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Confluent CCDAK Exam Syllabus Topics:

SectionObjectives
Topic 1: Kafka Fundamentals- Kafka architecture and core concepts
  • 1. Replication and fault tolerance
    • 2. Brokers, topics, partitions
      - Data flow in Kafka
      • 1. Offset management
        • 2. Producer and consumer model
          Topic 2: Kafka Ecosystem and Confluent Platform- Schema Registry
          • 1. Avro, Protobuf, JSON Schema usage
            - ksqlDB and connectors
            • 1. Stream querying concepts
              • 2. Kafka Connect basics
                Topic 3: Kafka Streams and Processing- Kafka Streams API
                • 1. Stateful vs stateless processing
                  • 2. Stream processing fundamentals
                    - Event processing patterns
                    • 1. Exactly-once semantics
                      • 2. Windowing and aggregation
                        Topic 4: Kafka Producers and Consumers- Producer API
                        • 1. Reliability and acknowledgments
                          • 2. Serialization and partitioning strategies
                            - Consumer API
                            • 1. Consumer groups
                              • 2. Rebalancing and offset commits

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q85-Q90):

                                NEW QUESTION # 85
                                (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.)

                                Answer: A,B

                                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 # 86
                                (You are writing lightweight XML messages to a Kafka topic named userinfo.
                                Which format should you use for the value field?)

                                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 # 87
                                You are working on an Orders microservice Thai is publishing messages lo an Orders topic. II is a business requirement that the Orders for a given customer are processed sequentially. Also, the Orders topic is partitioned tor scalability.
                                Which factors would you need to address during application development?

                                Answer: B


                                NEW QUESTION # 88
                                Which actions will trigger partition rebalance for a consumer group? (select three)

                                Answer: B,C,D

                                Explanation:
                                Rebalance occurs when a new consumer is added, removed or consumer dies or paritions increased.


                                NEW QUESTION # 89
                                The kafka-console-consumer CLI, when used with the default options

                                Answer: A

                                Explanation:
                                If a group is not specified, the kafka-console-consumer generates a random consumer group.


                                NEW QUESTION # 90
                                ......

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