Valid CCDAK Test Objectives | CCDAK Valid Study Guide

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The Confluent CCDAK Exam covers essential concepts such as Kafka clusters and components, key Kafka APIs, Kafka Connect and Streams API, Kafka Security, and Kafka Monitoring. It also evaluates candidates on their ability to configure and tune various Kafka components, build data streams processing applications using Kafka Streams API, and integrate Kafka with other data systems.

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CCDAK Valid Study Guide & CCDAK Free Exam Questions

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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.

The CCDAK Certification Exam is offered by Confluent, the company that created Apache Kafka. CCDAK exam is designed to be rigorous and challenging, and passing it requires a deep understanding of Kafka-based solutions and their application to real-world use cases. The CCDAK certification is a valuable credential for developers who work with Kafka and want to demonstrate their expertise to potential employers or clients.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q61-Q66):

NEW QUESTION # 61
You have a Kafka client application that has real-time processing requirements.
What are the most important metrics available in Kafka that you should monitor?

A. Total time to serve requests to replica followers
B. Aggregate incoming byte rate
C. Consumer lag between the brokers and the consumers
D. Consumer heartbeat rate to the group coordinator

Answer: C

NEW QUESTION # 62
Which statement describes the storage location for a sink connector's offsets?

A. The __consumer_offsets topic, like any other consumer
B. In a file specified by the offset.storage.file.filename configuration parameter
C. The topic specified in the offsets.storage.topic configuration parameter
D. In memory which is then periodically flushed to a RocksDB instance

Answer: A

Explanation:
Kafka Connect sink connectors use the standard Kafka consumer mechanism to track offsets, which means offsets are stored in the __consumer_offsets internal topic.
From Kafka Connect Documentation:
"Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer." The other options refer to source connectors or worker configuration.
offsets.storage.topic is relevant for source connectors.
Reference: Kafka Connect Concepts > Sink Connectors and Offset Storage

NEW QUESTION # 63
What is a consequence of increasing the number of partitions in an existing Kafka topic?

A. Consumers will need to process data from more partitions which will significantly increase consumer lag.
B. Records with the same key could be located in different partitions.
C. Existing data will be redistributed across the new number of partitions temporarily increasing cluster load.
D. The acknowledgment process will increase latency for producers using acks=all.

Answer: A

Explanation:
Increasing partitions increases parallelism, but also means:
Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.
This can result in increased lag, especially under high load.
From Kafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate." A is false: existing data is not redistributed.
B is false: records with the same key always map to the same partition based on hash.
D is not directly impacted by the partition count.
Reference: Kafka Topic Management > Adding Partitions

NEW QUESTION # 64
Select all the way for one consumer to subscribe simultaneously to the following topics - topic.history, topic.sports, topic.politics? (select two)

A. consumer.subscribe(Arrays.asList("topic.history", "topic.sports", "topic.politics"));
B. consumer.subscribe(Pattern.compile("topic\..*"));
C. consumer.subscribePrefix("topic.");
D. consumer.subscribe("topic.history"); consumer.subscribe("topic.sports"); consumer.subscribe("topic.politics");

Answer: A,B

Explanation:
Multiple topics can be passed as a list or regex pattern.

NEW QUESTION # 65
In Avro, adding an element to an enum without a default is a __ schema evolution

A. full
B. backward
C. forward
D. breaking

Answer: D

Explanation:
Since Confluent 5.4.0, Avro 1.9.1 is used. Since default value was added to enum complex type , the schema resolution changed from:
(<1.9.1) if both are enums:** if the writer's symbol is not present in the reader's enum, then an error is signalled. **(>=1.9.1) if both are enums:
if the writer's symbol is not present in the reader's enum and the reader has a default value, then that value is used, otherwise an error is signalled.

NEW QUESTION # 66
......

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