100% Pass Quiz Confluent - CCDAK–Efficient Test Dates
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Our Confluent CCDAK exam questions are designed to provide you with the most realistic CCDAK Exam experience possible. Each question is accompanied by an accurate answer, prepared by our team of experts. We also offer free Confluent CCDAK Exam Questions updates for 1 year after purchase, as well as a free CCDAK practice exam questions demo before purchase.
The Confluent CCDAK exam consists of 60 multiple-choice questions that must be completed within 90 minutes. The questions are designed to test the developer's knowledge of Kafka concepts and their ability to apply them in real-world scenarios. CCDAK exam is available online and can be taken from anywhere in the world. The cost of the exam is $300, and developers who pass will receive a digital badge and certificate that they can use to showcase their certification.
Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam is a hands-on exam that tests developers' ability to work with Kafka and Confluent's platform. CCDAK Exam covers a wide range of topics related to Kafka and Confluent's tools, and passing the exam demonstrates a developer's ability to work with Kafka and Confluent's platform at a high level. The CCDAK Certification Exam is an important certification for developers who want to work with Kafka and Confluent's platform, and it can help advance their careers and increase their earning potential.
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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q62-Q67):
NEW QUESTION # 62
You need to configure a sink connector to write records that fail into a dead letter queue topic.
Requirements:
* Topic name: DLQ-Topic
* Headers containing error context must be added to the messagesWhich three configuration parameters are necessary?(Select three.)
- A. errors.tolerance=none
- B. errors.tolerance=all
- C. errors.deadletterqueue.topic.name=DLQ-Topic
- D. errors.log.include.messages=true
- E. errors.log.enable=true
- F. errors.deadletterqueue.context.headers.enable=true
Answer: B,C,F
Explanation:
To send failed records to adead letter queue (DLQ), you must configure:
* errors.tolerance=all: Tells the connector tonot failon errors but handle them (e.g., send to DLQ).
* errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.
* errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.
FromKafka Connect Error Handling Docs:
"Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs.
Headers can include failure metadata."
Options D, E, F are related tologging, not DLQ behavior.
Reference:Kafka Connect Configurations > Error Handling
NEW QUESTION # 63
If I want to have an extremely high confidence that leaders and replicas have my data, I should use
- A. acks=1, replication factor=3, min.insync.replicas=2
- B. acks=all, replication factor=3, min.insync.replicas=1
- C. acks=all, replication factor=2, min.insync.replicas=1
- D. acks=all, replication factor=3, min.insync.replicas=2
Answer: D
Explanation:
acks=all means the leader will wait for all in-sync replicas to acknowledge the record. Also the min in-sync replica setting specifies the minimum number of replicas that need to be in-sync for the partition to remain available for writes.
NEW QUESTION # 64
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?
- A. EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.
- B. Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.
- C. EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.
- D. Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.
Answer: A
Explanation:
Kafka Streams uses transactional producers to guarantee exactly-once semantics (EOS). This ensures that both the output records and state store updates are committed atomically, avoiding duplication or partial writes.
From Kafka Streams Documentation > Processing Guarantees:
"Kafka Streams leverages Kafka's transactional APIs to 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
NEW QUESTION # 65
(You are writing lightweight XML messages to a Kafka topic named userinfo.
Which format should you use for the value field?)
- A. ByteSerializer
- B. VoidSerializer
- C. XmlSerializer
- D. StringSerializer
Answer: D
Explanation:
Apache Kafka does not impose any schema or message format; it treats records as byte arrays on the wire.
Serialization is therefore the responsibility of the producer. For lightweight XML messages, the most common and recommended approach is to represent the XML as a UTF-8 encoded string and use Kafka's built-in StringSerializer.
The official Kafka producer documentation provides StringSerializer as the standard serializer for textual data formats, including XML and JSON. This serializer converts Java String objects into byte arrays using UTF-8 encoding, making it simple, efficient, and interoperable with a wide range of consumers.
Option A is incorrect because Kafka does not provide a built-in XmlSerializer. Option C (ByteSerializer) could technically work, but it would require manually converting the XML string to a byte array, which adds unnecessary complexity for lightweight XML payloads. Option D (VoidSerializer) is used only when no value is sent and is not applicable here.
Therefore, using StringSerializer for the value field is the correct and officially supported approach for sending lightweight XML messages in Kafka.
NEW QUESTION # 66
In Kafka, every broker... (select three)
- A. is a controller
- B. contains only a subset of the topics and the partitions
- C. contains all the topics and all the partitions
- D. is a bootstrap broker
- E. knows all the metadata for all topics and partitions
- F. knows the metadata for the topics and partitions it has on its disk
Answer: B,D,E
Explanation:
Kafka topics are divided into partitions and spread across brokers. Each brokers knows about all the metadata and each broker is a bootstrap broker, but only one of them is elected controller
NEW QUESTION # 67
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