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The CCDAK certification is offered by Confluent, the company behind Apache Kafka. Confluent Certified Developer for Apache Kafka Certification Examination certification provides developers with a way to demonstrate their expertise in Kafka development and gain recognition from potential employers. The CCDAK Certification is also a requirement for becoming a Confluent Certified Developer or Confluent Certified Administrator.
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The Confluent Certified Developer for Apache Kafka Certification Examination certification is provided by Confluent, the company behind the Apache Kafka project, and requires passing an online exam that tests the core concepts, implementation, and management of data streaming applications using Apache Kafka. Confluent Certified Developer for Apache Kafka Certification Examination certification test is designed to challenge the candidates' knowledge of concepts, programming languages, and tools that are commonly used to build data streaming applications. Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q68-Q73):NEW QUESTION # 68
(You deploy a Kafka Streams application with five application instances.
Kafka Streams stores application metadata using internal topics.
Auto-topic creation is disabled in the Kafka cluster.
Which statement about this scenario is true?)
A. The application will be on hold until internal topics are created manually.
B. The application will continue to work and internal topics will be created, even if auto-topic creation is disabled.
C. The application will work, but application metadata will not be stored.
D. The application will terminate with a non-retriable exception.
Answer: D
Explanation:
According to the official Apache Kafka Streams documentation, Kafka Streams relies on internal topics (such as changelog topics and repartition topics) to store state, task assignments, and application metadata. These topics are essential for fault tolerance, rebalancing, and state recovery.
When auto-topic creation is disabled, Kafka Streams cannot automatically create the required internal topics unless they already exist. In this situation, the Streams application will fail during startup with a fatal, non- retriable exception, indicating that the necessary internal topics could not be created.
Kafka Streams does not pause or wait for manual topic creation, nor does it operate without storing metadata.
Instead, it explicitly requires these topics to exist or be creatable. While administrators can manually pre- create the required internal topics with the correct configuration, failure to do so causes the application to terminate.
Therefore, the correct and documented behavior is that the Kafka Streams application terminates with a non- retriable exception when auto-topic creation is disabled and required internal topics are missing.
NEW QUESTION # 69
You are managing the schema of data in a Kafka Topic using Schema Registry. You need to add new fields to the message schema. You need to select a compatibility type that allows you to add required fields, delete optional fields, and allows consumers to read all previous versions of the schema.
Which compatibility type is correct?
A. FULL_TRANSITIVE
B. FORWARD_TRANSITIVE
C. BACKWARD
D. FORWARD
Answer: A
NEW QUESTION # 70
(You want to enrich the content of a topic by joining it with key records from a second topic.
The two topics have a different number of partitions.
Which two solutions can you use?
Select two.)
A. Create as many Kafka Streams application instances as the maximum number of partitions of the two topics and use a KStream-KTable join.
B. Use a KStream-KTable join; Kafka Streams will automatically repartition the topics to satisfy the co- partitioning constraint.
C. Repartition one topic to a new topic with the same number of partitions as the other topic (co- partitioning constraint) and use a KStream-KTable join.
D. Use a GlobalKTable for one of the topics where data does not change frequently and use a KStream- GlobalKTable join.
Answer: C,D
Explanation:
The Apache Kafka Streams documentation defines a co-partitioning requirement for KStream-KTable and KStream-KStream joins. Both input topics must have the same number of partitions and the same key partitioning strategy.
One valid solution is to use a GlobalKTable (Option A). A GlobalKTable is fully replicated to every Kafka Streams instance, removing the co-partitioning requirement. This approach is recommended when the reference data is relatively small and changes infrequently.
Another valid solution is to repartition one topic so that both topics have the same number of partitions (Option B). Kafka Streams provides repartition topics specifically for this purpose, allowing proper KStream- KTable joins.
Option C does not resolve the partition mismatch, as increasing instances does not change partitioning. Option D is incorrect because Kafka Streams does not automatically repartition both topics for joins; repartitioning must be explicitly configured.
Therefore, the correct and officially supported solutions are using a GlobalKTable and explicitly repartitioning one topic.
NEW QUESTION # 71
Which actions will trigger partition rebalance for a consumer group? (select three)
A. Add a new consumer to consumer group
B. Remove a broker from the cluster
C. Increase partitions of a topic
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 # 72
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?
A. EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.
B. Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.
C. Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.
D. EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.
Answer: D
Explanation:
Kafka Streams usestransactional producersto guaranteeexactly-once semantics (EOS). This ensures that both theoutput recordsandstate store updatesare committed atomically, avoiding duplication or partial writes.
FromKafka Streams Documentation > Processing Guarantees:
"Kafka Streams leveragesKafka's transactional APIsto commit the output records and internal state updates as a single atomic unit, thereby providing exactly-once semantics."
* Option A is incorrect because log compaction is not disabled for EOS.
* Option C incorrectly describes a checkpointing system Kafka Streams does not use.
* Option D refers to deduplication, which is not how EOS is achieved in Streams.
Reference:Kafka Streams Processing Guarantees