Practice B1 Computational thinking 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 content moderation system for social media uses computational methods to identify and remove harmful content.
Explain how pattern recognition helps identify different types of harmful content including hate speech, misinformation, and spam.
Describe how abstraction manages the complexity of analysing text, images, and video content across multiple languages.
Analyse the challenges of balancing automated content moderation with freedom of expression concerns.
A music streaming service uses computational methods to recommend songs to users based on their listening history.
Describe how pattern recognition analyses user listening behaviour to understand musical preferences.
Explain how abstraction creates user and music profiles while protecting privacy.
Evaluate how algorithmic recommendations might limit musical diversity and discovery.
An online education platform uses computational methods to detect academic dishonesty and plagiarism.
Describe how pattern recognition identifies potential plagiarism in student submissions.
Explain how abstraction helps compare text similarity while protecting student privacy.
Evaluate the ethical considerations of using computational methods to monitor student academic integrity.
An environmental monitoring system uses computational approaches to track pollution levels and predict environmental changes.
Describe how decomposition breaks down environmental monitoring into manageable measurement and analysis tasks.
Explain how pattern recognition identifies environmental trends and pollution sources.
Evaluate how computational models help predict and prevent environmental problems.
A healthcare system uses computational methods to diagnose diseases from medical imaging data.
Describe how pattern recognition enables computers to identify disease indicators in medical images.
Explain how abstraction helps focus on relevant features while processing medical images.
Evaluate the role of human expertise in computational medical diagnosis systems.
A supply chain management system uses computational approaches to optimize inventory and logistics across global operations.
Describe how decomposition breaks down the complex global supply chain into manageable components.
Explain how pattern recognition optimizes inventory levels and predicts supply disruptions.
Evaluate how algorithmic supply chain management responds to unexpected disruptions like natural disasters or political events.
A disaster response system uses computational methods to coordinate emergency services and resource allocation during natural disasters.
Explain how decomposition helps organize the complex disaster response problem into manageable components.
Describe how pattern recognition and real-time data analysis improve emergency response effectiveness.
Analyse how algorithmic decision-making supports human emergency coordinators in crisis situations.
A cybersecurity company develops automated threat detection systems to protect computer networks.
Describe how pattern recognition helps identify potential security threats in network traffic.
Explain how abstraction simplifies complex network data for threat analysis.
Evaluate the importance of real-time algorithmic responses in cybersecurity systems.
A transportation company optimizes public transit schedules using computational methods to improve service efficiency.
Explain how computational thinking helps analyse passenger flow patterns and optimize transit routes.
Describe how real-time data and algorithms enable dynamic schedule adjustments.
Analyse the challenges of balancing computational optimization with practical operational constraints.
A fitness tracking application uses computational methods to monitor health metrics and provide personalized fitness recommendations.
Explain how computational thinking approaches help analyse personal health data for fitness insights.
Describe how pattern recognition identifies health trends and fitness progress patterns.
Analyse the privacy and ethical implications of computational health monitoring systems.