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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:

1. Your application is consuming from a topic with one consumer group.
The number of running consumers is equal to the number of partitions.
Application logs show that some consumers are leaving the consumer group during peak time, triggering a rebalance. You also notice that your application is processing many duplicates.
You need to stop consumers from leaving the consumer group.
What should you do?

A) Increase session.timeout.ms property.
B) Reduce max.poll.records property.
C) Split consumers in different consumer groups.
D) Add more consumer instances.

2. (You are building real-time streaming applications using Kafka Streams.
Your application has a custom transformation.
You need to define custom processors in Kafka Streams.
Which tool should you use?)

A) Kafka Streams Custom Transformation Language
B) Kafka Streams Domain Specific Language (DSL)
C) TopologyTestDriver
D) Processor API

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

4. (A consumer application needs to use an at-most-once delivery semantic.
What is the best consumer configuration and code skeleton to avoid duplicate messages being read?)

A) auto.offset.reset=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);for (var record : records) {// Any processing}consumer.commitAsync();}
B) auto.offset.reset=earliest and enable.auto.commit=truewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
C) auto.offset.reset=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
D) auto.offset.reset=latest and enable.auto.commit=truewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);for (var record : records) {// Any processing}consumer.commitAsync();}

5. (A stream processing application tracks user activity in online shopping carts, including items added, removed, and ordered throughout the day for each user.
You need to capture data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?)

A) Tumbling
B) Hopping
C) Sliding
D) Session

Solutions: