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Title: Confluent CCDAK Actual Exam Dumps | CCDAK Exam Course [Print This Page]
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Title: Confluent CCDAK Actual Exam Dumps | CCDAK Exam Course
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Confluent CCDAK exam is a performance-based exam that tests the developer¡¯s ability to create and manage Kafka clusters, design and develop Kafka producers and consumers, and configure Kafka security. CCDAK exam consists of 60 multiple-choice and multiple-response questions that need to be completed in 90 minutes. CCDAK exam is conducted online and can be taken from anywhere in the world.
The CCDAK Certification Exam is an important certification for developers who want to work with Kafka and Confluent's platform. It demonstrates to employers and clients that a developer has the skills and knowledge necessary to build and manage Kafka-based applications using Confluent's platform. In addition, the certification can help developers advance their careers and increase their earning potential.
To prepare for the CCDAK Certification examination, candidates must have a solid understanding of Kafka's architecture, design principles, and implementation details. They must also have experience working with Kafka in a production environment. Confluent offers a range of training courses and resources that can help candidates prepare for the exam. These courses cover topics such as Kafka architecture, Kafka Streams, Kafka Connect, and Kafka security.
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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q74-Q79):NEW QUESTION # 74
(Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1
Which two statements match the expected behavior?
Select two.)
Answer: A,D
Explanation:
Apache Kafka Connect stores source connector offsets in a Kafka topic defined by the offset.storage.topic configuration. Since this property is explicitly set to connect-offsets, Kafka Connect will commit offsets to that topic, making Option C correct.
The offset.storage.replication.factor controls how many replicas the offsets topic will have. When this value is set to -1, Kafka Connect uses the broker's default replication factor, as documented in the Kafka Connect worker configuration reference. Therefore, Option A is also correct.
Option B is incorrect because Kafka Connect does not use Kafka's internal consumer offsets topic (__consumer_offsets). It always uses the configured offsets topic. Option D is incorrect because offset.flush.
timeout.ms defines how long Connect will wait for offset commits to complete, not how long it waits before attempting a commit. The commit interval itself is controlled by offset.flush.interval.ms (60 seconds in this case).
Thus, the correct statements that match the expected behavior are A and C.

NEW QUESTION # 75
There are two consumers C1 and C2 belonging to the same group G subscribed to topics T1 and T2. Each of the topics has 3 partitions. How will the partitions be assigned to consumers with Partition Assigner being Round Robin Assigner?
Answer: A
Explanation:
The correct option is the only one where the two consumers share an equal number of partitions amongst the two topics of three partitions. An interesting article to read ishttps://medium.com/@anyili0928/w ... rategy-799fdf15d3ab

NEW QUESTION # 76
You create a topic named loT-Data with 10 partitions and replication factor of three.
A producer sends 1 MB messages compressed with Gzip.
Which two statements are true in this scenario?
(Select two.)
Answer: A,B
Explanation:
* A:Kafka batches storecompression metadata in their headers, indicating how the batch was compressed.
* B:Compression is controlled via theproducer config compression.type, which defaults to none.
Producers explicitly set Gzip or other formats.
FromKafka Producer Docs:
"Compression is done by the producer, and the type is stored in the message batch metadata. The broker does not recompress."
* C is false: messages are serializedbeforecompression.
* D is false: messages without keys are distributed acrossmultiple partitions.
Reference:Kafka Producer Configs > compression.type

NEW QUESTION # 77
An application is writing AVRO messages using Schema Registry to topic t1. During this process, the Schema Registry becomes unavailable for a few seconds.
What is the expected impact to the application?
Answer: B

NEW QUESTION # 78
If I produce to a topic that does not exist, and the broker setting auto.create.topic.enable=true, what will happen?
Answer: A
Explanation:
The broker settings comes into play when a topic is auto created

NEW QUESTION # 79
......
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