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

In Kafka Streams, the number of stream tasks = number of input partitions × num.stream.threads × number of instances, but only as many as the number of partitions can actively consume at once.

However, in this case, Kafka Streams assigns one task per partition, and because there are 5 partitions and 15 threads (3 instances × 5 threads), 15 tasks are created, and all 15 can be active depending on processing topology.

From Kafka Streams Developer Guide:

"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance."

So, 15 stream tasks will be created and 15 will be actively consuming.

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 onCompletion() method of Kafka’s Callback interface is executed once the broker acknowledges the message. This includes success or failure, and it is invoked asynchronously by the producer.

From Kafka Java Client API Documentation:

“The onCompletion method will be called when the record sent to the server has been acknowledged, or when an error occurs.”

This ensures that the record was sent and acknowledged, not just added to a batch or local buffer.