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

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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With acks=all and min.insync.replicas=2, Kafka requires at least two in-sync replicas to acknowledge a write. If only one broker is alive, the condition fails, and NotEnoughReplicasException is thrown by the producer.

From Kafka Producer Exception Docs:

“NotEnoughReplicasException is thrown when the number of in-sync replicas is insufficient to satisfy acks=all with min.insync.replicas.”

NetworkException is generic and not raised here.

NotCoordinatorException is related to consumer group coordination.

NotLeaderForPartitionException is unrelated unless accessing an unassigned leader.

When using Kafka Connect with sink connectors (like S3 Sink), each task is assigned one or more topic partitions. The total number of partitions (6) is evenly distributed across the available tasks (2). Thus, each task will handle 3 partitions.

From Kafka Connect Documentation:

“Kafka Connect divides the topic partitions among available tasks. For example, a topic with six partitions and two tasks results in each task handling three partitions.”