Loading…

Worked solutions

IB Computer Science SL · Paper 2

Computer Science, Standard Level

Session: November 2024
Marks: 180
Duration: 1 hour
Calculator: Not allowed
Questions: 12

Keep your copy of the paper open alongside. We don't reproduce the questions: each part below is labelled to match the November 2024 Global paper, which your school or the IB can provide.

Practice mode

Hide our working until you've had a go.

Option A: Databases · Option

Questions 1–3 · 45 marks

Question 1

8 marks

Police investigation relational model, referential integrity, and database views

Databases

(a)

2 marks

Analyze the entity relationships described in the scenario:
A police officer may conduct multiple interviews, but each interview is conducted by one officer (1-to-many relationship from POLICE_OFFICER to INTERVIEW).
A witness may participate in multiple interviews, but each interview involves one specific witness (1-to-many relationship from WITNESS to INTERVIEW).
Construct the Entity-Relationship Diagram (ERD):
POLICE_OFFICER (1) -----< (conducts) >----- (M) INTERVIEW (M) -----< (involves) >----- (1) WITNESS

Mentor tip

Represent INTERVIEW as the junction entity with 'many' crow's feet terminating on it from both 1-side parent entities.

(b)

3 marks

Reason 1 - Preventing orphan records: Enforcing foreign key constraints ensures that an interview record cannot reference a non-existent OfficerID or WitnessID.
Reason 2 - Consistency across relational operations: Guarantees that linked attributes across related tables remain synchronized during insertions and modifications.
Reason 3 - Cascading deletes and updates: Enables controlled cascading updates or deletions so that removing an officer or witness safely handles or restricts dependent interview logs.

Mentor tip

Focus on orphan record prevention, cross-table referential consistency, and cascading operations.

(c)

3 marks

Virtual relation definition: A view acts as a saved SQL query (`CREATE VIEW ViewName AS SELECT ... WHERE ...`) presenting a customized subset of columns and rows without storing redundant data.
Data abstraction and simplified reporting: Complex multi-table joins and filtering conditions are encapsulated into a simple virtual table for end users and routine weekly reports.
Security and access control: Restricts visibility so officers only inspect fields necessary for their duties, shielding sensitive or confidential personal data.

Mentor tip

Describe a view as a stored query that provides a dynamic virtual subset without creating separate physical tables.

Question 2

15 marks

Normalization, secondary keys, and 3NF schema design for police records

Databases

(a)

1 mark

A secondary key is a non-primary attribute (or alternate unique key) that is indexed to enable rapid search and retrieval of records based on frequently queried fields (e.g., searching by WitnessName or CrimeLocation).

Mentor tip

Clarify that a secondary key facilitates searching on non-primary-key columns.

(b)(i)

2 marks

Violation of uniqueness: A witness can be interviewed more than once by the same officer regarding the same crime, generating duplicate `(WitnessID, CrimeID, OfficerID)` combinations and violating primary key uniqueness.
Need for timestamp or surrogate key: To uniquely distinguish records, the key would need to incorporate `DateTimeStatement`, or preferably use an auto-incrementing surrogate `InterviewID`.

Mentor tip

Highlight that repeating an interview for the same crime and officer causes identical composite keys.

(b)(ii)

4 marks

Partial functional dependency (violates 2NF): Non-key attributes depend on only a subset of the composite primary key; for example, `CrimeLocation` depends strictly on `CrimeID` rather than the entire key `(WitnessID, CrimeID, OfficerID)`.
Transitive dependency (violates 3NF): Non-key attributes depend on other non-key attributes; for instance, `StationPhone` is functionally determined by `PoliceStation` rather than directly by the primary key.

Mentor tip

Cite specific attributes: CrimeLocation depending only on CrimeID (partial dependency) and StationPhone depending on PoliceStation (transitive dependency).

(c)

2 marks

Preservation of leading zeroes: Phone numbers frequently begin with leading zeroes (e.g., `01029238484`), which numerical data types truncate.
Inclusion of non-numeric characters: Alphanumeric types accommodate international formatting symbols such as `+`, parentheses, and hyphens, and prevent accidental arithmetic operations.

Mentor tip

Emphasize leading zero retention and non-arithmetic characters.

(d)

6 marks

Decompose the unnormalized structure into 3NF relations to eliminate repeating groups, partial dependencies, and transitive dependencies:
1. CRIME(CrimeID, CrimeType, CrimeAddress, CrimeDateTime) - Primary Key: CrimeID
2. OFFICER(OfficerID, OfficerName) - Primary Key: OfficerID
3. CRIMEOFFICER(CrimeID*, OfficerID*) - Composite Primary Key: (CrimeID, OfficerID)
4. VICTIM(VictimID, VictimName, VictimPhone) - Primary Key: VictimID
5. VICTIMCRIME(CrimeID*, VictimID*) - Composite Primary Key: (CrimeID, VictimID)
6. EVIDENCE(EvidenceID, EvidenceType, EvidenceDesc, CrimeID*) - Primary Key: EvidenceID, Foreign Key: CrimeID

Mentor tip

Ensure junction tables handle many-to-many links between Crimes and Officers, and Crimes and Victims, with an EvidenceID primary key for items.

Question 3

22 marks

Concurrency control, centralized databases, data mining, and GDPR compliance

Databases

(a)

2 marks

Exclusive write locking: When the first officer opens a witness record for editing, the DBMS places an exclusive lock on that record.
Concurrency resolution: The second officer is granted read-only access or made to wait until the first transaction commits and releases the lock, preventing race conditions and lost updates.

Mentor tip

Explain exclusive write locks and queuing/read-only restriction for concurrent users.

(b)

5 marks

Advantages of centralization:
- Eliminates data duplication and update anomalies across regional stations, ensuring all police personnel access synchronized, up-to-date records.
- Accelerates multi-jurisdictional investigations by enabling seamless cross-referencing of criminal profiles without contacting external local repositories.
Disadvantages of centralization:
- Creates a single point of failure; hardware outages or network disruptions can disable police operations nationwide.
- Presents a high-value target for unauthorized intrusion or distributed denial-of-service (DDoS) attacks, creating widespread privacy exposure.
Conclusion: Centralization drastically improves inter-agency operational efficiency, provided automated failover mirrors and stringent zero-trust encryption are implemented to mitigate single-point vulnerabilities.

Mentor tip

Structure with clear sections for advantages, disadvantages, and an evaluative conclusion.

(c)(i)

4 marks

Pattern discovery in large datasets: Data mining applies automated clustering and anomaly detection algorithms to discover non-obvious correlations across historical crime reports.
Predictive analysis and crime prevention: Identifies temporal and spatial trends in criminal behavior (such as repeat burglaries linked to specific time windows and neighborhoods), allowing departments to proactively position patrols and deploy preventative resources.

Mentor tip

Link the explanation to uncovering hidden patterns and making predictions in a policing context.

(c)(ii)

4 marks

Cross-source entity matching: Data matching compares distinct databases (such as vehicle registration, mobile tower pings, and surveillance logs) to identify records corresponding to the same person or vehicle.
Practical policing example: Matching a partial license plate captured on street CCTV near several theft incidents against regional car hire records to reveal a suspect operating under an alias.

Mentor tip

Provide a clear definition followed by a concrete investigative scenario.

(d)(i)

4 marks

Vulnerability to automated brute-force and credential stuffing: Attackers can execute automated dictionary or credential-stuffing scripts against password-only systems, easily compromising weak or reused passwords.
Interception and keystroke logging: Without multi-factor authentication (MFA), stolen passwords obtained via phishing, malware keyloggers, or unencrypted transmissions grant immediate, unrestricted access.

Mentor tip

Discuss brute force/dictionary attacks and the lack of a second factor protecting against credential theft.

