MCQs on Kafka Streams and Real-Time Processing | Apache Kafka MCQs Questions
Apache Kafka is a leading platform for real-time data streaming, and Kafka Streams API extends its capabilities for stream processing. Apache Kafka MCQs questions help learners grasp essential concepts such as stream processing topology, state stores, windowing, and building scalable applications. This comprehensive guide is designed for professionals and enthusiasts eager to explore Kafka Streams for efficient and real-time data processing solutions.
MCQs: Introduction to Kafka Streams API
- What is Kafka Streams API primarily used for?
a) Data storage
b) Real-time stream processing
c) Managing consumer groups
d) Configuring Kafka brokers - Kafka Streams API allows processing:
a) Only batch data
b) Real-time and historical data
c) Only historical data
d) File-based data - Which of the following is a key feature of Kafka Streams API?
a) Consumer offset management
b) Fault-tolerant stream processing
c) Data replication
d) Topic partitioning - Kafka Streams API uses:
a) Distributed File System
b) Log-based storage
c) Stateful and stateless processing
d) Object-based storage - Kafka Streams API can be directly embedded in:
a) Web applications
b) Database servers
c) Java applications
d) Virtual machines
MCQs: Streams Processing Topology and State Stores
- In Kafka Streams, a topology represents:
a) A network of producers and consumers
b) A data processing pipeline
c) A storage mechanism for offsets
d) A data compression format - What is a state store in Kafka Streams?
a) A temporary buffer for unprocessed data
b) A distributed database for storing application state
c) A log of all processed messages
d) A storage unit for failed transactions - Kafka Streams topologies are created using:
a) Topic APIs
b) StreamBuilder class
c) OffsetManager APIs
d) Consumer groups - State stores in Kafka Streams enable:
a) Stateless processing
b) Retrying failed transactions
c) Maintaining and querying application state
d) Monitoring broker performance - Which state store mechanism is used by default in Kafka Streams?
a) In-memory storage
b) RocksDB
c) HDFS
d) Redis
MCQs: Windowing, Joins, and Aggregations
- What is windowing in Kafka Streams?
a) Partitioning topics for better throughput
b) Breaking a stream into time-based segments
c) Compressing historical data
d) Synchronizing consumer groups - A tumbling window in Kafka Streams is defined as:
a) Overlapping windows
b) Non-overlapping, fixed-size time segments
c) Sliding windows with gaps
d) Dynamic, auto-adjusting windows - Kafka Streams joins can be performed on:
a) Topics and partitions
b) Two or more streams
c) Consumer groups and offsets
d) Producers and brokers - Which of the following is an example of aggregation in Kafka Streams?
a) Combining data from two topics
b) Calculating the sum of values over a window
c) Filtering duplicate records
d) Splitting a stream into multiple branches - Kafka Streams supports which type of joins?
a) Table-to-table joins only
b) Stream-to-stream and table-to-table joins
c) Topic-to-topic joins
d) Partition-to-partition joins
MCQs: Stateless vs Stateful Processing
- What is stateless processing in Kafka Streams?
a) Processing that depends on stored state
b) Processing independent of previous records
c) Processing using in-memory databases
d) Batch processing - Stateful processing in Kafka Streams requires:
a) No additional resources
b) Maintaining local state
c) Only real-time data
d) Dedicated consumer groups - Which of the following is an example of stateless processing?
a) Aggregating values
b) Filtering records based on conditions
c) Performing joins across topics
d) Managing state stores - Stateful operations include:
a) Map and filter
b) Windowing and aggregations
c) Partitioning topics
d) Compressing messages - Which processing type is generally faster in Kafka Streams?
a) Stateful processing
b) Stateless processing
c) Parallel processing
d) Asynchronous processing
MCQs: Building Scalable Stream Applications
- What is the primary method for achieving scalability in Kafka Streams?
a) Increasing consumer group size
b) Adding more partitions to topics
c) Using distributed state stores
d) Compressing message logs - Stream repartitioning is needed when:
a) A stream needs parallel processing
b) Consumer offsets need resetting
c) Topics are deleted
d) Producers are reconfigured - Which Kafka Streams feature ensures fault tolerance?
a) Consumer retries
b) Topic replication
c) State store changelogs
d) Broker leader election - A scalable Kafka Streams application should:
a) Minimize the use of stateful operations
b) Use a single partition
c) Always store data in HDFS
d) Avoid using producers - Kafka Streams applications are deployed as:
a) Standalone microservices
b) Database servers
c) Cloud-only applications
d) Part of brokers
General Knowledge MCQs on Kafka Streams
- What programming language is primarily used with Kafka Streams API?
a) Python
b) Java
c) JavaScript
d) Ruby - The main difference between Kafka Streams and Kafka Connect is:
a) Streams processes data, Connect moves data between systems
b) Streams is for brokers, Connect is for consumers
c) Connect is faster than Streams
d) Streams is cloud-based - What ensures exactly-once semantics in Kafka Streams?
a) Consumer offsets
b) Transactional APIs
c) Replication factor
d) Producer retries - Kafka Streams applications can be monitored using:
a) Zookeeper APIs
b) JMX metrics
c) Topic configuration logs
d) Consumer offset manager - Kafka Streams topology is:
a) Immutable once defined
b) Modifiable during runtime
c) Stored in brokers
d) Defined using Zookeeper
Answers Table
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) Real-time stream processing |
| 2 | b) Real-time and historical data |
| 3 | b) Fault-tolerant stream processing |
| 4 | c) Stateful and stateless processing |
| 5 | c) Java applications |
| 6 | b) A data processing pipeline |
| 7 | b) A distributed database for storing application state |
| 8 | b) StreamBuilder class |
| 9 | c) Maintaining and querying application state |
| 10 | b) RocksDB |
| 11 | b) Breaking a stream into time-based segments |
| 12 | b) Non-overlapping, fixed-size time segments |
| 13 | b) Two or more streams |
| 14 | b) Calculating the sum of values over a window |
| 15 | b) Stream-to-stream and table-to-table joins |
| 16 | b) Processing independent of previous records |
| 17 | b) Maintaining local state |
| 18 | b) Filtering records based on conditions |
| 19 | b) Windowing and aggregations |
| 20 | b) Stateless processing |
| 21 | b) Adding more partitions to topics |
| 22 | a) A stream needs parallel processing |
| 23 | c) State store changelogs |
| 24 | a) Minimize the use of stateful operations |
| 25 | a) Standalone microservices |
| 26 | b) Java |
| 27 | a) Streams processes data, Connect moves data between systems |
| 28 | b) Transactional APIs |
| 29 | b) JMX metrics |
| 30 | a) Immutable once defined |