Practice IB Computer Science (First Exam 2027) Topic A4.1 Machine Learning Fundamentals with authentic exam-style questions for both SL and HL students. This question bank focuses on the exact syllabus content for A4.1 Machine Learning Fundamentals and mirrors Paper 1, 2 style where relevant.
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State what the bias-variance trade-off in machine learning refers to.
A cybersecurity company uses machine learning for threat detection, anomaly detection, and automated incident response across enterprise networks.
Which statement best describes a limitation of supervised learning for cybersecurity threat detection?
A ride-sharing platform uses machine learning for dynamic pricing, driver matching, and route optimization while facing scrutiny over algorithmic fairness and labor practices.
Analyse how different machine learning models could be used to support decisions that aim to reduce passenger wait times, increase driver income, and improve route choices.
An e-commerce platform implements machine learning for dynamic pricing, inventory management, and customer segmentation across millions of products and users.
Compare the computational and storage requirements of deep learning, linear models, and tree-based models for large-scale e-commerce applications.
Explain how online learning differs from batch learning in the context of e-commerce applications that need to adapt quickly to market changes.
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 by filling eight empty cells only (one mark per correct entry): [8]
ML Type Learning Method Data Requirements Example Algorithms Use Case in Streaming Human Supervision Supervised
Labelled data
Content classification
Unsupervised Pattern discovery
K-means, PCA
None Reinforcement
Q-learning
Semi-supervised
User preference learning
(b) Analyse why machine learning is more suitable than traditional rule-based programming for movie recommendation systems. [4]
Complete the following table comparing different types of machine learning approaches by filling eight empty cells only (one mark per correct entry).
Analyse why machine learning is more suitable than traditional rule-based programming for movie recommendation systems.