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Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) Certification Exam is a valuable certification for developers who work with Kafka. CCDAK exam tests the skills and knowledge of developers in various aspects of Kafka, and passing it demonstrates proficiency in working with Kafka. Candidates can prepare for the exam by gaining theoretical knowledge and practical experience, and there are many resources available to help them do so.
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The CCDAK certification exam tests the candidate's knowledge of Kafka's architecture, core concepts, and APIs. CCDAK exam also evaluates the candidate's ability to develop Kafka-based applications and troubleshoot common issues. It is a comprehensive exam that covers all aspects of Kafka technology, from installation and configuration to advanced topics like security and performance tuning.
The CCDAK certification program is designed for developers who are looking to improve their skills and knowledge in Kafka development. Confluent Certified Developer for Apache Kafka Certification Examination certification program provides a comprehensive and structured approach to learning Kafka and its related technologies. By passing the CCDAK Certification Exam, developers can demonstrate their proficiency in Kafka development, which can lead to better job opportunities and higher salaries. The CCDAK certification program is an excellent way for developers to validate their skills and knowledge in Kafka development, and it is a valuable asset for individuals and organizations that use Kafka for data streaming and processing.
NEW QUESTION # 29
(You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)
Answer: A
Explanation:
According to the official Apache Kafka documentation for Kafka Streams, exactly-once semantics (EOS) are controlled using the processing.guarantee configuration. To ensure that each input record is processed once and only once, and that the results are written atomically to the output topic, Kafka Streams provides built-in EOS support.
The correct and recommended configuration is processing.guarantee=exactly_once_v2, which is the default EOS implementation starting from Kafka 3.0. This mode uses Kafka's transactional APIs to atomically commit consumer offsets and produce output records within a single transaction. As documented, this prevents duplicates even in the presence of failures, retries, or application restarts.
Option A is incorrect because log compaction does not guarantee exactly-once processing; it only retains the latest record per key. Option B is insufficient because enable.idempotence alone does not handle consumer offset commits or state store consistency in Kafka Streams. Option C is invalid because no such configuration exists in Kafka Streams.
Therefore, setting processing.guarantee=exactly_once_v2 is the only fully correct and officially supported solution for achieving EOS in Kafka Streams applications.
NEW QUESTION # 30
You want to perform table lookups against a KTable everytime a new record is received from the KStream. What is the output of KStream-KTable join?
Answer: B
Explanation:
Here KStream is being processed to create another KStream.
NEW QUESTION # 31
You have a topic t1 with six partitions. You use Kafka Connect to send data from topic t1 in your Kafka cluster to Amazon S3. Kafka Connect is configured for two tasks.
How many partitions will each task process?
Answer: B
Explanation:
When using Kafka Connect with sink connectors (like S3 Sink),each task is assigned one or more topic partitions. Thetotal number of partitions (6)isevenly distributedacross the available tasks (2). Thus, each task will handle3 partitions.
FromKafka Connect Documentation:
"Kafka Connect divides the topic partitions among available tasks. For example, a topic with six partitions and two tasks results in each task handling three partitions." Reference:Kafka Connect Concepts > Tasks and Partitions
NEW QUESTION # 32
Which two statements are correct when assigning partitions to the consumers in a consumer group using the assign() API?
(Select two.)
Answer: A,C
Explanation:
Using assign() givesmanual controlover partition assignment. In this mode:
* B is true: The applicationchooses partitionsdirectly without involving the group coordinator.
* C is true: Since no group coordination occurs,no rebalancinghappens if a consumer dies.
FromKafka Consumer API Docs:
"assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered."
* A is false: subscribe() and assign() aremutually exclusive.
* D is incorrect: Partitions don't need to be equal across topics.
Reference:Kafka Consumer Java API > assign() and subscribe()
NEW QUESTION # 33
What is not a valid authentication mechanism in Kafka?
Answer: B
Explanation:
Learn more about security herehttps://kafka.apache.org/documentation/#security
NEW QUESTION # 34
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2026 Latest Pass4guide CCDAK PDF Dumps and CCDAK Exam Engine Free Share: https://drive.google.com/open?id=1uoEd1-2gNBXcXItMaB7MlTzi_C4CtwUE