Splendid Distributed Databases Test - 79+ MCQs: Distributed Databases test: Assess Your Knowledge with Challenging Questions and Answers

1. Explain the role of a distributed query processor in distributed databases.

A distributed query processor is responsible for processing queries in a centralized database.

A distributed query processor is irrelevant in distributed databases.

A distributed query processor ensures that queries are processed on a single node for consistency.

A distributed query processor coordinates and optimizes queries across multiple nodes in a distributed database.

2. What is a distributed transaction?

A transaction that occurs only on weekends.

A transaction involving multiple databases that must be treated as a single, indivisible unit.

A transaction that is limited to a single server.

A transaction that is never committed to the database.

3. What is sharding in the context of distributed databases?

Data Encryption

Data Partitioning

Data Compression

Data Indexing

4. How does the 'Vector Clock' algorithm help maintain causality in distributed databases?

Concurrency Control

Query Optimization

Data Encryption

Logical Clock Synchronization

5. How does eventual consistency differ from strong consistency in distributed databases?

Timing of Updates

Data Redundancy

Data Encryption

Data Compression

6. Why is data partitioning important in distributed databases?

Ensuring Data Redundancy

Optimizing Query Performance

Achieving Atomicity

Facilitating Data Distribution

7. What is the role of consensus algorithms in distributed databases?

Consensus algorithms have no impact on distributed databases.

Consensus algorithms are only relevant in centralized databases.

Consensus algorithms ensure that all nodes in a distributed database agree on a specific value or decision.

Consensus algorithms only apply to vertical partitioning.

8. Elaborate on the principles and challenges of achieving geo-replication in distributed databases, and analyze its impact on system performance and data consistency.

Data Replication

Query Optimization

Load Balancing

Consistency Model

9. Explain the concept of 'anti-entropy' in distributed databases and its role in maintaining consistency and reliability in a decentralized environment.

Data Replication

Query Optimization

Load Balancing

Consistency Model

10. In distributed databases, what does ACID stand for?

Atomicity, Consistency, Isolation, Durability

Availability, Concurrency, Integrity, Durability

Atomic, Causal, Isolated, Durable

Agility, Compatibility, Isolation, Decoupling

11. Explain the principles and challenges of achieving quorum-based consistency in distributed databases, and analyze its impact on system reliability and performance.

Consistency Model

Query Optimization

Load Balancing

Handling Network Failures

12. Examine the principles and challenges of achieving elasticity and scalability in distributed databases, and propose strategies for dynamically adapting to changing workloads and data volumes.

Data Replication

Query Optimization

Load Balancing

Consistency Model

13. Discuss the challenges and solutions related to achieving atomicity in distributed transactions across multiple nodes, considering scenarios of partial failures.

Concurrency Control

Query Optimization

Data Encryption

Logical Clock Synchronization

14. Why is it essential for distributed databases to support ACID properties?

Ensuring Data Redundancy

Optimizing Query Performance

Facilitating Data Distribution

Maintaining Data Consistency

15. What is the purpose of replication in distributed databases?

To slow down database performance.

To create backups for data security.

To increase data redundancy.

To ensure consistency and availability by having multiple copies of the same data.

16. How does horizontal partitioning contribute to scalability in distributed databases?

Increasing Data Redundancy

Improving Data Isolation

Enhancing Query Performance

Facilitating Data Distribution

17. How does the 'Quorum-based Replication' approach enhance the reliability and performance of distributed databases compared to traditional replication methods?

Data Replication

Query Optimization

Load Balancing

Ensuring Consistency

18. Explain the concept of distributed deadlock detection and resolution in the context of distributed databases.

Optimizing Query Performance

Data Replication

Deadlock Avoidance

Handling Network Failures

19. What is CAP theorem in the context of distributed databases?

Concurrency, Availability, Partition Tolerance

Consistency, Availability, Performance

Causality, Atomicity, Persistence

Compliance, Accessibility, Portability

20. What is the purpose of a distributed cache in a distributed database system?

Distributed cache is used for decorative purposes in distributed databases.

Distributed cache is irrelevant in distributed databases.

Distributed cache speeds up query performance by storing frequently accessed data in memory.

Distributed cache only applies to centralized databases.

21. What is the purpose of a distributed log in a distributed database system?

A distributed log is used for decorative purposes in distributed databases.

A distributed log is irrelevant in distributed databases.

A distributed log speeds up query performance by recording all database activities.

A distributed log only applies to centralized databases.

22. What challenges does the 'CAP theorem' pose for distributed databases?

Ensuring Availability

Ensuring Consistency

Ensuring Partition Tolerance

Ensuring Performance

23. What is the role of a distributed coordinator in a distributed database system?

To manage a single database.

To coordinate transactions and ensure consistency across multiple databases.

To create backups for data security.

To limit access to the database.

24. Explain the CAP theorem in the context of distributed databases.

CAP theorem describes the three essential properties of a distributed database: Consistency, Availability, and Partition Tolerance.

CAP theorem is irrelevant to distributed databases.

CAP theorem only applies to centralized databases.

CAP theorem stands for Centralized, Available, and Partitioned databases.

25. Why are distributed databases used?

To make databases more difficult to manage.

To reduce data redundancy and improve fault tolerance.

To limit access to the database.

To make databases slower.

