MCQs on Introduction to Apache Kafka | Apache Kafka MCQs Questions
Apache Kafka is a powerful distributed event-streaming platform widely used for building real-time data pipelines and event-driven architectures. Apache Kafka MCQs questions cover its history, evolution, and key concepts like brokers, topics, partitions, producers, and consumers. This comprehensive knowledge is essential for professionals aiming to leverage Kafka for scalable and efficient data integration and processing across various industries.
MCQs: Understanding Event-Driven Architectures
- What is the main purpose of event-driven architectures?
a) Processing batch jobs
b) Real-time data streaming and integration
c) Data storage optimization
d) File system management - Event-driven systems are characterized by:
a) Synchronous communication
b) Asynchronous communication
c) Periodic backups
d) Scheduled batch processes - In an event-driven system, what triggers an event?
a) A scheduled timer
b) An action or change in state
c) A batch process
d) A database query - Which type of messaging model does Apache Kafka use?
a) Point-to-point
b) Publish-subscribe
c) Request-response
d) Multi-threaded - What are the benefits of event-driven architectures?
a) High latency and complexity
b) Real-time processing and scalability
c) Reduced flexibility
d) Increased hardware dependency
MCQs: History and Evolution of Kafka
- Who originally developed Apache Kafka?
a) Google
b) LinkedIn
c) Facebook
d) Twitter - Apache Kafka was initially released in:
a) 2005
b) 2008
c) 2011
d) 2015 - Kafka became a part of which Apache Software Foundation project?
a) Apache Storm
b) Apache Hadoop
c) Apache Flink
d) Apache Incubator - What motivated the development of Kafka?
a) The need for a high-throughput messaging system
b) Lack of storage options in existing systems
c) Distributed database requirements
d) Cloud-native architecture - Kafka’s design was influenced by which system?
a) RabbitMQ
b) ActiveMQ
c) LinkedIn’s data pipelines
d) Microsoft Azure
MCQs: Kafka Core Concepts
- What is the role of a Kafka broker?
a) Store log data permanently
b) Manage message delivery between producers and consumers
c) Execute SQL queries
d) Handle HTTP requests - In Kafka, what is a topic?
a) A storage unit for messages
b) A log of message streams
c) A metadata structure
d) A partition of data - Partitions in Kafka enable:
a) Sequential processing only
b) Parallel processing and scalability
c) File compression
d) Distributed SQL queries - Which component in Kafka is responsible for producing messages?
a) Consumer
b) Producer
c) Broker
d) Zookeeper - A Kafka consumer subscribes to:
a) Partitions
b) Brokers
c) Topics
d) Producers
MCQs: Kafka Use Cases and Industry Adoption
- Apache Kafka is primarily used for:
a) Real-time data streaming
b) Static file storage
c) Machine learning models
d) Web hosting - Which industry heavily relies on Kafka for event-driven architectures?
a) Healthcare
b) E-commerce and finance
c) Tourism
d) Agriculture - Kafka can be used to build:
a) Static websites
b) Real-time analytics pipelines
c) Simple email servers
d) Database migration tools - How does Kafka support real-time data integration?
a) By synchronizing batch jobs
b) Through distributed streaming and pub-sub messaging
c) By replicating database records
d) Using REST APIs only - Which of the following is an example of Kafka use?
a) Analyzing sensor data in IoT systems
b) Generating SQL reports
c) Processing images
d) Creating XML-based web services
General Knowledge MCQs on Kafka
- What is the default storage mechanism for Kafka messages?
a) Disk-based logs
b) In-memory queues
c) JSON files
d) Databases - Kafka guarantees message ordering within:
a) Topics
b) Brokers
c) Partitions
d) Clusters - Which tool is commonly used to manage Kafka clusters?
a) Apache Zookeeper
b) Spark SQL
c) Hadoop HDFS
d) Flink Dashboard - Kafka uses what kind of commit log?
a) Append-only
b) Read-only
c) Update-in-place
d) Hierarchical - What determines the retention of messages in Kafka?
a) Broker configuration
b) Consumer offsets
c) Topic settings
d) Partition replication
Performance and Optimization MCQs
- To improve throughput in Kafka, you should:
a) Increase the number of partitions
b) Reduce consumer instances
c) Use a single producer
d) Avoid partitioning - Kafka replication factor ensures:
a) Faster message delivery
b) Fault tolerance
c) Reduced storage cost
d) Parallel processing - What happens when a Kafka broker fails?
a) The cluster halts entirely
b) The partition leader is re-elected
c) Data is permanently lost
d) Consumers stop reading - Which feature of Kafka helps in data reprocessing?
a) Consumer offset rewind
b) Producer retries
c) Cluster backup
d) Leader election - How can you monitor Kafka performance?
a) By analyzing consumer group logs
b) Using tools like Kafka Manager and JMX
c) Through SQL queries
d) Using REST APIs
Answers Table
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) Real-time data streaming and integration |
| 2 | b) Asynchronous communication |
| 3 | b) An action or change in state |
| 4 | b) Publish-subscribe |
| 5 | b) Real-time processing and scalability |
| 6 | b) LinkedIn |
| 7 | c) 2011 |
| 8 | d) Apache Incubator |
| 9 | a) The need for a high-throughput messaging system |
| 10 | c) LinkedIn’s data pipelines |
| 11 | b) Manage message delivery between producers and consumers |
| 12 | b) A log of message streams |
| 13 | b) Parallel processing and scalability |
| 14 | b) Producer |
| 15 | c) Topics |
| 16 | a) Real-time data streaming |
| 17 | b) E-commerce and finance |
| 18 | b) Real-time analytics pipelines |
| 19 | b) Through distributed streaming and pub-sub messaging |
| 20 | a) Analyzing sensor data in IoT systems |
| 21 | a) Disk-based logs |
| 22 | c) Partitions |
| 23 | a) Apache Zookeeper |
| 24 | a) Append-only |
| 25 | c) Topic settings |
| 26 | a) Increase the number of partitions |
| 27 | b) Fault tolerance |
| 28 | b) The partition leader is re-elected |
| 29 | a) Consumer offset rewind |
| 30 | b) Using tools like Kafka Manager and JMX |