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

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.

To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.

From Kafka Consumer Error Handling Docs:

“On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record.”

A does not prevent re-processing the bad record.

C is invalid; there's no skip() method in the Kafka consumer API.

D results in service interruption — not ideal for resiliency.

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.