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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q10-Q15):

NEW QUESTION # 10
You are writing a producer application and need to ensure proper delivery. You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
(Select two.)

A. Surround the call of producer.send() with a try/catch block to catch KafkaException.
B. Use a callback argument in producer.send() where you check delivery status.
C. Check that producer.send() returned a RecordMetadata object and is not null.
D. Check the value of ProducerRecord.status().

Answer: A,B

Explanation:
For proper delivery handling with acks=all:
* Usecallbackto log or act on success/failure.
* Usetry/catchto handle synchronous exceptions like serialization errors or network failures.
FromKafka Producer Documentation:
"Errors can be caught either via the returned Future<RecordMetadata> or via the callback interface. For fatal errors, use a try/catch block around the send call." Option B is incorrect because send() returns a Future, not RecordMetadata directly.
Option D is invalid - ProducerRecord has no method called status().
Reference:Kafka Producer Error Handling and Callback APIs

NEW QUESTION # 11
To produce data to a topic, a producer must provide the Kafka client with...

A. the list of brokers that have the data, the topic name and the partitions list
B. any broker from the cluster and the topic name and the partitions list
C. all the brokers from the cluster and the topic name
D. any broker from the cluster and the topic name

Answer: D

Explanation:
All brokers can respond to a Metadata request, so a client can connect to any broker in the cluster and then figure out on its own which brokers to send data to.

NEW QUESTION # 12
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. There is a bad data link layer (layer 2) connection from the producer to the cluster.
B. The producer code has an expensive callback function.
C. The producer is sending large batches of messages.
D. Compression is enabled.

Answer: B

NEW QUESTION # 13
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?

A. Decrease the session timeout of each consumer.
B. Increase the number of bytes the consumers read with each fetch request.
C. Disable auto commit and have the consumers manually commit offsets.
D. Remove some consumers from the consumer group.

Answer: B

Explanation:
To increase consumer throughput, one effective strategy is to increase the amount of data fetched in each poll by raising fetch.max.bytes or max.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
From Kafka Consumer Config Docs:
"Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead." Removing consumers (A) may reduce parallelism.
Manual commit (C) adds complexity, not throughput.
Decreasing session timeout (D) risks unnecessary rebalances.
Reference: Kafka Consumer Configuration > fetch.max.bytes

NEW QUESTION # 14
You have a topic with four partitions. The application reads from it using two consumers in a single consumer group.
Processing is CPU-bound, and lag is increasing.
What should you do?

A. Add more consumers to increase the level of parallelism of the processing.
B. Add more partitions to the topic to increase the level of parallelism of the processing.
C. Decrease the max.poll.records property of consumers.
D. Increase the max.poll.records property of consumers.

Answer: A

Explanation:
If the application is CPU-bound and lagging, adding more consumers to the group will allow better parallel processing, especially since the topic has 4 partitions, allowing up to 4 active consumers.
From Kafka Consumer Group Docs:
"Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized." B may help but requires repartitioning and coordination.
C or D affects how much data is polled, not how fast it's processed.
Reference: Kafka Consumer Concepts > Parallelism and Scaling

NEW QUESTION # 15
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