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To become certified as a Confluent CCDAK certified developer for Apache Kafka, candidates must pass a rigorous online examination. CCDAK exam is computer-based and consists of 90 multiple choice questions, and has a time limit of 120 minutes. CCDAK exam is designed to challenge the technical skills and knowledge needed for creating and managing Apache Kafka applications using Confluent's platform. Passing the Confluent CCDAK Exam demonstrates a proficiency in working with Kafka streams in a distributed environment using Confluent platform, allowing candidates to leverage this certification to advance their careers.

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The CCDAK Certification is highly regarded by employers and industry professionals as a mark of excellence in Apache Kafka development. It is an excellent way for developers to demonstrate their knowledge and expertise in Apache Kafka and to differentiate themselves from their peers in a competitive job market. Additionally, being certified in Apache Kafka helps professionals to stay current with the latest trends and developments in the rapidly evolving world of big data and real-time streaming.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q52-Q57):

NEW QUESTION # 52
Consumer failed to process record # 10 and succeeded in processing record # 11. Select the course of action that you should choose to guarantee at least once processing

A. Do not commit until successfully processing the record #10
B. Commit offsets at 11
C. Commit offsets at 10

Answer: B

Explanation:
Here, you shouldn't commit offsets 11 or 10 as it would indicate that the message #10 has been processed successfully.

NEW QUESTION # 53
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 partitions to the topic to increase the level of parallelism of the processing.
B. Increase the max.poll.records property of consumers.
C. Decrease the max.poll.records property of consumers.
D. Add more consumers to increase the level of parallelism of the processing.

Answer: D

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 # 54
How can you gracefully make a Kafka consumer to stop immediately polling data from Kafka and gracefully shut down a consumer application?

A. Call consumer.wakeUp() and catch a WakeUpException
B. Call consumer.poll() in another thread
C. Kill the consumer thread

Answer: A

Explanation:
See https://stackoverflow.com/a/37748336/3019499

NEW QUESTION # 55
Which actions will trigger partition rebalance for a consumer group? (select three)

A. Increase partitions of a topic
B. Remove a broker from the cluster
C. Add a new consumer to consumer group
D. A consumer in a consumer group shuts down
E. Add a broker to the cluster

Answer: A,C,D

Explanation:
Rebalance occurs when a new consumer is added, removed or consumer dies or paritions increased.

NEW QUESTION # 56
What are three built-in abstractions in the Kafka Streams DSL?
(Select three.)

A. GlobalKTable
B. KStream
C. GlobalKStream
D. KTable
E. StreamTable

Answer: A,B,D

Explanation:
The Kafka Streams DSL provides three primary abstractions:
KStream: Represents an unbounded stream of records (append-only).
KTable: Represents a changelog stream where each key has the latest value (like a materialized view).
GlobalKTable: A fully replicated version of KTable across all stream instances, enabling global joins.
From Kafka Streams Developer Guide:
"Kafka Streams provides three core abstractions: KStream, KTable, and GlobalKTable, each serving different purposes in stream processing and joins." GlobalKStream and StreamTable do not exist in Kafka's API.
Reference: Kafka Streams Concepts > KStream, KTable, GlobalKTable

NEW QUESTION # 57
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