CCDAK Exam Dumps : Confluent Certified Developer for Apache Kafka Certification Examination

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

From Kafka 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 might slow consumption further. More consumers won’t help since each partition already has a dedicated consumer.

To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.

From Kafka Consumer Error Handling Docs:

“On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record.”

A does not prevent re-processing the bad record.

C is invalid; there's no skip() method in the Kafka consumer API.

D results in service interruption — not ideal for resiliency.

Low I/O thread activity with blocked throughput often indicates that producer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.

From Kafka Producer Performance Guide:

“Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput.”

Compression (A) may slightly impact CPU but not I/O thread usage.

Large batches (B) improve throughput if managed correctly.

A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.