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Comprehensive and Detailed Explanation From Exact Extract:

✅C. Consumers can consume both committed and uncommitted transactions.By default,Kafka consumers only read committed messagesif they are configured with isolation.level=read_committed. However, if configured as read_uncommitted, theycan also consume uncommitted (potentially aborted) transactional messages.

From Kafka Documentation:

"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."

✅D. Information about producers and their transactions is stored in the _transaction_state topic.Kafka uses an internal topic named__transaction_stateto maintain metadata about producer transactions. This topic is essential for tracking thetransaction lifecycle, fencing, and recovery.

From Kafka Internals:

“Kafka stores the state of active and completed transactions in an internal topic called __transaction_state.”

RetriableException is a subclass of KafkaException, indicating transient issues thatmight succeed if retried. Examples include:

LeaderNotAvailableException: Happens when metadata is not yet propagated or leader election is in progress.

NotEnoughReplicasException: Happens when the number of in-sync replicas is insufficient. This can be transient if replicas come back online.

From theApache Kafka Java Client documentation:

"These exceptions (like LeaderNotAvailableException, NotEnoughReplicasException) are transient and the client will retry them automatically depending on retry configuration."

RecordTooLargeExceptionandAuthorizationExceptionarenon-retriableas they indicate client-side or permission errors.

Unit Testing→ MockProducer

Integration Testing→ Testcontainers

Performance Testing→ Trogdor

Mock Data Generation→ Connect Datagen

MockProducer: Simulates a Kafka producer in unit tests (no real broker interaction).

Testcontainers: Spawns Kafka in Docker forreal environment testing.

Trogdor: Kafka’s built-inperformance load testingframework.

Connect Datagen: Createssample source recordsfor test and demo purposes.

FromKafka Developer Tools Guide:

“Kafka developers commonly use MockProducer for unit tests, Testcontainers for integration, and Trogdor for performance tests.”

Kafka clients useSLF4Jas a logging facade. To redirect Kafka logs tolog4j2, you must include theSLF4J binding for log4j2in your classpath:

“org.apache.logging.log4j:log4j-slf4j-impl bridges SLF4J to Log4j2 so that Kafka's logs integrate into your application's Log4j2 configuration.”

Option A is forLog4j 1.x, which is outdated.

Option C is incorrect; Kafka clients don't include logging bindings.

Option D is insufficient without the SLF4J bridge.

Kafka Connectsink connectorsuse thestandard Kafka consumer mechanismto track offsets, which means offsets are stored in the__consumer_offsets internal topic.

FromKafka Connect Documentation:

“Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer.”

The other options refer tosource connectorsor worker configuration.

offsets.storage.topic is relevant forsource connectors.

The ConsumerRebalanceListener lets youhandle partition assignments and revocationsduring rebalances. This is critical for managingoffsets,stateful processing, orexternal transactions.

FromKafka Consumer Rebalance Docs:

“Implementing ConsumerRebalanceListener allows your application to take action before and after partitions are reassigned.”

A is true: It lets your app react when partitionsassigned to the consumer change.

B, C, and D are unrelated to consumer rebalancing directly.

To increase consumerthroughput, one effective strategy is toincrease the amount of datafetched in each poll by raisingfetch.max.bytesormax.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.

FromKafka Consumer Config Docs:

“Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead.”

Removing consumers (A) may reduce parallelism.

Manual commit (C) adds complexity, not throughput.

Decreasing session timeout (D) risks unnecessary rebalances.

If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.

FromKafka Docs > Heartbeats and Failures:

“If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance.”

Reducing max.poll.records mightslow consumption further. More consumers won’t help since each partition already has a dedicated consumer.