Confluent CCDAK : Confluent Certified Developer for Apache Kafka Certification Examination

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Confluent CCDAK Exam Syllabus Topics:

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:

1. What is accomplished by producing data to a topic with a message key?

A) Kafka brokers allow you to add more partitions to a given topic, without impacting the data flow for existing keys.
B) Messages with the same key are routed to a deterministically selected partition, enabling order guarantees within that partition.
C) It provides a mechanism for encrypting messages at the partition level to ensure secure data transmission.
D) Consumers can filter messages in real time based on the message key without processing unrelated messages.

2. (You are experiencing low throughput from a Java producer.
Kafka producer metrics show a low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?)

A) The producer code has an expensive callback function.
B) The producer is sending large batches of messages.
C) Compression is enabled.
D) There is a bad data link layer (Layer 2) connection from the producer to the cluster.

3. You create a topic named stream-logs with:
A replication factor of 3
Four partitions
Messages that are plain logs without a keyHow will messages be distributed across partitions?

A) Messages will be distributed round-robin among all the topic partitions.
B) All messages will be written to the same log segment.
C) The first message will always be written to partition 0.
D) Messages will be distributed among all the topic partitions with strict ordering.

4. Which partition assignment minimizes partition movements between two assignments?

A) RoundRobinAssignor
B) RangeAssignor
C) PartitionAssignor
D) StickyAssignor

5. Which is true about topic compaction?

A) When a client produces a new event with an existing key, the broker immediately deletes the offset of the existing event.
B) Topic compaction does not remove old events; instead, when clients consume events from a compacted topic, they store events in a hashmap that maintains the latest value.
C) When a client produces a new event with an existing key, the old value is overwritten with the new value in the compacted log segment.
D) Compaction will keep exactly one message per key after compaction of inactive log segments.

Solutions: