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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q68-Q73):

NEW QUESTION # 68
You are experiencing low throughput from a Java producer. You monitor the Kafka metrics and notice that the metrics for that producer show low I/O thread ratio and low I/O thread wait ratio.
What is most likely the 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. There is a bad data link layer (layer 2) connection from the producer to the cluster.
D. Compression is enabled.

Answer: A

NEW QUESTION # 69
Your streams application is reading from an input topic that has 5 partitions. You run 5 instances of your application, each with num.streams.threads set to 5. How many stream tasks will be created and how many will be active?

A. 5 created, 1 active
B. 25 created, 25 active
C. 25 created, 5 active
D. 5 created, 5 active

Answer: C

Explanation:
One partition is assigned a thread, so only 5 will be active, and 25 threads (i.e. tasks) will be created

NEW QUESTION # 70
(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=-1
Which two statements match the expected behavior?
Select two.)

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

Answer: A,C

Explanation:
Apache Kafka Connect stores source connector offsets in a Kafka topic defined by the offset.storage.topic configuration. Since this property is explicitly set to connect-offsets, Kafka Connect will commit offsets to that topic, making Option C correct.
The offset.storage.replication.factor controls how many replicas the offsets topic will have. When this value is set to -1, Kafka Connect uses the broker's default replication factor, as documented in the Kafka Connect worker configuration reference. Therefore, Option A is also correct.
Option B is incorrect because Kafka Connect does not use Kafka's internal consumer offsets topic (__consumer_offsets). It always uses the configured offsets topic. Option D is incorrect because offset.flush.
timeout.ms defines how long Connect will wait for offset commits to complete, not how long it waits before attempting a commit. The commit interval itself is controlled by offset.flush.interval.ms (60 seconds in this case).
Thus, the correct statements that match the expected behavior are A and C.

NEW QUESTION # 71
A client connects to a broker in the cluster and sends a fetch request for a partition in a topic. It gets an exception Not Leader For Partition Exception in the response. How does client handle this situation?

A. Send metadata request to the same broker for the topic and select the broker hosting the leader replica
B. Get the Broker id from Zookeeper that is hosting the leader replica and send request to it
C. Send fetch request to each Broker in the cluster
D. Send metadata request to Zookeeper for the topic and select the broker hosting the leader replica

Answer: A

Explanation:
In case the consumer has the wrong leader of a partition, it will issue a metadata request. The Metadata request can be handled by any node, so clients know afterwards which broker are the designated leader for the topic partitions. Produce and consume requests can only be sent to the node hosting partition leader.

NEW QUESTION # 72
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. Partition leaders may change.
C. Partitions assigned to a consumer may change.
D. Offsets are compacted.

Answer: C

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 # 73
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