(d)(ii)

3 marks

Purpose limitation: Personal data must only be collected and processed for explicit, specified, and legitimate lawful purposes.
Data minimization: Systems must store only data that is adequate, relevant, and strictly necessary for the designated operational task.
Right of access and rectification: Individuals hold the statutory right to view the personal data recorded about them and demand correction of inaccurate entries.

Mentor tip

Cite distinct legal principles such as purpose limitation, data minimization, and access/rectification rights.

Option B: Modelling and simulation · Option

Questions 4–6 · 45 marks

Question 4

18 marks

Birthday paradox probability modelling, spreadsheet simulation, and algorithmic testing

Modelling and simulation

(a)

4 marks

Step 1 - Populate sample sizes: In column A (rows 1 to 39), input integer values for $N$ from $2$ to $40$ .
Step 2 - Calculate pair count: In cell B1, enter `=(ColA1*(ColA1-1))/2` to compute the number of pairs $N P$ .
Step 3 - Compute different birthday probability: In cell C1, enter `=(364/365)^B1` to find the probability of all pairs having different birthdays $D P$ .
Step 4 - Compute shared birthday probability and chart: In cell D1, enter `=1-C1` for $S B$ , fill rows down to row 39, and insert an X-Y scatter plot with Column A on the x-axis and Column D on the y-axis.

N P = \frac{N ( N - 1 )}{2} D P = (\frac{364}{365})^{N P} S B = 1 - D P

Mentor tip

Clearly specify formulas for each column and explain dragging down to generate the scatter plot.

(b)

4 marks

Column A (People): Enter index numbers 1 through 15 in cells ColA1:A15.
Column B (Random Birthdays): In cells B1:B15, generate random calendar days using `=RANDBETWEEN(1, 365)`.
Column C (Frequency Count): In cell C1, enter `=COUNTIF(B $1 : B$ 15, B1)` and fill down to C15, utilizing absolute references for the range to detect duplicates.
Output Cell (Detection): In cell C16, output the result using `=IF(MAX(C1:C15) > 1, TRUE, FALSE)` or `=IF(SUM(C1:C15) > 15, TRUE, FALSE)`.

Mentor tip

Ensure absolute cell references (B $1 : B$ 15) are used in COUNTIF to prevent sliding ranges.

(c)

8 marks

Construct the Monte Carlo simulation algorithm in structured pseudocode:
TOTAL_SIMS = 10000
N_PEOPLE = 23
sharedMatchCount = 0
loop trial from 1 to TOTAL_SIMS
DAY = new array of size N_PEOPLE
loop k from 0 to N_PEOPLE - 1
DAY[k] = RANDINT(1, 365)
end loop
hasDuplicate = false
i = 0
loop while i < N_PEOPLE - 1 and not hasDuplicate
j = i + 1
loop while j < N_PEOPLE and not hasDuplicate
if DAY[i] == DAY[j] then
hasDuplicate = true
end if
j = j + 1
end loop
i = i + 1
end loop
if hasDuplicate then
sharedMatchCount = sharedMatchCount + 1
end if
end loop
percentageResult = (sharedMatchCount / TOTAL_SIMS) * 100
output "Simulated probability of shared birthday: ", percentageResult, "%"

Percentage = \frac{sharedMatchCount}{TOTAL_SIMS} \times 100

Mentor tip

Use nested loops to pair-check elements without comparing an element to itself.

(d)

2 marks

Law of Large Numbers: Running 10,000 iterations dampens random stochastic noise, allowing the empirical frequency to converge closely to the theoretical probability (~50.7%).
Computational efficiency trade-off: Provides high statistical confidence while executing in a fraction of a second on modern processors, avoiding the delays of millions of trials.

Mentor tip

Mention convergence under the Law of Large Numbers and balancing speed with statistical accuracy.

Question 5

20 marks

Demographic simulation, model verification, stochastic forecasting, and social impacts

Modelling and simulation

(a)

2 marks

Variable 1: Crude birth rate (or fertility rate per female).
Variable 2: Crude mortality rate (or average life expectancy / net migration rate).

Mentor tip

State two measurable demographic parameters affecting population size.

(b)

5 marks

Mathematical basis: Population change follows well-defined mathematical relationships (births, deaths, aging cohorts) that can be formalized into differential or difference equations.
Safe scenario experimentation: Enables demographers to test hypothetical 'what-if' policies (e.g., changes in retirement age, healthcare investments) without waiting decades or impacting real populations.
Availability of historical baselines: Centuries of census data exist, providing rich quantitative datasets to calibrate variables and validate predictive algorithms before projecting forward.

Mentor tip

Highlight the availability of historical validation data and the risk-free testing of 'what-if' scenarios.

(c)

5 marks

Data partitioning: Split known historical demographic data into a training period (e.g., 1850 to 1980) and a testing validation period (e.g., 1981 to 2020).
Historical backtesting (hindcasting): Run the simulation parameterized on the training subset to predict population figures over the testing window.
Quantitative error evaluation: Compare simulated outputs against actual recorded historical census counts using statistical error metrics such as Mean Squared Error (MSE).
Sensitivity analysis: Systematically adjust key parameters to identify variables with disproportionate influence and refine parameter weighting.

Mentor tip

Detail historical backtesting, splitting data into training and test sets, and calculating error.

(d)

2 marks

Probabilistic / stochastic modeling: Implement Monte Carlo or Bayesian modeling that incorporates probability distributions for unexpected disruptions (such as pandemics, famines, or medical breakthroughs) rather than relying exclusively on fixed deterministic growth rates.

Mentor tip

Explain using stochastic or probabilistic distributions to capture uncertainty and sudden events.

(e)

6 marks

Consequence 1 - Healthcare and hospital infrastructure failure:
- Underestimating population growth results in severe shortages of medical staff, acute care beds, and critical supplies, leading to hospital overcrowding and preventable mortality.
- Overestimating growth leads to wasteful capital misallocation, leaving facilities underutilized while diverting resources from other essential civic programs.
Consequence 2 - Municipal utilities and public transit strain:
- Underestimating growth causes municipal clean water, electrical grids, and transit networks to exceed design capacity, producing routine power brownouts, sewage overflows, and severe traffic gridlock.
- This degrades public health, elevates commuter stress, and damages regional economic productivity.

Mentor tip

Elaborate two distinct societal consequences (healthcare and civic infrastructure), analyzing both over- and under-prediction.

Question 6

7 marks

Data visualization paradigms, 2D raster memory, and 3D rendering hardware demands

Modelling and simulation

(a)

1 mark

Visualization is the graphical representation of data, abstractions, or physical phenomena using visual elements to help users interpret complex patterns and relationships.

Mentor tip

Provide a concise definition focusing on translating raw data into graphical forms for human analysis.

(b)

2 marks

Modest framebuffer footprint: 2D static visualizations require memory primarily to store pixel color values in a 2D raster grid ( $W i d t h \times H e i g h t \times B i t s P er P i x e l$ ), which takes only a few megabytes.
Linear scaling with resolution: Memory demand depends strictly on screen resolution and color bit-depth, operating comfortably within standard system RAM on consumer devices.

Mentor tip

State that 2D raster memory scales directly with pixel resolution and bit depth, requiring minimal RAM.

(c)

4 marks

Intensive parallel processing: 3D temporal animations require graphics processing units (GPUs) with thousands of cores executing parallel matrix multiplications to compute vertex transformations, polygon rasterization, and dynamic lighting/shadowing at 30 to 60 frames per second.
Substantial dedicated VRAM: High-speed video memory (e.g., 8 GB or more of GDDR RAM) is needed to store multi-layer texture maps, 3D polygon meshes, depth (Z) buffers, and framebuffers across animation timelines.
Hardware consequence: Inadequate GPU compute capacity or insufficient VRAM causes dropped frames, visual stutter, or downgraded rendering quality.

