <em>Billetmania</em>’s day-to-day operations.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Define the term <em>database transaction</em>.</p>\n<div class=\"marks\">[1]</div>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline how the <em>Billetmania</em> information system would utilize a database.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain the importance of transaction durability to <em>Billetmania</em> when clients book tickets.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Explain how the <em>Billetmania</em> database management system ensures that a seat is not booked by two people simultaneously.</p>\n<div class=\"marks\">[4]</div>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[1 max]</strong></em>. <br/>A logical unit of work that is executed in full or not at all;<br/>A transaction is a single logical operation that comprises of a sequence of database operations;<br/>A transation satisfies the ACID (Atomicity, Consistency, Isolation, Durability) properties;</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>. <br/>The database would store details of concerts, tickets sold, customers;<br/>An information system would access the database to present data it in a way that aids informs managers / aids decision making (e.g. charts of ticket sales);</p>\n<p><em>Accept any other reasonable example</em>.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>. <br/>Durability ensures that transactions are saved permanently and do not accidentally disappear or get erased;<br/><em>Billetmania</em> does not need to worry the transaction being lost even in the event of power loss, crashes, or errors;<br/>A seat in a theatre is guaranteed even if the database crashes;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[4 max]</strong></em>. <br/>Concurrency allows multiple users try to book tickets at the same time / complete a transaction at the same time;<br/>Concurrency prevents access by more than one user to the same row/record;<br/>Concurrency uses row locking;<br/>Select seats are locked for a short period of time to ensure that they aren’t double booked/overwritten;</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>A standard question that was not well answered. Many candidates presented vague responses that did not define the concept of <em>Database Transaction</em>, some of them seemed to have confusion with the concept of <em>Data Migration</em>.</p>\n<div class=\"question_part_label\">a.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to outline the role of the database in the given scenario by using correct examples but failed to point out how the information system would utilize the database access.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Some candidates provided detailed responses explaining durability in context by including characteristics of persistence in the event of system failure, but many of the responses were not specific enough and did not correctly relate the response to the given scenario.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many responses were incomplete and failed to provide a proper explanation. Some of them were very generic. Most candidates were aware of the row locking concept and its application in the given scenario.</p>\n<div class=\"question_part_label\">d.</div>\n</div>",
    "question_id": "20N.2.SL.TZ0.2",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-1-basic-concepts",
      "a-2-the-relational-database-model"
    ]
  },
  {
    "Question": "<div class=\"specification\">\n<p><em>ZCC</em> has a chain of offices that sell different types of paper to customers all over the world. They have data stored in their data warehouses that will help them make important marketing decisions for the future, as they have plans to diversify into other products like gift-wrappers, scribble-pads, stationery, books and calculators.</p>\n</div><div class=\"specification\">\n<p><em>ZCC</em> is going to use data mining techniques to discover patterns in their data.</p>\n</div><div class=\"specification\">\n<p>The company has customers who have missed the payment deadline for their purchases from <em>ZCC</em>.</p>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why data warehousing is time dependent.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline <strong>one</strong> reason why <em>ZCC</em> uses a data warehouse.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Outline why transformation of the data is necessary prior to it being loaded into the data warehouse.</p>\n<div class=\"marks\">[2]</div>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Compare cluster analysis and classification as techniques for discovering patterns in <em>ZCC</em>'s data.</p>\n<div class=\"marks\">[6]</div>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p>Describe how the process of deviation detection can be applied to identify customers who are likely to miss the payment deadline for their purchases from <em>ZCC</em>.</p>\n<div class=\"marks\">[3]</div>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px; padding-right: 20px;\">\n<p><em>ZCC</em> is aware that other data mining and detection techniques will allow more informed marketing decisions to be made.</p>\n<p>Explain how database segmentation and link analysis can be used by <em>ZCC</em> to improve their marketing strategies.</p>\n<div class=\"marks\">[5]</div>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Markscheme": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Data warehouses contain both historical and current data;<br/>Timestamps are required to compare data from different times;</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>Increased Query and System Performance;<br/>data warehouse is built for analysis and retrieval of data rather than efficient upkeep of individual records (i.e. transactions);</p>\n<p>Timely Access to Data;<br/>ETL, are used within a data warehouse environment. These routines consolidate data from multiple source systems and transform the data into a useful format that enable quick querying;</p>\n<p>Enhanced Data Quality and Consistency;<br/>Data from the various business units and departments is standardized and the inconsistent nature of data from the different sources is removed;<br/>Individual business units will start to utilize the same data repository as the source system for their individual queries and reports;</p>\n<p>Historical Intelligence;<br/>Data warehouse stores large amounts of historical data and time-period analysis, trend analysis, and trend prediction thus allowing for advanced reporting and analysis of multiple time-periods;</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[2 max]</strong></em>.