Parallel Database Systems Questions and Answers
Parallel Database Systems are a vital part of modern data management where data processing tasks are distributed across multiple processors for efficiency. These systems enhance performance, scalability, and fault tolerance in handling large datasets. In technical interviews and database-related exams, programming questions and answers on parallel query execution, data partitioning, and optimization are common. Understanding how parallelism improves query response time helps in designing faster, high-performance systems. This topic is frequently covered in Accenture, IBM, and Cognizant placement exams. Strengthen your preparation by practicing database programming questions with detailed explanations and practical examples.
Parallel Database Systems
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25 questions
11. The term "speedup" in parallel systems means:
- Lower accuracy
- Faster execution
- More failures
- Higher costs
12. What does “scaleup” measure?
- Quality of queries
- Ability to handle larger loads
- Speed of memory
- Data duplication
13. Which is a common method of data partitioning?
- Round-robin
- Data hiding
- Data cloning
- Data masking
14. Parallel join algorithms are used to:
- Delete database
- Speed up join operations
- Encrypt tables
- Merge schemas
15. Pipeline parallelism works by:
- Running tasks one after another
- Passing intermediate results directly
- Restarting queries repeatedly
- Storing results permanently
16. Which failure affects multiple nodes simultaneously?
- Node failure
- System failure
- Disk failure
- User failure
17. Which parallel DB architecture reduces data contention the most?
- Shared-memory
- Shared-disk
- Shared-nothing
- Centralized
18. What is the first step in query parallelization?
- Query rewriting
- Query deletion
- Query encryption
- Query backup
19. Replication in a parallel DB helps improve:
- Processing speed
- Data availability
- Program execution
- Table deletion
20. Which type of parallelism splits data into smaller pieces and processes them independently?
- Task
- Pipeline
- Data
- Memory