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.
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:
| 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.
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.
A language translation service uses machine learning to provide real-time translation across 100+ languages while addressing challenges of linguistic diversity and cultural nuance.
Compare different machine learning approaches for language translation including statistical, neural, and hybrid methods.
A natural language processing application translates text between languages and provides sentiment analysis for customer feedback across global markets.
Analyse the challenges of developing ML models that handle multiple languages with different writing systems, grammar rules, and cultural contexts. Discuss the specific issues, impact on model performance, and mitigation strategies for each of the following challenge categories: (i) Data Representation challenges (ii) Training Data challenges (iii) Cultural Context challenges (iv) Technical Implementation challenges
Explain how transfer learning can help develop NLP models for languages with limited training data.
A multinational technology company develops AI systems for various applications including healthcare, finance, and social media across different countries with varying regulations and cultural norms.
Compare the machine learning requirements for healthcare diagnosis versus social media content recommendation, considering accuracy needs, data types, and performance metrics.
A social media platform uses machine learning for content moderation, detecting harmful content, spam, and misinformation across multiple languages and media types.
Design a comprehensive evaluation framework for content moderation ML systems:
| Evaluation Metric | Definition | Importance for Moderation | Calculation Method | Acceptable Threshold |
|---|---|---|---|---|
| Precision | Minimize false positives | Important to avoid over-flagging | - | - |
| Recall | True positives / (TP + FN) | Important to catch most violations | True positives / (TP + FN) | High (>90%) |
| F1-Score | Balanced metric | Balances precision and recall | 2 * (Precision * Recall) / (Precision + Recall) | - |
| Accuracy | (TP + TN) / Total | Less reliable with class imbalance | (TP + TN) / (TP + FP + TN + FN) | - |
Analyse the challenges of developing ML models that work effectively across different languages, cultures, and content types (text, images, videos).