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Confluent CCDAK (Confluent Certified Developer for Apache Kafka) certification exam is designed for software developers who want to demonstrate their proficiency in building Kafka-based applications. Kafka is an open-source distributed messaging system that allows applications to exchange messages in real-time. It offers a highly scalable, fault-tolerant, and reliable platform for building large-scale distributed systems. The CCDAK Exam Tests the candidate's ability to work with Kafka and its ecosystem components e.g., producer, consumer, connectors, and streams API.

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Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) is an exam designed for professionals seeking to validate their knowledge and skills in Apache Kafka. CCDAK exam is administered by Confluent, a leading provider of enterprise data streaming platforms, and is intended for developers who have experience with Kafka and want to demonstrate their proficiency in the technology. The CCDAK exam is a crucial step for professionals who want to pursue a career in data streaming and messaging, and it is highly regarded in the industry.

Confluent, the company that created Kafka, offers training and certification programs for professionals who want to learn and demonstrate their expertise in Kafka. The CCDAK Certification Exam is one of the many programs offered by Confluent. With the growing popularity of Kafka among enterprises, the CCDAK certification can help professionals advance their careers in big data and real-time processing.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q69-Q74):

NEW QUESTION # 69
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?

Answer: D

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 # 70
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?

producer.send(record, new MyCallback(record));

Answer: B


NEW QUESTION # 71
You are writing to a topic with acks=all.
The producer receives acknowledgments but you notice duplicate messages.
You find that timeouts due to network delay are causing resends.
Which configuration should you use to prevent duplicates?

Answer: D

Explanation:
To ensure exactly-once delivery and avoid duplicates even during retries:
enable.idempotence=true ensures deduplication on the broker
retries=2147483647 allows unlimited retries on retriable errors
max.in.flight.requests.per.connection=5 is the maximum value that preserves message order with idempotence From Kafka Producer Config Docs:
"To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection #
5."
A is unrelated (consumer-side)
C disables retries
D disables idempotence, leading to duplicates
Reference: Kafka Producer Configs > enable.idempotence, retries


NEW QUESTION # 72
A consumer has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to
2311. The consumer group never committed offsets for the topic before. Where will the consumer read from?

Answer: A

Explanation:
Latest means that data retrievals will start from where the offsets currently end


NEW QUESTION # 73
You need to explain the best reason to implement the consumer callback interface ConsumerRebalanceListener prior to a Consumer Group Rebalance.
Which statement is correct?

Answer: A

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 # 74
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