Mentor tip

Cover GPU parallel processing (shading/lighting) and dedicated high-bandwidth VRAM for mesh/texture storage.

Option C: Web science · Option

Questions 7–9 · 45 marks

Question 7

16 marks

School portal web architecture, external CSS, user-agent parsing, and script distribution

Web science

(a)

1 mark

Domain name: `one2seven.org`

Mentor tip

State only the domain name without the protocol (https://) or path.

(b)

2 marks

Component 1: Navigation menu bar (linking Community, Calendar, Employment, Español).
Component 2: Header logo / Hero banner / Content announcements section.

Mentor tip

Identify standard structural components of the webpage shown in the figure.

(c)

2 marks

Function 1: Parsing and rendering HTML, CSS, and DOM elements into an interactive visual webpage.
Function 2: Executing client-side JavaScript scripts (and managing HTTP/HTTPS network requests/responses).

Mentor tip

List two primary browser responsibilities: rendering markup/styles and executing client-side scripts.

(d)

3 marks

Centralized formatting and maintainability: Linking an external cascading style sheet enables site-wide styling rules (typography, colors, layouts) to be maintained in a single file.
Site-wide cascading updates: Modifying a single CSS rule automatically updates the visual appearance of all linked pages across the entire portal.
Reduced network bandwidth and faster loading: Browsers cache the external CSS file after the initial download, speeding up subsequent page loads.

Mentor tip

Emphasize centralized updates across all pages and browser caching benefits.

(e)(i)

1 mark

Client-side scripting.

Mentor tip

Identify client-side execution within the browser.

(e)(ii)

3 marks

Query parameter check: Examines `window.location.search` to determine whether the URL query contains `check=false`; if present, script execution halts.
User agent string inspection: Scans `navigator.userAgent` to check if the browser identifies as a mobile device ('iPhone', 'iPad', 'Android', 'Blackberry', or 'WebOs').
Conditional client redirect: If a mobile device substring is detected, the browser executes `window.location.replace("https://one2seven.org/mobile.html")` to navigate to the mobile layout.

Mentor tip

Detail the query parameter check, user agent inspection, and mobile redirect.

(e)(iii)

1 mark

Immediate client redirection: Executing detection in the browser eliminates the latency of a round-trip HTTP request to the server, providing faster redirection and reducing web server processing load.

Mentor tip

Point out the avoidance of server round-trip latency and reduction in server workload.

(f)

3 marks

Client-side role: Provides instantaneous user interface responsiveness, interactive navigation, and form validation (such as checking email formatting) without waiting for server responses.
Server-side role: Manages secure database queries, user authentication, and student record protection behind the school's internal firewall, shielding private records from client-side inspection.
Balanced architecture: Combining both tiers delivers a smooth user experience while ensuring institutional data security and access control.

Mentor tip

Explain the division of responsibility: client-side for immediate UI interaction and server-side for secure authentication and database access.

Question 8

13 marks

Cloud streaming architecture, lossy compression, IP rights, and digital borders

Web science

(a)

2 marks

Characteristic 1: On-demand self-service provisioning of computing and storage resources without manual human intervention.
Characteristic 2: Rapid elasticity and dynamic scalability to expand or contract capacity according to user demand (or measured pay-per-use billing).

Mentor tip

State two standard cloud traits from NIST guidelines: on-demand self-service and rapid elasticity.

(b)

3 marks

Global edge caching and reduced latency: Distributed cloud networks (Content Delivery Networks / CDNs) cache video media across geographically dispersed edge servers close to end-users, delivering high-bandwidth streaming without buffering.
Elimination of single points of failure: Traditional client-server models depend on centralized server clusters that can become overwhelmed during peak viewing hours, whereas distributed cloud systems dynamically re-route traffic during outages.

Mentor tip

Contrast localized CDN edge delivery with the single-point bottlenecks of client-server systems.

(c)

2 marks

Irreversible data removal: Lossy compression permanently discards high-frequency visual data and subtle color variations deemed less noticeable to human vision to achieve high compression ratios.
Visual artifacts on high-resolution displays: Discarded details cannot be recovered upon decompression, resulting in compression artifacts, banding, or loss of clarity during fast-motion sequences.

Mentor tip

Explain that permanent data discarding prevents full reconstruction and causes visual artifacts.

(d)

3 marks

Digital piracy and unauthorized distribution: High-quality web streams can be captured using stream-ripping software and redistributed across pirate networks, violating producer copyrights.
Jurisdictional licensing conflicts: Copyright licensing agreements are negotiated on a country-by-country basis; streaming content globally creates risks of infringing regional distribution rights when users bypass geo-blocks.

Mentor tip

Discuss digital stream-ripping piracy and territorial copyright licensing discrepancies.

(e)

3 marks

Global digital accessibility: Web protocols allow users anywhere in the world to access the same media and cultural resources, transcending physical nation-state boundaries.
Circumvention of geographic barriers: Technologies like Virtual Private Networks (VPNs) and proxy networks enable users to mask geographical locations and bypass territorial content filters, creating a borderless information space.

Mentor tip

Explain how global access and VPN routing make geographic borders permeable online.

Question 9

16 marks

Search engine evaluation, PageRank mechanics, HTTPS security, SEO ethics, and monopolies

Web science

(a)

1 mark

A search engine is a web-based software application that crawls, indexes, and retrieves relevant web documents matching user search queries.

Mentor tip

Define as an automated system that crawls, indexes, and returns query-relevant web content.

(b)

2 marks

Metric 1: Query response time / latency (time required to return indexed results).
Metric 2: Click-through rate (CTR) / Bounce rate / Total number of matching indexed hits.

Mentor tip

State two measurable search engine metrics such as response latency and click-through rate.

(c)(i)

2 marks

Feature 1 - Inbound link authority: Models hyperlinks as endorsements, ranking pages higher if they receive numerous inbound links from authoritative websites.
Feature 2 - Recursive rank distribution: Weights each incoming link by the linking page's own rank, divided by its total outbound link count.

Mentor tip

Describe counting inbound links and weighting them by the authority of the linking source.

(c)(ii)

2 marks

Vulnerability of underlying assumptions: PageRank assumes that incoming links represent objective endorsements of quality, making it susceptible to commercial manipulation through link farms, paid backlinks, and reciprocal rings.

Mentor tip

Point out the flawed premise that links always reflect impartial quality rather than manipulation.

(d)

3 marks

Transport encryption: HTTPS utilizes TLS/SSL cryptographic protocols to encrypt data packets exchanged between the browser and server, shielding medical or confidential queries from packet sniffing.
Data integrity and authentication: Uses cryptographic message authentication codes (MACs) to prevent packet tampering in transit, and validates server digital certificates to protect users against spoofing.

Mentor tip

Discuss end-to-end encryption against eavesdropping, data integrity, and server authentication.

(e)

3 marks

Short-term market penetration (Black Hat): A pharmaceutical startup facing entrenched competition might use aggressive SEO (hidden keywords, doorway pages) to rapidly achieve visibility and generate early revenue.
Long-term credibility and risk mitigation (White Hat): Relying entirely on black hat methods risks severe search engine penalties or delisting; transitioning to white hat practices (authoritative content, valid schema markup) builds lasting domain trust.

Mentor tip

Balance aggressive short-term visibility against long-term search engine penalty avoidance and trust.

(f)

3 marks

Self-reinforcing feedback loops: Dominant search engines capture the vast majority of search queries, generating massive datasets to continuously refine algorithms and consolidate market dominance.
Multinational regulatory fragmentation: Operating globally across diverse legal jurisdictions allows major search platforms to bypass single-nation anti-monopoly regulations, self-preferencing their own commercial services without independent oversight.

