Practice A4 Machine learning with authentic IB Computer Science (First Exam 2027) exam questions for both SL and HL students. This question bank mirrors Paper 1, 2, 3 structure, covering key topics like programming concepts, algorithms, and data structures. Get instant solutions, detailed explanations, and build exam confidence with questions in the style of IB examiners.
An educational technology platform uses AI to personalize learning experiences for students while addressing concerns about data privacy and algorithmic bias.
Discuss the ethical considerations of using AI to track student performance and predict academic outcomes.
A facial recognition system is deployed for security purposes in public spaces, raising concerns about privacy, surveillance, and civil liberties.
Examine the ethical challenges of facial recognition technology in public surveillance, considering privacy rights, consent, and potential for misuse.
Analyse how demographic bias in facial recognition systems disproportionately affects certain groups and discuss solutions to address these inequities.
A predictive policing system uses machine learning to forecast crime hotspots and allocate police resources, but critics argue it reinforces racial profiling and over-policing of minority communities.
Analyse the potential benefits and harms of predictive policing systems:
| Aspect | Potential Benefits | Potential Harms | Affected Communities | Mitigation Measures |
|---|---|---|---|---|
| Crime Prevention | - | - | - | - |
| Resource Allocation | Efficient deployment | - | General public | - |
| Community Relations | - | - | - | Community engagement |
| Justice System | - | Reinforced bias | - | - |
Evaluate strategies for developing more equitable predictive policing systems that balance public safety with civil rights.
A smart city traffic management system uses machine learning to optimize traffic flow, reduce emissions, and improve public safety across a metropolitan area.
Analyse how different machine learning approaches handle the complexity of urban traffic prediction with multiple variables and real-time constraints.
A smart city traffic management system uses machine learning to optimize traffic flow, reduce emissions, and improve public safety across a metropolitan area.
Explain how data from multiple sources (traffic cameras, GPS, weather, events) must be integrated and pre-processed for effective traffic management.
Discuss the privacy and surveillance implications of comprehensive traffic monitoring systems in urban environments.
A financial institution develops machine learning models for credit scoring and fraud detection to assess loan applications and monitor transactions.
Evaluate the importance of model interpretability and explainability in financial machine learning applications, considering regulatory requirements and customer trust.
Analyse how feature selection and feature engineering impact the performance and reliability of financial ML models.
A content moderation system for a global social media platform uses AI to detect harmful content while balancing free speech concerns and cultural differences.
Compare the effectiveness of different machine learning approaches for detecting hate speech, misinformation, and violent content across multiple languages.
A medical diagnosis system uses machine learning to analyse patient symptoms and medical images for disease detection.
Explain the difference between training data, validation data, and test data in the context of developing a medical AI system. Discuss why each dataset is crucial for reliable model development.
Describe the concepts of overfitting and underfitting in machine learning models, providing specific examples of how each might occur in medical diagnosis applications.
A financial fraud detection system processes millions of transactions daily using machine learning to identify suspicious activities while minimizing false positives that inconvenience customers.
Explain how ensemble methods and anomaly detection algorithms can be combined to improve fraud detection accuracy.
Analyse the ethical considerations of automated fraud detection including customer privacy, algorithmic bias, and the presumption of innocence.
A technology company is developing a recommendation system for their streaming platform to suggest movies to users based on viewing history.
Complete the following table comparing different types of machine learning approaches:
| ML Type | Learning Method | Data Requirements | Example Algorithms | Use Case in Streaming | Human Supervision |
|---|---|---|---|---|---|
| Supervised | Labeled data | Labeled data | Content classification | Content classification | Yes |
| Unsupervised | Pattern discovery | Unlabeled data | K-means, PCA | None | No |
| Reinforcement | Trial and error | Feedback/rewards | Q-learning | None | Indirect (via rewards) |
| Semi-supervised | Mix of labeled/unlabeled | Some labeled data | - | User preference learning | Partial |
Analyse why machine learning is more suitable than traditional rule-based programming for movie recommendation systems.