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

To ensureexactly-once deliveryand avoid duplicates even during retries:

enable.idempotence=trueensures deduplication on the broker

retries=2147483647allows unlimited retries on retriable errors

max.in.flight.requests.per.connection=5is themaximum value that preserves message order with idempotence

FromKafka Producer Config Docs:

“To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection ≤ 5.”

A is unrelated (consumer-side)

C disables retries

D disables idempotence, leading to duplicates

If a key isnot specified, thedefault Kafka partitioner uses round-robinlogic to assign partitions. This leads toeven distributionof messages across all partitions.

FromKafka Producer Design:

“If the key is null, the producer will assign the partition in a round-robin fashion to achieve load balancing.”

There isno error, and partition 0 isnot alwayschosen in this case.

The ConsumerRebalanceListener lets youhandle partition assignments and revocationsduring rebalances. This is critical for managingoffsets,stateful processing, orexternal transactions.

FromKafka 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 partitionsassigned to the consumer change.

B, C, and D are unrelated to consumer rebalancing directly.

Thebatch.sizeconfig sets themaximum number of bytes to batch per partitionbefore sending. This allows Kafka producers toamortize I/Oand improve throughput.

FromKafka Producer Configuration Docs:

“batch.size is the maximum amount of data per partition the producer will batch before sending.”

buffer.memory sets total memory for the producer, not per-batch.

send.buffer.bytes is aTCP socket buffer, not a Kafka config.

max.block.ms controls blocking time, not size.

For Kafka Streams to performjoins, topics must beco-partitioned, meaning:

They have thesame number of partitions(A), and

They use thesame partitioning strategy(typically by key) (B)

FromKafka 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.