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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Increasing partitions increases parallelism, but also means:

Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.

This can result in increased lag, especially under high load.

From Kafka Topic Management Docs:

“Increasing the number of partitions increases consumer work, and if consumers can’t keep up, lag can accumulate.”

A is false: existing data is not redistributed.

B is false: records with the same key always map to the same partition based on hash.

D is not directly impacted by the partition count.

Kafka Connect can run in standalone mode or distributed mode. For simple tasks like reading logs from a file on a single host, standalone mode is recommended.

From Kafka Connect User Guide:

“Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file).”

Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.