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.

If the consumer fails to send heartbeats in time (due to being slow under load), it is considered dead, causing a rebalance. Increasing session.timeout.ms gives the consumer more time to process messages and send heartbeats, preventing premature removal.

From Kafka Docs > Heartbeats and Failures:

“If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance.”

Reducing max.poll.records might slow consumption further. More consumers won’t help since each partition already has a dedicated consumer.

For Kafka Streams to perform joins, topics must be co-partitioned, meaning:

They have the same number of partitions (A), and

They use the same partitioning strategy (typically by key) (B)

From Kafka Streams Documentation > Joins:

“Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner.”

Value schema (C) and retention time (D) have no bearing on join compatibility.