Mentor tip

Describe self-reinforcing network effects and multinational operations that circumvent local anti-trust laws.

Option D: Object-oriented programming · Option

Questions 10–12 · 45 marks

Question 10

15 marks

Field trip allocation system, method signatures, encapsulation, and assignment logic

Object-oriented programming

(a)

1 mark

The school offers exactly 16 different trips, so an array of size 16 stores references to all available Trip objects.

Mentor tip

State that 16 trips are offered, requiring an array of size 16.

(b)

2 marks

Component 1: Method name (identifier).
Component 2: Parameter list (enclosed in parentheses, specifying parameter types and names, or empty if none).

Mentor tip

Identify method name and parameter list as mandatory parts of any method signature.

(c)

2 marks

Data hiding and bundling: Class attributes (such as `placesFilled`, `isRunning`, and `tList`) are declared `private` to prevent unauthorized direct modification from outside the class.
Controlled access via public interface: Internal state can only be read or modified through public accessor and mutator methods like `getPlacesFilled()` and `updatePlacesFilled()`.

Mentor tip

Reference specific private variables in Trip/Student and the use of public getters/setters.

(d)

2 marks

Increment and threshold check: The method increments `placesFilled` by 1 and checks whether `placesFilled >= 10` (or `> 9`).
State transition: When this threshold is met, it updates the boolean status by assigning `isRunning = true`.

Mentor tip

Explain checking if placesFilled reaches 10 and setting isRunning to true.

(e)

6 marks

Implement `assignTrip(Student s)` in `TripAllocator` using an iteration loop with a termination flag:
public void assignTrip(Student s) {
int i = 0;
boolean assigned = false;
while (!assigned && i < 10) {
int tripNumber = s.sPreferenceList[i];
// Map 1-based trip number to 0-based array index
if (allTrips[tripNumber - 1].getPlacesFilled() < 25) {
s.setTripAssigned(allTrips[tripNumber - 1].getTripTitle());
allTrips[tripNumber - 1].addStudentToTripList(s);
allTrips[tripNumber - 1].updatePlacesFilled();
assigned = true;
}
i++;
}
}

Mentor tip

Remember to map the 1-indexed trip preference number to the 0-indexed array using [tripNumber - 1].

(f)

2 marks

Handling undersubscribed trips: After all requests are processed, trips with fewer than 10 students cannot run (`isRunning == false`).
Reallocating stranded students: A reassignment method is necessary to move students enrolled in cancelled trips to their next available running preference.

Mentor tip

Explain that cancelled trips with fewer than 10 students require reallocating enrolled students.

Question 11

15 marks

Joint preference modification, vacancy reporting, and unassigned student counter

Object-oriented programming

(a)

1 mark

Add a private attribute in `Student` to store the linked companion: `private String friendId;` (or `private Student friend;`).

Mentor tip

Identify adding a private field to store the companion's ID or Student reference.

(b)

4 marks

Modification 1 - Dual vacancy validation: Before assigning a trip, check whether at least two spots remain (`allTrips[n - 1].getPlacesFilled() < 24`) rather than checking for a single opening.
Modification 2 - Paired assignment: Assign both the current student and the companion friend to the trip, adding both names to `tList` and calling `updatePlacesFilled()` twice.

Mentor tip

Explain checking for at least 2 vacancies (< 24) and updating records for both students.

(c)

5 marks

Construct `displayPlacesLeft()` in `TripAllocator`:
public void displayPlacesLeft() {
for (int i = 0; i < allTrips.length; i++) {
if (allTrips[i].isRunning() && allTrips[i].getPlacesFilled() < 25) {
int placesLeft = 25 - allTrips[i].getPlacesFilled();
System.out.println("Trip " + (i + 1) + ": " + allTrips[i].getTripTitle() + " Places left: " + placesLeft);
}
}
}

Mentor tip

Check that the trip is running AND not full (< 25), and print the exact format requested.

(d)

5 marks

Construct `noTrip()` in `TripAllocator` with proper method signature and loop:
public int noTrip() {
int unassignedCount = 0;
for (int i = 0; i < allStudents.length; i++) {
if (allStudents[i].getTripAssigned().equals("zzz")) {
unassignedCount++;
}
}
return unassignedCount;
}

Mentor tip

Include public int noTrip() signature and use .equals("zzz") to check the default trip string.

Question 12

15 marks

Inheritance hierarchies, UML class relationships, OOP extensibility, and modularity

Object-oriented programming

(a)(i)

5 marks

Construct the UML inheritance class diagram:
1. Draw superclass `Trip` at top with its attributes and methods.
2. Draw three subclass boxes below: `AdventureTrip`, `ServiceTrip`, `CultureTrip`.
3. Connect each subclass to `Trip` with a solid line and an open hollow arrowhead pointing up to `Trip`.
4. Add subclass-specific attributes and methods:
- `AdventureTrip`: `- shoesRequired: boolean`, `+ checkFitness()`
- `ServiceTrip`: `- communicationSkillLevel: int`, `+ verifySkills()`
- `CultureTrip`: `- cameraRequired: boolean`, `+ checkCamera()`

Mentor tip

Ensure open hollow triangular arrowheads point up toward superclass Trip, with specific attributes for each subclass.

(a)(ii)

1 mark

Java declaration syntax: `public class AdventureTrip extends Trip` (or `ServiceTrip extends Trip` / `CultureTrip extends Trip`).

Mentor tip

State the full class declaration using the 'extends' keyword.

(a)(iii)

4 marks

Advantage 1 - Code reusability: Subclasses automatically inherit attributes and methods from `Trip` without rewriting boilerplate getters, setters, and participant tracking code.
Advantage 2 - Extensibility and polymorphism: Subclasses can override parent methods or introduce specialized behaviors while treating all trip types uniformly through the common `Trip` interface.

Mentor tip

Detail code reusability without code duplication, and polymorphism/extensibility.

(a)(iv)

1 mark

Minimizing coupling: Lowering dependencies ensures modifications to internal class implementations do not produce cascading side-effects or break other modules.

Mentor tip

Explain that loose coupling reduces the ripple effect of changes across the codebase.

(b)

4 marks

Advantage 1 - Parallel collaborative development: Distinct functional modules with clear interfaces can be engineered concurrently by different team members, shortening delivery schedules.
Advantage 2 - Isolated unit testing and maintenance: Independent modules can be tested in isolation, making bug detection faster and allowing refactoring without impacting the entire system.

Mentor tip

Focus on parallel development by teams and isolated debugging/unit testing.

Our analysis

Where the marks are

Each part sorted by the topic it mostly tests. Databases carries the most marks on this paper.

Databases45 marks · 25%
Q1, Q2, Q3
Modelling and simulation45 marks · 25%
Q4, Q5, Q6
Web science45 marks · 25%
Q7, Q8, Q9
Object-oriented programming45 marks · 25%
Q10, Q11, Q12

Mentor insights

Traps to watch for

01
Composite primary key collision on repeat interviews · Q2
Assuming (WitnessID, CrimeID, OfficerID) is a valid composite primary key overlooks that an officer may interview the same witness multiple times for the same crime. A unique primary key requires adding a timestamp or using an independent InterviewID.
02
Omitting absolute cell references in duplicate-checking formulas · Q4
When copying =COUNTIF(B $1 : B$ 15, B1) across rows, forgetting the dollar signs ($) in the range causes the lookup boundary to slide downward, producing inaccurate frequency counts.
03
Off-by-one indexing between 1-based trip numbers and 0-based arrays · Q10
Trip preference numbers range from 1 to 16, whereas allTrips array indices span 0 to 15. Accessing allTrips[n] directly instead of allTrips[n - 1] triggers an ArrayIndexOutOfBoundsException when trip 16 is selected.

