Practice A3.4 Alternative databases and data warehouses (HL only) 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 initiative integrates multiple systems including traffic management, utility monitoring, and citizen services.
Analyze how a data lake architecture could integrate real-time sensor streams with historical city planning data for predictive analytics.
A global e-commerce company implements a data warehousing solution for business intelligence and analytics.
Design a dimensional modeling approach for analyzing sales data:
| Component | Type | Purpose | Example Attributes | Design Considerations |
|---|---|---|---|---|
| Fact Table | Measure storage | Grain definition | ||
| Product Dimension | Dimension | ProductName, Category | ||
| Time Dimension | Temporal analysis | Hierarchy levels | ||
| Customer Dimension | Slowly changing dimensions | |||
| Geography Dimension | Dimension |
Analyze the differences between OLTP and OLAP systems in terms of design goals, query patterns, and performance characteristics.
A global logistics company implements a multi-model database to handle diverse data types and query requirements.
Analyze the benefits and challenges of multi-model databases that support document, graph, and key-value data models within a single system:
| Aspect | Benefits | Challenges | Implementation Considerations | Use Case Examples |
|---|---|---|---|---|
| Data Modeling | ||||
| Query Languages | Unified interface | Cross-model queries | ||
| Performance | Operational Complexity | |||
| Multiple expertise required |
Evaluate when organizations should choose multi-model databases versus specialized single-model databases.
A financial services company implements real-time fraud detection using streaming data processing.
Analyze the architecture requirements for real-time data processing including stream processing engines and event-driven systems:
Compare batch processing versus stream processing approaches for fraud detection scenarios, considering latency, throughput, and accuracy trade-offs.
An e-commerce platform experiences rapid growth requiring database scalability and performance optimization.
Evaluate the use of distributed caching systems like Redis to reduce database load for frequently accessed product information.
A social media analytics company processes large volumes of unstructured data including posts, images, and user interactions.
Compare different NoSQL database types for handling social media data:
| NoSQL Type | Data Model | Scalability | Query Flexibility | Consistency Model | Social Media Use Case |
|---|---|---|---|---|---|
| Document | Horizontal | User profiles | |||
| Key-Value | Simple pairs | Limited | |||
| Column-Family | Eventual | Time-series data | |||
| Graph | High |
Explain how MapReduce programming model enables distributed processing of large social media datasets.
A cloud-native application uses microservices architecture with distributed data management across multiple database types.
Analyze the challenges of implementing distributed transactions across microservices using different database technologies, including the CAP theorem implications.
Explain how the Saga pattern provides an alternative to traditional ACID transactions in distributed systems.
A healthcare research organization builds a data lake for storing and analyzing diverse medical datasets.
Explain the concept of a data lake and how it differs from traditional data warehouses in terms of data storage, schema management, and processing approaches.
Discuss the challenges of data governance and quality management in data lake environments.
A distributed ledger system for supply chain tracking requires consensus mechanisms and data immutability.
Compare different consensus algorithms used in distributed databases including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance (PBFT).
Explain how blockchain technology ensures data immutability and auditability in supply chain applications.
A research institution builds a semantic database for managing scientific publications and research relationships.
Explain how RDF (Resource Description Framework) and SPARQL enable semantic data representation and querying for research data.
Discuss the advantages of semantic databases for knowledge discovery and data integration in research environments.