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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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.

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.”