Solutions and tips are GeorgeTheTutor's own; the questions are not reproduced here. Not affiliated with or endorsed by the International Baccalaureate Organization.

Want someone to go through your paper with you?

Our maths mentors took these exams recently. One-to-one, they'll find where your marks are going and fix it before May.

Find your maths mentor More past papers

Worked solutions

IB Computer Science SL · Paper 2

Computer Science, Standard Level

Session: November 2024
Marks: 180
Duration: 1 hour
Calculator: Not allowed
Questions: 12

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12

Keep your copy of the paper open alongside. We don't reproduce the questions: each part below is labelled to match the November 2024 Global paper, which your school or the IB can provide.

Practice mode

Hide our working until you've had a go.

Option A: Databases · Option

Questions 1–3 · 45 marks

Question 1

8 marks

Police investigation relational model, referential integrity, and database views

Databases

(a)

2 marks

Analyze the entity relationships described in the scenario:
A police officer may conduct multiple interviews, but each interview is conducted by one officer (1-to-many relationship from POLICE_OFFICER to INTERVIEW).
A witness may participate in multiple interviews, but each interview involves one specific witness (1-to-many relationship from WITNESS to INTERVIEW).
Construct the Entity-Relationship Diagram (ERD):
POLICE_OFFICER (1) -----< (conducts) >----- (M) INTERVIEW (M) -----< (involves) >----- (1) WITNESS

Mentor tip

Represent INTERVIEW as the junction entity with 'many' crow's feet terminating on it from both 1-side parent entities.

(b)

3 marks

Reason 1 - Preventing orphan records: Enforcing foreign key constraints ensures that an interview record cannot reference a non-existent OfficerID or WitnessID.
Reason 2 - Consistency across relational operations: Guarantees that linked attributes across related tables remain synchronized during insertions and modifications.
Reason 3 - Cascading deletes and updates: Enables controlled cascading updates or deletions so that removing an officer or witness safely handles or restricts dependent interview logs.

Mentor tip

Focus on orphan record prevention, cross-table referential consistency, and cascading operations.

(c)

3 marks

Virtual relation definition: A view acts as a saved SQL query (`CREATE VIEW ViewName AS SELECT ... WHERE ...`) presenting a customized subset of columns and rows without storing redundant data.
Data abstraction and simplified reporting: Complex multi-table joins and filtering conditions are encapsulated into a simple virtual table for end users and routine weekly reports.
Security and access control: Restricts visibility so officers only inspect fields necessary for their duties, shielding sensitive or confidential personal data.

Mentor tip

Describe a view as a stored query that provides a dynamic virtual subset without creating separate physical tables.

Question 2

15 marks

Normalization, secondary keys, and 3NF schema design for police records

Databases

(a)

1 mark

Mentor tip

Clarify that a secondary key facilitates searching on non-primary-key columns.

(b)(i)

2 marks

Violation of uniqueness: A witness can be interviewed more than once by the same officer regarding the same crime, generating duplicate `(WitnessID, CrimeID, OfficerID)` combinations and violating primary key uniqueness.
Need for timestamp or surrogate key: To uniquely distinguish records, the key would need to incorporate `DateTimeStatement`, or preferably use an auto-incrementing surrogate `InterviewID`.

Mentor tip

Highlight that repeating an interview for the same crime and officer causes identical composite keys.

(b)(ii)

4 marks

Partial functional dependency (violates 2NF): Non-key attributes depend on only a subset of the composite primary key; for example, `CrimeLocation` depends strictly on `CrimeID` rather than the entire key `(WitnessID, CrimeID, OfficerID)`.
Transitive dependency (violates 3NF): Non-key attributes depend on other non-key attributes; for instance, `StationPhone` is functionally determined by `PoliceStation` rather than directly by the primary key.

Mentor tip

Cite specific attributes: CrimeLocation depending only on CrimeID (partial dependency) and StationPhone depending on PoliceStation (transitive dependency).

(c)

2 marks

Preservation of leading zeroes: Phone numbers frequently begin with leading zeroes (e.g., `01029238484`), which numerical data types truncate.
Inclusion of non-numeric characters: Alphanumeric types accommodate international formatting symbols such as `+`, parentheses, and hyphens, and prevent accidental arithmetic operations.

Mentor tip

Emphasize leading zero retention and non-arithmetic characters.

(d)

6 marks

Decompose the unnormalized structure into 3NF relations to eliminate repeating groups, partial dependencies, and transitive dependencies:
1. CRIME(CrimeID, CrimeType, CrimeAddress, CrimeDateTime) - Primary Key: CrimeID
2. OFFICER(OfficerID, OfficerName) - Primary Key: OfficerID
3. CRIMEOFFICER(CrimeID*, OfficerID*) - Composite Primary Key: (CrimeID, OfficerID)
4. VICTIM(VictimID, VictimName, VictimPhone) - Primary Key: VictimID
5. VICTIMCRIME(CrimeID*, VictimID*) - Composite Primary Key: (CrimeID, VictimID)
6. EVIDENCE(EvidenceID, EvidenceType, EvidenceDesc, CrimeID*) - Primary Key: EvidenceID, Foreign Key: CrimeID

Mentor tip

Ensure junction tables handle many-to-many links between Crimes and Officers, and Crimes and Victims, with an EvidenceID primary key for items.

Question 3

22 marks

Concurrency control, centralized databases, data mining, and GDPR compliance

Databases

(a)

2 marks

Exclusive write locking: When the first officer opens a witness record for editing, the DBMS places an exclusive lock on that record.
Concurrency resolution: The second officer is granted read-only access or made to wait until the first transaction commits and releases the lock, preventing race conditions and lost updates.

Mentor tip

Explain exclusive write locks and queuing/read-only restriction for concurrent users.

(b)

5 marks

Advantages of centralization:
- Eliminates data duplication and update anomalies across regional stations, ensuring all police personnel access synchronized, up-to-date records.
- Accelerates multi-jurisdictional investigations by enabling seamless cross-referencing of criminal profiles without contacting external local repositories.
Disadvantages of centralization:
- Creates a single point of failure; hardware outages or network disruptions can disable police operations nationwide.
- Presents a high-value target for unauthorized intrusion or distributed denial-of-service (DDoS) attacks, creating widespread privacy exposure.
Conclusion: Centralization drastically improves inter-agency operational efficiency, provided automated failover mirrors and stringent zero-trust encryption are implemented to mitigate single-point vulnerabilities.

Mentor tip

Structure with clear sections for advantages, disadvantages, and an evaluative conclusion.

(c)(i)

4 marks

Pattern discovery in large datasets: Data mining applies automated clustering and anomaly detection algorithms to discover non-obvious correlations across historical crime reports.
Predictive analysis and crime prevention: Identifies temporal and spatial trends in criminal behavior (such as repeat burglaries linked to specific time windows and neighborhoods), allowing departments to proactively position patrols and deploy preventative resources.

Mentor tip

Link the explanation to uncovering hidden patterns and making predictions in a policing context.

(c)(ii)

4 marks

Cross-source entity matching: Data matching compares distinct databases (such as vehicle registration, mobile tower pings, and surveillance logs) to identify records corresponding to the same person or vehicle.
Practical policing example: Matching a partial license plate captured on street CCTV near several theft incidents against regional car hire records to reveal a suspect operating under an alias.

Mentor tip

Provide a clear definition followed by a concrete investigative scenario.

(d)(i)

4 marks

Vulnerability to automated brute-force and credential stuffing: Attackers can execute automated dictionary or credential-stuffing scripts against password-only systems, easily compromising weak or reused passwords.
Interception and keystroke logging: Without multi-factor authentication (MFA), stolen passwords obtained via phishing, malware keyloggers, or unencrypted transmissions grant immediate, unrestricted access.

