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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 Producers and Consumers- Producer API
          • 1. Serialization and partitioning strategies
            • 2. Reliability and acknowledgments
              - Consumer API
              • 1. Rebalancing and offset commits
                • 2. Consumer groups
                  Topic 3: 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 4: Kafka Streams and Processing- Event processing patterns
                        • 1. Windowing and aggregation
                          • 2. Exactly-once semantics
                            - Kafka Streams API
                            • 1. Stream processing fundamentals
                              • 2. Stateful vs stateless processing

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q57-Q62):

                                NEW QUESTION # 57
                                To prevent network-induced duplicates when producing to Kafka, I should use

                                Answer: A

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


                                NEW QUESTION # 58
                                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: B

                                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/what-i-have-learned-from-kafka-partition-assignment-strategy-799fdf15d3ab


                                NEW QUESTION # 59
                                (You are building real-time streaming applications using Kafka Streams.
                                Your application has a custom transformation.
                                You need to define custom processors in Kafka Streams.
                                Which tool should you use?)

                                Answer: B

                                Explanation:
                                The Apache Kafka Streams documentation clearly distinguishes between the Kafka Streams DSL and the Processor API. While the DSL is designed for common stream processing operations such as filtering, mapping, joining, and aggregations, it does not support fully custom processing logic.
                                For use cases that require custom transformations, fine-grained control over record processing, access to headers, timestamps, state stores, and punctuation, Kafka Streams provides the Processor API. This API allows developers to implement custom Processor, Transformer, or ValueTransformer classes and explicitly define the processing topology.
                                Option A (TopologyTestDriver) is a testing utility, not a development API. Option C (DSL) is higher-level and not suitable for advanced custom logic. Option D does not exist in Kafka.
                                Therefore, the correct and officially supported approach for defining custom processors in Kafka Streams is to use the Processor API.


                                NEW QUESTION # 60
                                You want to connect with username and password to a secured Kafka cluster that has SSL encryption.
                                Which properties must your client include?

                                Answer: B

                                Explanation:
                                ForSASL/PLAIN authentication over SSL, the correct combination is:
                                * security.protocol=SASL_SSL
                                * sasl.jaas.config=...PlainLoginModule required username=... password=...
                                FromKafka Security Documentation:
                                "For SASL/PLAIN over SSL, configure security.protocol=SASL_SSL and provide login module configuration using sasl.jaas.config." Options B and D are incorrect because they do not pair the correct protocol with the authentication method.
                                Reference:Apache Kafka Security Configs > SASL_SSL, PLAIN mechanism


                                NEW QUESTION # 61
                                Your application is consuming from a topic with one consumer group.
                                The number of running consumers is equal to the number of partitions.
                                Application logs show that some consumers are leaving the consumer group during peak time, triggering a rebalance. You also notice that your application is processing many duplicates.
                                You need to stop consumers from leaving the consumer group.
                                What should you do?

                                Answer: C

                                Explanation:
                                If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.
                                FromKafka Docs > Heartbeats and Failures:
                                "If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance." Reducing max.poll.records mightslow consumption further. More consumers won't help since each partition already has a dedicated consumer.
                                Reference:Apache Kafka Consumer Configs > session.timeout.ms


                                NEW QUESTION # 62
                                ......

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