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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Application Testing	8%	- Unit testing producers and consumers - Integration testing and test clusters - Mocking and test utilities
Topic 2: Apache Kafka Streams	12%	- Error handling and state management - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation - Windowing and time semantics
Topic 3: Application Observability	13%	- Logging and monitoring metrics - Tracing and performance tuning - Error diagnosis and troubleshooting
Topic 4: Kafka Connect	15%	- Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns
Topic 5: Apache Kafka Fundamentals	23%	- Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets
Topic 6: Apache Kafka Application Development	28%	- Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling

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Confluent Certified Developer for Apache Kafka Certification Examination 認定 CCDAK 試験問題 (Q63-Q68):

質問 # 63
The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?

A. Its position in the producer's batch of messages
B. The ID of the partition to which the message was committed
C. The sequential ID of the message committed into a partition
D. The number of bytes that overflowed beyond a producer batch of messages

正解：C

解説：
The offset in the RecordMetadata object returned by the producer represents the position of the record in the partition - i.e., the sequential ID assigned by Kafka once the message is committed.
From Kafka Producer API Documentation:
"The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker." D refers to metadata.partition(), not offset().
B and C are unrelated to how Kafka handles committed offsets.
Reference: Kafka Producer Java API > RecordMetadata

質問 # 64
What is true about replicas ?

A. Leader replica handles all produce and consume requests
B. Produce and consume requests are load-balanced between Leader and Follower replicas
C. Produce requests can be done to the replicas that are followers
D. Follower replica handles all consume requests

正解：A

解説：
Replicas are passive - they don't handle produce or consume request. Produce and consume requests get sent to the node hosting partition leader.

質問 # 65
You are sending messages to a Kafka cluster in JSON format and want to add more information related to each message:
* Format of the message payload
* Message creation time
* A globally unique identifier that allows the message to be traced through the systemWhere should this additional information be set?

A. Key
B. Broker
C. Value
D. Header

正解：D

解説：
Kafkamessage headersare the right place to includemetadatasuch as:
* Payload format (e.g., schema version)
* Timestamps (custom)
* Trace or correlation IDs
FromKafka Producer API Docs:
"Headers are a map of key-value pairs that can be used to include metadata alongside the message, without modifying the message body."
* Keys determine partitioning and ordering
* Values are the message payload
* Brokers do not store arbitrary metadata fields
Reference:Kafka ProducerRecord API > Headers

質問 # 66
To prevent network-induced duplicates when producing to Kafka, I should use

A. enable.idempotence=true
B. batch.size=1
C. max.in.flight.requests.per.connection=1
D. retries=200000

正解：A

解説：
Producer idempotence helps prevent the network introduced duplicates. More details herehttps://cwiki.apache.org/confluence/display/KAFKA/Idempotent+Producer

質問 # 67
Which of these joins does not require input topics to be sharing the same number of partitions?

A. KStream-KTable join
B. KTable-KTable join
C. KStream-GlobalKTable
D. KStream-KStream join

正解：C

解説：
GlobalKTables have their datasets replicated on each Kafka Streams instance and therefore no repartitioning is required

質問 # 68
......

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