Mentor tip

Discuss brute force/dictionary attacks and the lack of a second factor protecting against credential theft.

(d)(ii)

3 marks

Purpose limitation: Personal data must only be collected and processed for explicit, specified, and legitimate lawful purposes.
Data minimization: Systems must store only data that is adequate, relevant, and strictly necessary for the designated operational task.
Right of access and rectification: Individuals hold the statutory right to view the personal data recorded about them and demand correction of inaccurate entries.

Mentor tip

Cite distinct legal principles such as purpose limitation, data minimization, and access/rectification rights.

Option B: Modelling and simulation · Option

Questions 4–6 · 45 marks

Question 4

18 marks

Birthday paradox probability modelling, spreadsheet simulation, and algorithmic testing

Modelling and simulation

(a)

4 marks

Step 1 - Populate sample sizes: In column A (rows 1 to 39), input integer values for $N$ from $2$ to $40$ .
Step 2 - Calculate pair count: In cell B1, enter `=(ColA1*(ColA1-1))/2` to compute the number of pairs $N P$ .
Step 3 - Compute different birthday probability: In cell C1, enter `=(364/365)^B1` to find the probability of all pairs having different birthdays $D P$ .
Step 4 - Compute shared birthday probability and chart: In cell D1, enter `=1-C1` for $S B$ , fill rows down to row 39, and insert an X-Y scatter plot with Column A on the x-axis and Column D on the y-axis.

N P = \frac{N ( N - 1 )}{2} D P = (\frac{364}{365})^{N P} S B = 1 - D P

Mentor tip

Clearly specify formulas for each column and explain dragging down to generate the scatter plot.

(b)

4 marks

Column A (People): Enter index numbers 1 through 15 in cells ColA1:A15.
Column B (Random Birthdays): In cells B1:B15, generate random calendar days using `=RANDBETWEEN(1, 365)`.
Column C (Frequency Count): In cell C1, enter `=COUNTIF(B $1 : B$ 15, B1)` and fill down to C15, utilizing absolute references for the range to detect duplicates.
Output Cell (Detection): In cell C16, output the result using `=IF(MAX(C1:C15) > 1, TRUE, FALSE)` or `=IF(SUM(C1:C15) > 15, TRUE, FALSE)`.

Mentor tip

Ensure absolute cell references (B $1 : B$ 15) are used in COUNTIF to prevent sliding ranges.

(c)

8 marks

Construct the Monte Carlo simulation algorithm in structured pseudocode:
TOTAL_SIMS = 10000
N_PEOPLE = 23
sharedMatchCount = 0
loop trial from 1 to TOTAL_SIMS
DAY = new array of size N_PEOPLE
loop k from 0 to N_PEOPLE - 1
DAY[k] = RANDINT(1, 365)
end loop
hasDuplicate = false
i = 0
loop while i < N_PEOPLE - 1 and not hasDuplicate
j = i + 1
loop while j < N_PEOPLE and not hasDuplicate
if DAY[i] == DAY[j] then
hasDuplicate = true
end if
j = j + 1
end loop
i = i + 1
end loop
if hasDuplicate then
sharedMatchCount = sharedMatchCount + 1
end if
end loop
percentageResult = (sharedMatchCount / TOTAL_SIMS) * 100
output "Simulated probability of shared birthday: ", percentageResult, "%"

Percentage = \frac{sharedMatchCount}{TOTAL_SIMS} \times 100

Mentor tip

Use nested loops to pair-check elements without comparing an element to itself.

(d)

2 marks

Law of Large Numbers: Running 10,000 iterations dampens random stochastic noise, allowing the empirical frequency to converge closely to the theoretical probability (~50.7%).
Computational efficiency trade-off: Provides high statistical confidence while executing in a fraction of a second on modern processors, avoiding the delays of millions of trials.

Mentor tip

Mention convergence under the Law of Large Numbers and balancing speed with statistical accuracy.

Question 5

20 marks

Demographic simulation, model verification, stochastic forecasting, and social impacts

Modelling and simulation

(a)

2 marks

Variable 1: Crude birth rate (or fertility rate per female).
Variable 2: Crude mortality rate (or average life expectancy / net migration rate).

Mentor tip

State two measurable demographic parameters affecting population size.

(b)

5 marks

Mathematical basis: Population change follows well-defined mathematical relationships (births, deaths, aging cohorts) that can be formalized into differential or difference equations.
Safe scenario experimentation: Enables demographers to test hypothetical 'what-if' policies (e.g., changes in retirement age, healthcare investments) without waiting decades or impacting real populations.
Availability of historical baselines: Centuries of census data exist, providing rich quantitative datasets to calibrate variables and validate predictive algorithms before projecting forward.

Mentor tip

Highlight the availability of historical validation data and the risk-free testing of 'what-if' scenarios.

(c)

5 marks

Data partitioning: Split known historical demographic data into a training period (e.g., 1850 to 1980) and a testing validation period (e.g., 1981 to 2020).
Historical backtesting (hindcasting): Run the simulation parameterized on the training subset to predict population figures over the testing window.
Quantitative error evaluation: Compare simulated outputs against actual recorded historical census counts using statistical error metrics such as Mean Squared Error (MSE).
Sensitivity analysis: Systematically adjust key parameters to identify variables with disproportionate influence and refine parameter weighting.

Mentor tip

Detail historical backtesting, splitting data into training and test sets, and calculating error.

(d)

2 marks

Mentor tip

Explain using stochastic or probabilistic distributions to capture uncertainty and sudden events.

(e)

6 marks

Consequence 1 - Healthcare and hospital infrastructure failure:
- Underestimating population growth results in severe shortages of medical staff, acute care beds, and critical supplies, leading to hospital overcrowding and preventable mortality.
- Overestimating growth leads to wasteful capital misallocation, leaving facilities underutilized while diverting resources from other essential civic programs.
Consequence 2 - Municipal utilities and public transit strain:
- Underestimating growth causes municipal clean water, electrical grids, and transit networks to exceed design capacity, producing routine power brownouts, sewage overflows, and severe traffic gridlock.
- This degrades public health, elevates commuter stress, and damages regional economic productivity.

Mentor tip

Elaborate two distinct societal consequences (healthcare and civic infrastructure), analyzing both over- and under-prediction.

Question 6

7 marks

Data visualization paradigms, 2D raster memory, and 3D rendering hardware demands

Modelling and simulation

(a)

1 mark

Visualization is the graphical representation of data, abstractions, or physical phenomena using visual elements to help users interpret complex patterns and relationships.

Mentor tip

Provide a concise definition focusing on translating raw data into graphical forms for human analysis.

(b)

2 marks

Modest framebuffer footprint: 2D static visualizations require memory primarily to store pixel color values in a 2D raster grid ( $W i d t h \times H e i g h t \times B i t s P er P i x e l$ ), which takes only a few megabytes.
Linear scaling with resolution: Memory demand depends strictly on screen resolution and color bit-depth, operating comfortably within standard system RAM on consumer devices.

Mentor tip

State that 2D raster memory scales directly with pixel resolution and bit depth, requiring minimal RAM.

(c)

4 marks

Intensive parallel processing: 3D temporal animations require graphics processing units (GPUs) with thousands of cores executing parallel matrix multiplications to compute vertex transformations, polygon rasterization, and dynamic lighting/shadowing at 30 to 60 frames per second.
Substantial dedicated VRAM: High-speed video memory (e.g., 8 GB or more of GDDR RAM) is needed to store multi-layer texture maps, 3D polygon meshes, depth (Z) buffers, and framebuffers across animation timelines.
Hardware consequence: Inadequate GPU compute capacity or insufficient VRAM causes dropped frames, visual stutter, or downgraded rendering quality.

