Practice A4.1 Machine learning fundamentals 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.
What does the bias-variance trade-off in machine learning refer to?
A cybersecurity company uses machine learning for threat detection, anomaly detection, and automated incident response across enterprise networks.
Evaluate different machine learning approaches for cybersecurity applications, considering the adversarial nature of the problem where attackers actively try to evade detection.
Explain how concept drift affects cybersecurity ML models and describe strategies to maintain model effectiveness over time.
A ride-sharing platform uses machine learning for dynamic pricing, driver matching, and route optimization while facing scrutiny over algorithmic fairness and labor practices.
[A4.1] Analyse how different machine learning models handle the multi-objective optimization of minimizing wait times, maximizing driver income, and optimizing routes.
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 different machine learning approaches 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 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 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 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 in the context of a streaming platform recommendation system.
| ML Type | Learning Method | Data Requirements | Example Algorithms | Use Case in Streaming | Human Supervision |
|---|---|---|---|---|---|
| Supervised | (i) | Labeled data | (ii) | Content classification | Yes |
| Unsupervised | Pattern discovery | (iii) | K-means, PCA | (iv) | No |
| Reinforcement | (v) | Feedback / rewards | Q-learning | (vi) | Indirect |
| Semi-supervised | (vii) | Some labeled data | (viii) | User preference learning | Partial |
Analyse why machine learning is more suitable than traditional rule-based programming for movie recommendation systems.
A smart agriculture system uses machine learning to predict crop yields, detect plant diseases, and optimize irrigation schedules based on sensor data and weather patterns.
Explain how machine learning models can integrate multiple data sources (sensors, weather, satellite imagery) to make agricultural predictions.
Describe the role of domain expertise in agriculture when developing and validating machine learning models for farming applications.
Analyse the potential consequences of false positives and false negatives in agricultural ML systems for disease detection and yield prediction.
Practice A4.1 Machine learning fundamentals 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.
What does the bias-variance trade-off in machine learning refer to?
A cybersecurity company uses machine learning for threat detection, anomaly detection, and automated incident response across enterprise networks.
Evaluate different machine learning approaches for cybersecurity applications, considering the adversarial nature of the problem where attackers actively try to evade detection.
Explain how concept drift affects cybersecurity ML models and describe strategies to maintain model effectiveness over time.
A ride-sharing platform uses machine learning for dynamic pricing, driver matching, and route optimization while facing scrutiny over algorithmic fairness and labor practices.
[A4.1] Analyse how different machine learning models handle the multi-objective optimization of minimizing wait times, maximizing driver income, and optimizing routes.
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 different machine learning approaches 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 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 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 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 in the context of a streaming platform recommendation system.
| ML Type | Learning Method | Data Requirements | Example Algorithms | Use Case in Streaming | Human Supervision |
|---|---|---|---|---|---|
| Supervised | (i) | Labeled data | (ii) | Content classification | Yes |
| Unsupervised | Pattern discovery | (iii) | K-means, PCA | (iv) | No |
| Reinforcement | (v) | Feedback / rewards | Q-learning | (vi) | Indirect |
| Semi-supervised | (vii) | Some labeled data | (viii) | User preference learning | Partial |
Analyse why machine learning is more suitable than traditional rule-based programming for movie recommendation systems.
A smart agriculture system uses machine learning to predict crop yields, detect plant diseases, and optimize irrigation schedules based on sensor data and weather patterns.
Explain how machine learning models can integrate multiple data sources (sensors, weather, satellite imagery) to make agricultural predictions.
Describe the role of domain expertise in agriculture when developing and validating machine learning models for farming applications.
Analyse the potential consequences of false positives and false negatives in agricultural ML systems for disease detection and yield prediction.