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

As documented in the official Apache Kafka Connect framework documentation, Kafka Connect workers form a logical cluster by using a Kafka consumer group. The configuration property that identifies this cluster is group.id.

All Kafka Connect workers configured with the same group.id automatically join the same Connect cluster. Kafka’s group coordination protocol is then used to distribute connector and task configurations among the workers, providing fault tolerance, scalability, and automatic rebalancing.

Option A (cluster.id) is a Kafka internal identifier used by brokers and is not configurable for Kafka Connect workers. Option B (worker.id) does not exist as a valid Kafka Connect configuration. Option D (connector.id) identifies individual connectors within a Connect cluster, not the worker cluster itself.

Therefore, the group.id property is the correct and officially documented configuration that determines which Kafka Connect cluster a worker belongs to.

Comprehensive and Detailed Explanation From Exact Extract:

✅ C. Consumers can consume both committed and uncommitted transactions.By default, Kafka consumers only read committed messages if they are configured with isolation.level=read_committed. However, if configured as read_uncommitted, they can also consume uncommitted (potentially aborted) transactional messages.

From Kafka Documentation:

"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."

✅ D. Information about producers and their transactions is stored in the _transaction_state topic.Kafka uses an internal topic named __transaction_state to maintain metadata about producer transactions. This topic is essential for tracking the transaction lifecycle, fencing, and recovery.

From Kafka Internals:

“Kafka stores the state of active and completed transactions in an internal topic called __transaction_state.”

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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