Mentor tip

Cover GPU parallel processing (shading/lighting) and dedicated high-bandwidth VRAM for mesh/texture storage.

Option C: Web science · Option

Questions 7–9 · 45 marks

Question 7

16 marks

School portal web architecture, external CSS, user-agent parsing, and script distribution

Web science

(a)

1 mark

Domain name: `one2seven.org`

Mentor tip

State only the domain name without the protocol (https://) or path.

(b)

2 marks

Component 1: Navigation menu bar (linking Community, Calendar, Employment, Español).
Component 2: Header logo / Hero banner / Content announcements section.

Mentor tip

Identify standard structural components of the webpage shown in the figure.

(c)

2 marks

Function 1: Parsing and rendering HTML, CSS, and DOM elements into an interactive visual webpage.
Function 2: Executing client-side JavaScript scripts (and managing HTTP/HTTPS network requests/responses).

Mentor tip

List two primary browser responsibilities: rendering markup/styles and executing client-side scripts.

(d)

3 marks

Centralized formatting and maintainability: Linking an external cascading style sheet enables site-wide styling rules (typography, colors, layouts) to be maintained in a single file.
Site-wide cascading updates: Modifying a single CSS rule automatically updates the visual appearance of all linked pages across the entire portal.
Reduced network bandwidth and faster loading: Browsers cache the external CSS file after the initial download, speeding up subsequent page loads.

Mentor tip

Emphasize centralized updates across all pages and browser caching benefits.

(e)(i)

1 mark

Client-side scripting.

Mentor tip

Identify client-side execution within the browser.

(e)(ii)

3 marks

Query parameter check: Examines `window.location.search` to determine whether the URL query contains `check=false`; if present, script execution halts.
User agent string inspection: Scans `navigator.userAgent` to check if the browser identifies as a mobile device ('iPhone', 'iPad', 'Android', 'Blackberry', or 'WebOs').
Conditional client redirect: If a mobile device substring is detected, the browser executes `window.location.replace("https://one2seven.org/mobile.html")` to navigate to the mobile layout.

Mentor tip

Detail the query parameter check, user agent inspection, and mobile redirect.

(e)(iii)

1 mark

Mentor tip

Point out the avoidance of server round-trip latency and reduction in server workload.

(f)

3 marks

Client-side role: Provides instantaneous user interface responsiveness, interactive navigation, and form validation (such as checking email formatting) without waiting for server responses.
Server-side role: Manages secure database queries, user authentication, and student record protection behind the school's internal firewall, shielding private records from client-side inspection.
Balanced architecture: Combining both tiers delivers a smooth user experience while ensuring institutional data security and access control.

Mentor tip

Explain the division of responsibility: client-side for immediate UI interaction and server-side for secure authentication and database access.

Question 8

13 marks

Cloud streaming architecture, lossy compression, IP rights, and digital borders

Web science

(a)

2 marks

Characteristic 1: On-demand self-service provisioning of computing and storage resources without manual human intervention.
Characteristic 2: Rapid elasticity and dynamic scalability to expand or contract capacity according to user demand (or measured pay-per-use billing).

Mentor tip

State two standard cloud traits from NIST guidelines: on-demand self-service and rapid elasticity.

(b)

3 marks

Global edge caching and reduced latency: Distributed cloud networks (Content Delivery Networks / CDNs) cache video media across geographically dispersed edge servers close to end-users, delivering high-bandwidth streaming without buffering.
Elimination of single points of failure: Traditional client-server models depend on centralized server clusters that can become overwhelmed during peak viewing hours, whereas distributed cloud systems dynamically re-route traffic during outages.

Mentor tip

Contrast localized CDN edge delivery with the single-point bottlenecks of client-server systems.

(c)

2 marks

Irreversible data removal: Lossy compression permanently discards high-frequency visual data and subtle color variations deemed less noticeable to human vision to achieve high compression ratios.
Visual artifacts on high-resolution displays: Discarded details cannot be recovered upon decompression, resulting in compression artifacts, banding, or loss of clarity during fast-motion sequences.

Mentor tip

Explain that permanent data discarding prevents full reconstruction and causes visual artifacts.

(d)

3 marks

Digital piracy and unauthorized distribution: High-quality web streams can be captured using stream-ripping software and redistributed across pirate networks, violating producer copyrights.
Jurisdictional licensing conflicts: Copyright licensing agreements are negotiated on a country-by-country basis; streaming content globally creates risks of infringing regional distribution rights when users bypass geo-blocks.

Mentor tip

Discuss digital stream-ripping piracy and territorial copyright licensing discrepancies.

(e)

3 marks

Global digital accessibility: Web protocols allow users anywhere in the world to access the same media and cultural resources, transcending physical nation-state boundaries.
Circumvention of geographic barriers: Technologies like Virtual Private Networks (VPNs) and proxy networks enable users to mask geographical locations and bypass territorial content filters, creating a borderless information space.

Mentor tip

Explain how global access and VPN routing make geographic borders permeable online.

Question 9

16 marks

Search engine evaluation, PageRank mechanics, HTTPS security, SEO ethics, and monopolies

Web science

(a)

1 mark

A search engine is a web-based software application that crawls, indexes, and retrieves relevant web documents matching user search queries.

Mentor tip

Define as an automated system that crawls, indexes, and returns query-relevant web content.

(b)

2 marks

Metric 1: Query response time / latency (time required to return indexed results).
Metric 2: Click-through rate (CTR) / Bounce rate / Total number of matching indexed hits.

Mentor tip

State two measurable search engine metrics such as response latency and click-through rate.

(c)(i)

2 marks

Feature 1 - Inbound link authority: Models hyperlinks as endorsements, ranking pages higher if they receive numerous inbound links from authoritative websites.
Feature 2 - Recursive rank distribution: Weights each incoming link by the linking page's own rank, divided by its total outbound link count.

Mentor tip

Describe counting inbound links and weighting them by the authority of the linking source.

(c)(ii)

2 marks

Mentor tip

Point out the flawed premise that links always reflect impartial quality rather than manipulation.

(d)

3 marks

Transport encryption: HTTPS utilizes TLS/SSL cryptographic protocols to encrypt data packets exchanged between the browser and server, shielding medical or confidential queries from packet sniffing.
Data integrity and authentication: Uses cryptographic message authentication codes (MACs) to prevent packet tampering in transit, and validates server digital certificates to protect users against spoofing.

Mentor tip

Discuss end-to-end encryption against eavesdropping, data integrity, and server authentication.

(e)

3 marks

Short-term market penetration (Black Hat): A pharmaceutical startup facing entrenched competition might use aggressive SEO (hidden keywords, doorway pages) to rapidly achieve visibility and generate early revenue.
Long-term credibility and risk mitigation (White Hat): Relying entirely on black hat methods risks severe search engine penalties or delisting; transitioning to white hat practices (authoritative content, valid schema markup) builds lasting domain trust.

Mentor tip

Balance aggressive short-term visibility against long-term search engine penalty avoidance and trust.

(f)

3 marks

Self-reinforcing feedback loops: Dominant search engines capture the vast majority of search queries, generating massive datasets to continuously refine algorithms and consolidate market dominance.
Multinational regulatory fragmentation: Operating globally across diverse legal jurisdictions allows major search platforms to bypass single-nation anti-monopoly regulations, self-preferencing their own commercial services without independent oversight.

Mentor tip

Describe self-reinforcing network effects and multinational operations that circumvent local anti-trust laws.

Option D: Object-oriented programming · Option

Questions 10–12 · 45 marks

Question 10

15 marks

Field trip allocation system, method signatures, encapsulation, and assignment logic

Object-oriented programming

(a)

1 mark

The school offers exactly 16 different trips, so an array of size 16 stores references to all available Trip objects.

