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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:

1. (You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)

A) Set processing.guarantee=exactly_once_v2 in the Kafka Streams configuration.
B) Set enable.idempotence=true in the internal producer configuration of the Kafka Streams application.
C) Set enable.exactly_once=true in the Kafka Streams configuration.
D) Enable compaction on the output topic to handle duplicates.

2. Match each configuration parameter with the correct option.
To answer choose a match for each option from the drop-down. Partial
credit is given for each correct answer.

3. The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?

A) The sequential ID of the message committed into a partition
B) Its position in the producer's batch of messages
C) The number of bytes that overflowed beyond a producer batch of messages
D) The ID of the partition to which the message was committed

4. Which is true about topic compaction?

A) 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.
B) When a client produces a new event with an existing key, the broker immediately deletes the offset of the existing event.
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.

5. (You want to enrich the content of a topic by joining it with key records from a second topic.
The two topics have a different number of partitions.
Which two solutions can you use?
Select two.)

A) Repartition one topic to a new topic with the same number of partitions as the other topic (co- partitioning constraint) and use a KStream-KTable join.
B) Use a KStream-KTable join; Kafka Streams will automatically repartition the topics to satisfy the co- partitioning constraint.
C) Use a GlobalKTable for one of the topics where data does not change frequently and use a KStream- GlobalKTable join.
D) Create as many Kafka Streams application instances as the maximum number of partitions of the two topics and use a KStream-KTable join.

Solutions: