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Confluent CCDAK Practice Exams, Latest CCDAK Test Format

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Confluent Certified Developer for Apache Kafka (CCDAK) certification exam is designed for individuals who want to demonstrate their expertise in using Apache Kafka to build data streaming applications. Confluent Certified Developer for Apache Kafka Certification Examination certification is offered by Confluent, a leading provider of data streaming platforms and solutions. The CCDAK Certification Exam is an industry-recognized validation of an individual's knowledge and skills in designing, developing, and deploying event-driven, real-time streaming applications using Apache Kafka.

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TOP CCDAK Practice Exams: Confluent Certified Developer for Apache Kafka Certification Examination - Valid Confluent Latest CCDAK Test Format

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By becoming certified, developers can demonstrate their proficiency in Apache Kafka and differentiate themselves from their peers. Confluent Certified Developer for Apache Kafka Certification Examination certification showcases the candidate's knowledge and validates their ability to design and develop high-quality streaming applications using Kafka. Furthermore, it helps organizations to identify individuals with the necessary skills and expertise to design and build data streaming applications, which is essential for addressing business problems in real-time.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q73-Q78):

NEW QUESTION # 73
You need to configure a sink connector to write records that fail into a dead letter queue topic. Requirements:
Topic name: DLQ-Topic
Headers containing error context must be added to the messagesWhich three configuration parameters are necessary?(Select three.)

A. errors.tolerance=none
B. errors.deadletterqueue.context.headers.enable=true
C. errors.log.include.messages=true
D. errors.tolerance=all
E. errors.log.enable=true
F. errors.deadletterqueue.topic.name=DLQ-Topic

Answer: B,D,F

Explanation:
To send failed records to a dead letter queue (DLQ), you must configure:
errors.tolerance=all: Tells the connector to not fail on errors but handle them (e.g., send to DLQ).
errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.
errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.
From Kafka Connect Error Handling Docs:
"Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs.
Headers can include failure metadata."
Options D, E, F are related to logging, not DLQ behavior.
Reference: Kafka Connect Configurations > Error Handling

NEW QUESTION # 74
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. Increase the max.poll.records property of consumers.
B. Add more consumers to increase the level of parallelism of the processing.
C. Add more partitions to the topic to increase the level of parallelism of the processing.
D. Decrease the max.poll.records property of consumers.

Answer: B

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 # 75
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You want to optimize its throughput so the consumer group processes more messages in the same amount of time.
Which changes increase the consumption throughput?

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

Answer: C

NEW QUESTION # 76
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. Increase the max.poll.records property of consumers.
B. Add more consumers to increase the level of parallelism of the processing.
C. Add more partitions to the topic to increase the level of parallelism of the processing.
D. Decrease the max.poll.records property of consumers.

Answer: B

Explanation:
If the application isCPU-boundandlagging, addingmore consumersto the group will allow betterparallel processing, especially since the topic has4 partitions, allowing up to 4 active consumers.
FromKafka 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 # 77
Select the Kafka Streams joins that are always windowed joins.

A. KStream-KTable join
B. KTable-KTable join
C. KStream-GlobalKTable
D. KStream-KStream join

Answer: D

Explanation:
Seehttps://docs.confluent.io/current/streams/developer-guide/dsl-api.html#joining

NEW QUESTION # 78
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