Mentor tip

State that 16 trips are offered, requiring an array of size 16.

(b)

2 marks

Component 1: Method name (identifier).
Component 2: Parameter list (enclosed in parentheses, specifying parameter types and names, or empty if none).

Mentor tip

Identify method name and parameter list as mandatory parts of any method signature.

(c)

2 marks

Data hiding and bundling: Class attributes (such as `placesFilled`, `isRunning`, and `tList`) are declared `private` to prevent unauthorized direct modification from outside the class.
Controlled access via public interface: Internal state can only be read or modified through public accessor and mutator methods like `getPlacesFilled()` and `updatePlacesFilled()`.

Mentor tip

Reference specific private variables in Trip/Student and the use of public getters/setters.

(d)

2 marks

Increment and threshold check: The method increments `placesFilled` by 1 and checks whether `placesFilled >= 10` (or `> 9`).
State transition: When this threshold is met, it updates the boolean status by assigning `isRunning = true`.

Mentor tip

Explain checking if placesFilled reaches 10 and setting isRunning to true.

(e)

6 marks

Implement `assignTrip(Student s)` in `TripAllocator` using an iteration loop with a termination flag:
public void assignTrip(Student s) {
int i = 0;
boolean assigned = false;
while (!assigned && i < 10) {
int tripNumber = s.sPreferenceList[i];
// Map 1-based trip number to 0-based array index
if (allTrips[tripNumber - 1].getPlacesFilled() < 25) {
s.setTripAssigned(allTrips[tripNumber - 1].getTripTitle());
allTrips[tripNumber - 1].addStudentToTripList(s);
allTrips[tripNumber - 1].updatePlacesFilled();
assigned = true;
}
i++;
}
}

Mentor tip

Remember to map the 1-indexed trip preference number to the 0-indexed array using [tripNumber - 1].

(f)

2 marks

Handling undersubscribed trips: After all requests are processed, trips with fewer than 10 students cannot run (`isRunning == false`).
Reallocating stranded students: A reassignment method is necessary to move students enrolled in cancelled trips to their next available running preference.

Mentor tip

Explain that cancelled trips with fewer than 10 students require reallocating enrolled students.

Question 11

15 marks

Joint preference modification, vacancy reporting, and unassigned student counter

Object-oriented programming

(a)

1 mark

Add a private attribute in `Student` to store the linked companion: `private String friendId;` (or `private Student friend;`).

Mentor tip

Identify adding a private field to store the companion's ID or Student reference.

(b)

4 marks

Modification 1 - Dual vacancy validation: Before assigning a trip, check whether at least two spots remain (`allTrips[n - 1].getPlacesFilled() < 24`) rather than checking for a single opening.
Modification 2 - Paired assignment: Assign both the current student and the companion friend to the trip, adding both names to `tList` and calling `updatePlacesFilled()` twice.

Mentor tip

Explain checking for at least 2 vacancies (< 24) and updating records for both students.

(c)

5 marks

Construct `displayPlacesLeft()` in `TripAllocator`:
public void displayPlacesLeft() {
for (int i = 0; i < allTrips.length; i++) {
if (allTrips[i].isRunning() && allTrips[i].getPlacesFilled() < 25) {
int placesLeft = 25 - allTrips[i].getPlacesFilled();
System.out.println("Trip " + (i + 1) + ": " + allTrips[i].getTripTitle() + " Places left: " + placesLeft);
}
}
}

Mentor tip

Check that the trip is running AND not full (< 25), and print the exact format requested.

(d)

5 marks

Construct `noTrip()` in `TripAllocator` with proper method signature and loop:
public int noTrip() {
int unassignedCount = 0;
for (int i = 0; i < allStudents.length; i++) {
if (allStudents[i].getTripAssigned().equals("zzz")) {
unassignedCount++;
}
}
return unassignedCount;
}

Mentor tip

Include public int noTrip() signature and use .equals("zzz") to check the default trip string.

Question 12

15 marks

Inheritance hierarchies, UML class relationships, OOP extensibility, and modularity

Object-oriented programming

(a)(i)

5 marks

Construct the UML inheritance class diagram:
1. Draw superclass `Trip` at top with its attributes and methods.
2. Draw three subclass boxes below: `AdventureTrip`, `ServiceTrip`, `CultureTrip`.
3. Connect each subclass to `Trip` with a solid line and an open hollow arrowhead pointing up to `Trip`.
4. Add subclass-specific attributes and methods:
- `AdventureTrip`: `- shoesRequired: boolean`, `+ checkFitness()`
- `ServiceTrip`: `- communicationSkillLevel: int`, `+ verifySkills()`
- `CultureTrip`: `- cameraRequired: boolean`, `+ checkCamera()`

Mentor tip

Ensure open hollow triangular arrowheads point up toward superclass Trip, with specific attributes for each subclass.

(a)(ii)

1 mark

Java declaration syntax: `public class AdventureTrip extends Trip` (or `ServiceTrip extends Trip` / `CultureTrip extends Trip`).

Mentor tip

State the full class declaration using the 'extends' keyword.

(a)(iii)

4 marks

Advantage 1 - Code reusability: Subclasses automatically inherit attributes and methods from `Trip` without rewriting boilerplate getters, setters, and participant tracking code.
Advantage 2 - Extensibility and polymorphism: Subclasses can override parent methods or introduce specialized behaviors while treating all trip types uniformly through the common `Trip` interface.

Mentor tip

Detail code reusability without code duplication, and polymorphism/extensibility.

(a)(iv)

1 mark

Minimizing coupling: Lowering dependencies ensures modifications to internal class implementations do not produce cascading side-effects or break other modules.

Mentor tip

Explain that loose coupling reduces the ripple effect of changes across the codebase.

(b)

4 marks

Advantage 1 - Parallel collaborative development: Distinct functional modules with clear interfaces can be engineered concurrently by different team members, shortening delivery schedules.
Advantage 2 - Isolated unit testing and maintenance: Independent modules can be tested in isolation, making bug detection faster and allowing refactoring without impacting the entire system.

Mentor tip

Focus on parallel development by teams and isolated debugging/unit testing.

Our analysis

Where the marks are

Each part sorted by the topic it mostly tests. Databases carries the most marks on this paper.

Databases45 marks · 25%
Q1, Q2, Q3
Modelling and simulation45 marks · 25%
Q4, Q5, Q6
Web science45 marks · 25%
Q7, Q8, Q9
Object-oriented programming45 marks · 25%
Q10, Q11, Q12

Mentor insights

Traps to watch for

01
Composite primary key collision on repeat interviews · Q2
Assuming (WitnessID, CrimeID, OfficerID) is a valid composite primary key overlooks that an officer may interview the same witness multiple times for the same crime. A unique primary key requires adding a timestamp or using an independent InterviewID.
02
Omitting absolute cell references in duplicate-checking formulas · Q4
When copying =COUNTIF(B $1 : B$ 15, B1) across rows, forgetting the dollar signs ($) in the range causes the lookup boundary to slide downward, producing inaccurate frequency counts.
03
Off-by-one indexing between 1-based trip numbers and 0-based arrays · Q10
Trip preference numbers range from 1 to 16, whereas allTrips array indices span 0 to 15. Accessing allTrips[n] directly instead of allTrips[n - 1] triggers an ArrayIndexOutOfBoundsException when trip 16 is selected.

Solutions and tips are GeorgeTheTutor's own; the questions are not reproduced here. Not affiliated with or endorsed by the International Baccalaureate Organization.

Want someone to go through your paper with you?

Our maths mentors took these exams recently. One-to-one, they'll find where your marks are going and fix it before May.

Find your maths mentor More past papers