Free PDF Quiz 2026 Confluent CCDAK: Confluent Certified Developer for Apache Kafka Certification Examination Updated Study Guide Pdf

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Confluent CCDAK Exam Syllabus Topics:

Section	Objectives
Topic 1: Kafka Fundamentals	- Kafka architecture and core concepts 1. Replication and fault tolerance 2. Brokers, topics, partitions - Data flow in Kafka 1. Producer and consumer model 2. Offset management
Topic 2: Kafka Producers and Consumers	- Producer API 1. Reliability and acknowledgments 2. Serialization and partitioning strategies - Consumer API 1. Rebalancing and offset commits 2. Consumer groups
Topic 3: Kafka Streams and Processing	- Event processing patterns 1. Windowing and aggregation 2. Exactly-once semantics - Kafka Streams API 1. Stream processing fundamentals 2. Stateful vs stateless processing
Topic 4: Kafka Ecosystem and Confluent Platform	- Schema Registry 1. Avro, Protobuf, JSON Schema usage - ksqlDB and connectors 1. Kafka Connect basics 2. Stream querying concepts

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q58-Q63):

NEW QUESTION # 58
A stream processing application is consuming from a topic that has five partitions. You run three instances of your application, each with num.streams.threads set to five.
You need to identify the amount of total stream tasks that will be created and the amount that will be actively consuming messages from the input topic.
Which option is correct?

A. 5 created, 1 actively consuming
B. 15 created, 15 actively consuming
C. 5 created, 5 actively consuming
D. 15 created, 5 actively consuming

Answer: B

NEW QUESTION # 59
You need to explain the best reason to implement the consumer callback interface ConsumerRebalanceListener prior to a Consumer Group Rebalance.
Which statement is correct?

A. Previous log files are deleted.
B. Partitions assigned to a consumer may change.
C. Offsets are compacted.
D. Partition leaders may change.

Answer: B

Explanation:
The ConsumerRebalanceListener lets you handle partition assignments and revocations during rebalances.
This is critical for managing offsets, stateful processing, or external transactions.
From Kafka 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 partitions assigned to the consumer change.
B, C, and D are unrelated to consumer rebalancing directly.
Reference: Kafka Consumer JavaDocs > ConsumerRebalanceListener

NEW QUESTION # 60
Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1Which four statements match the expected behavior?(Select four.)

A. The offsets topic will use the broker default replication factor.
B. The connector will commit offsets to a topic called connect-offsets.
C. The connector will wait 60000ms before trying to commit offsets for tasks.
D. The connector will wait 500ms for offset data to be committed.

Answer: A,B,C,D

Explanation:
Each of these settings plays a key role inKafka Connect offset management:
* offset.flush.interval.ms=60000 # Offset commits happen every 60 seconds.
* offset.flush.timeout.ms=500 # The flush operation times out if it takes more than 500 ms.
* offset.storage.topic=connect-offsets # This is the internal topic for storing source offsets.
* offset.storage.replication.factor=-1 # This uses thebroker's default replication factor(commonly set via default.replication.factor).
FromKafka Connect Docs:
"If offset.storage.replication.factor=-1, the value will be determined by the broker's default replication factor." Reference:Kafka Connect Worker Configuration

NEW QUESTION # 61
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 value schema.
D. Both topics have the same retention time.

Answer: A,B

Explanation:
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.
Reference:Kafka Streams Concepts > Joins and Co-Partitioning

NEW QUESTION # 62
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?

A. Less resilient
B. Message order not preserved
C. Reduce throughput
D. At least once delivery is not guaranteed

Answer: B

Explanation:
Some messages may require multiple retries. If there are more than 1 requests in flight, it may result in messages received out of order. Note an exception to this rule is if you enable the producer settingenable.
idempotence=true which takes care of the out of ordering case on its own. Seehttps://issues.apache.org/jira
/browse/KAFKA-5494

NEW QUESTION # 63
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