Confluent Certified Developer for Apache Kafka Certification Examination : CCDAK

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Confluent CCDAK Exam Syllabus Topics:

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:

1. Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?

A) Log the bad record and call the consumer.skip() method.
B) Log the bad record and seek the consumer to the offset of the next record.
C) Throw a runtime exception to trigger a restart of the application.
D) Log the bad record, no other action needed.

2. A producer is configured with the default partitioner. It is sending records to a topic that is configured with five partitions. The record does not contain any key.
What is the result of this?

A) An error will be raised and no record will be sent.
B) Records will be sent to partition 0.
C) Records will be dispatched among the available partitions.
D) Records will be sent to the least used partition.

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

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

5. You need to correctly join data from two Kafka topics.
Which two scenarios will allow for co-partitioning?
(Select two.)

A) Both topics have the same key and partitioning strategy.
B) Both topics have the same number of partitions.
C) Both topics have the same retention time.
D) Both topics have the same value schema.

Solutions: