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

RetriableException is a subclass of KafkaException, indicating transient issues that might 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 the Apache Kafka Java Client documentation:

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

RecordTooLargeException and AuthorizationException are non-retriable as they indicate client-side or permission errors.

The ConsumerRebalanceListener lets you handle partition assignments and revocations during rebalances. This is critical for managing offsets, stateful processing, or external transactions.

From Kafka 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 partitions assigned to the consumer change.

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

According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer’s network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.

This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.

An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer’s sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.

Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.

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