26. Explore the impact of network partitions on the availability and consistency of distributed databases, and propose strategies to mitigate these effects.

Consistency Model

Data Encryption

Query Optimization

Load Balancing

27. What is the significance of a distributed hash table (DHT) in distributed databases?

Load Balancing

Data Encryption

Query Optimization

Data Indexing

28. Explore the challenges and benefits of implementing blockchain technology in distributed databases, and discuss its potential impact on data integrity and decentralization.

Consistency Model

Query Optimization

Load Balancing

Handling Network Failures

29. Explore the challenges and benefits of implementing hybrid database architectures that combine both centralized and distributed components, and discuss scenarios where this approach is advantageous.

Consistency Model

Query Optimization

Load Balancing

Handling Network Failures

30. What is the significance of replication factors in distributed databases?

Replication factors have no impact on distributed databases.

Replication factors refer to the number of nodes in a centralized database.

Replication factors determine the number of copies of data stored across multiple nodes in a distributed database.

Replication factors only apply to horizontal partitioning.

31. How does data replication contribute to fault tolerance in distributed databases?

Ensuring Data Redundancy

Optimizing Query Performance

Load Balancing

Handling Network Failures

32. Which consistency model ensures that all nodes in a distributed database have the same data at the same time?

Eventual Consistency

Causal Consistency

Strong Consistency

Sequential Consistency

33. Define the term 'isolation' in the context of distributed databases.

Isolation is the process of isolating a distributed database from external access.

Isolation is irrelevant in distributed databases.

Isolation refers to the independence of transactions, ensuring they do not interfere with each other.

Isolation only applies to vertical partitioning.

34. Why is load balancing important in distributed databases?

Load balancing has no impact on distributed databases.

Load balancing helps distribute the workload evenly across multiple servers, preventing performance bottlenecks.

Load balancing only applies to centralized databases.

Load balancing slows down database performance.

35. What is the purpose of the 'MapReduce' paradigm in distributed databases?

Data Replication

Query Optimization

Data Encryption

Load Balancing

36. Explain the term 'replica consistency' in the context of distributed databases.

Replica consistency refers to having different data on each replica in a distributed database.

Replica consistency is irrelevant in distributed databases.

Replica consistency ensures that all replicas in a distributed database have the same data at all times.

Replica consistency only applies to horizontal partitioning.

37. How does the 'Chord algorithm' contribute to distributed hash tables (DHTs)?

Data Encryption

Query Optimization

Load Balancing

Node Lookup and Routing

38. Explain the concept of data partitioning in distributed databases.

Data partitioning is irrelevant in distributed databases.

Data partitioning involves dividing a database into partitions to distribute the workload and improve performance.

Data partitioning only applies to centralized databases.

Data partitioning is a process of combining multiple databases into a single entity.

39. What is a distributed index in the context of distributed databases?

A distributed index is used for decorative purposes in distributed databases.

A distributed index is irrelevant in distributed databases.

A distributed index speeds up query performance by facilitating faster data retrieval across multiple nodes.

A distributed index only applies to vertical partitioning.

40. How does the 'Raft consensus algorithm' differ from the 'Paxos algorithm' in terms of simplicity and usability in distributed systems?

Load Balancing

Consensus Protocol

Data Encryption

Query Optimization

41. Discuss the challenges associated with load balancing in large-scale distributed databases and propose strategies to optimize resource allocation and utilization.

Data Replication

Query Optimization

Load Balancing

Consistency Model

42. What is the role of a 'distributed transaction' in database systems?

Data Encryption

Ensuring Data Isolation

Query Execution

Transaction Management

43. What is the significance of the two-phase commit protocol in distributed databases?

The two-phase commit protocol has no impact on distributed databases.

The two-phase commit protocol is only relevant in centralized databases.

The two-phase commit protocol ensures atomicity in distributed database transactions.

The two-phase commit protocol is used for data replication.

44. What is the primary advantage of using distributed databases over centralized databases?

Distributed databases have slower performance than centralized databases.

Distributed databases are harder to manage.

Distributed databases provide better fault tolerance and scalability.

Centralized databases are more secure than distributed databases.

45. Explain the role of a distributed lock in maintaining data integrity.

Distributed locks are not used for data integrity.

Distributed locks ensure immediate consistency across all nodes.

Distributed locks prevent simultaneous access to shared resources, maintaining data integrity.

Distributed locks only apply to centralized databases.

46. What is Byzantine Fault Tolerance, and how does it address malicious attacks in distributed systems? Provide examples of its application in real-world scenarios.

Data Encryption

Consensus Protocol

Load Balancing

Query Optimization

47. Discuss the role of machine learning algorithms in optimizing performance and resource allocation in large-scale distributed databases, and analyze their potential impact on system efficiency.

Data Replication

Query Optimization

Load Balancing

Consistency Model

48. What is the role of the 'Paxos algorithm' in achieving consensus in distributed databases, and why is it challenging?

Load Balancing

Consensus Protocol

Data Encryption

Query Optimization

49. What challenges are associated with achieving global consistency in distributed databases?

Network Latency

Data Encryption

Query Optimization

Load Balancing

50. In the context of distributed databases, explain the principles and challenges of achieving data partitioning for massive datasets, and discuss the impact on query performance.

Data Replication

Query Optimization

Load Balancing

Consistency Model

Distributed Databases MCQ Test 1