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, andall 15 can be activedepending on processing topology.

FromKafka 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.

Kafka Streams usestransactional producersto guaranteeexactly-once semantics (EOS). This ensures that both theoutput recordsandstate store updatesare committed atomically, avoiding duplication or partial writes.

FromKafka Streams Documentation > Processing Guarantees:

“Kafka Streams leveragesKafka’s transactional APIsto commit the output records and internal state updatesas a single atomic unit, thereby providing exactly-once semantics.”

Option A is incorrect because log compaction is not disabled for EOS.

Option C incorrectly describes a checkpointing system Kafka Streams does not use.

Option D refers to deduplication, which is not how EOS is achieved in Streams.

For proper delivery handling with acks=all:

Usecallbackto log or act on success/failure.

Usetry/catchto handle synchronous exceptions like serialization errors or network failures.

FromKafka Producer Documentation:

“Errors can be caught either via the returned Future or via the callback interface. For fatal errors, use a try/catch block around the send call.”

Option B is incorrect because send() returns a Future, not RecordMetadata directly.

Option D is invalid — ProducerRecord has no method called status().