MCQs on Kafka Architecture and Components | Apache Kafka MCQs Questions

Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and scalable messaging systems. In this chapter, we explore the Kafka cluster architecture, Zookeeper’s role, and the inner workings of brokers and topics. Understanding partitions, replication mechanisms, and high availability is critical for mastering Kafka’s architecture. These Apache Kafka MCQs questions will test your knowledge of these core concepts and help you prepare for interviews, certifications, or real-world implementations.

Multiple-Choice Questions (MCQs)

Kafka Cluster Architecture

1. What is a Kafka cluster composed of?
   a) Brokers and consumers
   b) Producers and brokers
   c) Brokers and Zookeeper nodes
   d) Consumers and partitions
2. What is the purpose of Kafka’s cluster architecture?
   a) Ensure high availability and scalability
   b) Limit the number of producers
   c) Reduce data persistence time
   d) Optimize hardware usage
3. How does Kafka achieve fault tolerance?
   a) Through replication across brokers
   b) By caching data on producers
   c) Using high-memory nodes
   d) By avoiding disk writes
4. Which term best describes Kafka’s design?
   a) Centralized messaging queue
   b) Distributed event-streaming platform
   c) Standalone data processor
   d) Single-node message broker
5. What are Kafka topics used for?
   a) Partitioning producers
   b) Organizing and storing records
   c) Managing Zookeeper nodes
   d) Logging broker activity

Zookeeper and Its Role in Kafka

6. What is the primary role of Zookeeper in Kafka?
   a) Monitoring and managing brokers
   b) Handling data replication
   c) Performing producer-to-consumer communication
   d) Managing topic partitions
7. Which of the following is NOT managed by Zookeeper in Kafka?
   a) Broker metadata
   b) Topic configurations
   c) Consumer group coordination
   d) Message serialization
8. What happens if Zookeeper fails in a Kafka cluster?
   a) Kafka stops processing new messages
   b) Kafka continues with limited functionality
   c) Kafka brokers become unresponsive
   d) Producers halt message production
9. How is Zookeeper data stored?
   a) In memory only
   b) As logs on disk
   c) In a distributed database
   d) Within Kafka topics
10. What is a Zookeeper quorum?
    a) Minimum number of nodes required to maintain consistency
    b) A group of producers and consumers
    c) A special Kafka topic for metadata
    d) A network of interconnected brokers

Kafka Brokers and Topics

11. What is the role of a Kafka broker?
    a) Storing and serving messages for topics
    b) Managing partitions within Zookeeper
    c) Synchronizing producer and consumer communication
    d) Replicating data across clusters
12. How are topics distributed in Kafka?
    a) Evenly across brokers
    b) Based on consumer offsets
    c) Randomly among producers
    d) Using Zookeeper nodes
13. What happens when a Kafka broker fails?
    a) The leader for its partitions is reassigned
    b) The entire cluster stops functioning
    c) Producers resend messages from the beginning
    d) Consumers switch to backup brokers
14. What determines the number of partitions in a Kafka topic?
    a) Topic configuration during creation
    b) Zookeeper quorum settings
    c) The number of consumers in the group
    d) Broker memory capacity
15. What is a retention policy in Kafka?
    a) A rule for how long messages are retained
    b) A strategy for distributing partitions
    c) A mechanism for managing consumer offsets
    d) A configuration for topic replication

Partitions and Replication Mechanisms

16. What is the purpose of partitions in Kafka?
    a) Parallelizing message processing
    b) Minimizing broker storage usage
    c) Caching producer data
    d) Increasing consumer group size
17. How does Kafka ensure high availability of messages?
    a) By replicating partitions across brokers
    b) Using in-memory storage
    c) Through producer-side buffering
    d) By assigning each topic to one broker
18. What is a leader partition in Kafka?
    a) The partition responsible for all writes and reads
    b) The largest partition in a topic
    c) A partition managed by Zookeeper
    d) The partition containing metadata logs
19. How does Kafka handle replication factor conflicts?
    a) Automatically adjusts the replication factor
    b) Marks the topic as unavailable
    c) Prioritizes leader partitions
    d) Deletes excess replicas
20. What is ISR (In-Sync Replica) in Kafka?
    a) A replica synchronized with the leader partition
    b) A consumer offset tracking mechanism
    c) A backup partition stored in Zookeeper
    d) A broker configuration for retention policies

High Availability and Fault Tolerance

21. What is the minimum replication factor recommended for high availability?
    a) 1
    b) 2
    c) 3
    d) 4
22. How does Kafka achieve fault tolerance?
    a) By distributing partitions across brokers
    b) Using persistent memory only
    c) Assigning all partitions to a single broker
    d) Configuring Zookeeper for load balancing
23. What happens during a partition reassignment in Kafka?
    a) Messages are redistributed across brokers
    b) Consumer offsets are reset
    c) Topics are recreated
    d) Zookeeper assigns a new leader
24. Which configuration prevents data loss in Kafka?
    a) Setting acks=all in producers
    b) Using acks=0 in producers
    c) Increasing the number of consumers
    d) Reducing the replication factor
25. What is the significance of the “min.insync.replicas” setting in Kafka?
    a) It ensures a minimum number of replicas are in sync before acknowledging writes
    b) It controls the maximum number of consumers per group
    c) It manages topic creation in Zookeeper
    d) It balances partitions across brokers
26. How does Kafka handle broker failures?
    a) Automatically elects new leaders for partitions
    b) Reverts to single-node operation
    c) Requires manual intervention
    d) Halts all message production
27. What is a common strategy for ensuring fault tolerance in Kafka clusters?
    a) Using a high replication factor for topics
    b) Minimizing the number of brokers
    c) Avoiding consumer groups
    d) Disabling Zookeeper
28. What is the purpose of the Kafka Controller?
    a) Managing broker states and leader elections
    b) Synchronizing producer and consumer communication
    c) Replicating data across partitions
    d) Monitoring topic retention policies
29. How are consumers affected when a broker fails?
    a) They are redirected to a new leader for the partition
    b) They stop consuming messages entirely
    c) They reset their offsets to zero
    d) They switch to in-memory processing
30. What is the main benefit of Kafka’s fault-tolerant design?
    a) Reliable message delivery despite failures
    b) Eliminating the need for replication
    c) Reducing hardware requirements
    d) Simplifying producer logic

Answers