<br/>When the data is collected from different sources each source will have their own standards if we have two different data sources A and B;<br/>Selection of the data that is going to be useful in analysis – the different offices will have data relevant only to them, such as staff names, etc.;<br/>Standardization of the data – the company may have imposed a standard on all its offices, but this is not always the case, and the data will certainly have to be Checked, e.g. date formats are different in different countries;<br/>Other transformation techniques that students may give as examples are more like:</p>\n<ul>\n<li>Cleaning (e.g. “Male” to “M” and “Female” to “F” etc.)</li>\n<li>Filtering (e.g. selecting only certain columns to load)</li>\n<li>Enriching (e.g. Full name to First Name , Middle Name , Last Name)</li>\n<li>Splitting a column into multiple columns and vice versa.</li>\n</ul>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[6 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for a description of cluster analysis</em><br/><em>Award <strong>[1]</strong> for description of cluster analysis being used to find patterns</em><br/><em>Award <strong>[1]</strong> for a description of link analysis</em><br/><em>Award <strong>[1]</strong> for a description of link analysis being used to find patterns</em><br/><em>Award up to <strong>[2]</strong> for comparison between the two techniques</em></p>\n<p><strong>Cluster Analysis</strong><br/>Cluster analysis groups customers by age / according to different factors, such as region/location;<br/>Therefore, it enables comparison between groups;</p>\n<p><strong>Classification</strong><br/>A classifier or model is developed using training sets of data;<br/>New data is then added to the model and compared against the predicted outcomes / new classifiers may be developed;</p>\n<p><strong>Discussion</strong><br/>Classification requires prior knowledge of the customer base, cluster analysis does not;<br/>Data can be classed in new samples using classification whereas for cluster analysis only suggests groups based upon patterns in data;<br/>Labelled samples from a set of classes is required for classification whereas for cluster analysis unlabelled samples will do;</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[3 max]</strong></em>.<br/>Access the customer payment details for purchases of fairly large orders from the data warehouse;<br/>For a given group (and for a particular period/given a range for timestamp) of customers;<br/>Identify the outliers/customers who have defaulted more than a fixed number of times in payment after running the deviation detection (multiple if-then-else statements) algorithm;<br/>List the names of those customers;</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p><em>Award <strong>[5 max]</strong></em>.<br/><em>Award <strong>[1]</strong> for an outline of customer segmentation</em><br/><em>Award <strong>[1]</strong> for an outline of link analysis</em><br/><em>Award up to <strong>[3 max]</strong> for use by ZCC to improve marketing strategies</em></p>\n<p><strong>Customer segmentation</strong><br/>Divides a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, order of the type of paper, frequency of orders placed and the size of the orders normally placed;<br/>assign each customer to one of the segments;<br/>A typical segmentation makes each segment distinct from other segments (different segments have different needs), it is homogeneous within the segment (exhibits common needs);<br/>segmenting is using borders to form groups;<br/>Segmentation groups objects into similar groups;<br/>The resulting groups contain members that are more similar to each other than they are to other groups;</p>\n<p><strong>Link Analysis</strong><br/>Load a claim and runs a query back to the database to find all other claims sharing any similar attributes;<br/>show matches on the address of a claimant in the original case being investigated;<br/>Now combine matches-merge identical nodes. So, we can more easily see unusual connections;<br/>Once seen a suspicious link, accept or escalate;<br/>Representing data as a network offers an engaging way for analysts to rapidly understand events;</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "Examiners report": "<div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to identify why data warehouses required timed data.</p>\n<div class=\"question_part_label\">a.i.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates were unable to clearly explain why data warehouses are advantageous for a company.</p>\n<div class=\"question_part_label\">a.ii.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates were able to describe why the data from different data sources needs to be standardized before being loaded into a data warehouse.</p>\n<div class=\"question_part_label\">b.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Many candidates were able to give generic descriptions of cluster analysis and classification, but they were unable to discuss the differences between the two in any detail.</p>\n<div class=\"question_part_label\">c.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>Most candidates demonstrated a good understanding of data deviation in this situation.</p>\n<div class=\"question_part_label\">d.</div>\n</div><div class=\"question\" style=\"padding-left: 20px;\">\n<p>The majority of candidates were only able to discuss data segmentation and link analysis at an abstract level.</p>\n<div class=\"question_part_label\">e.</div>\n</div>",
    "question_id": "19M.2.HL.TZ0.4",
    "topics": [
      "option-a-databases"
    ],
    "subtopics": [
      "a-4-further-database-models-and-database-analysis"
    ]
  }
]