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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. Stream processing fundamentals
                  • 2. Stateful vs stateless processing
                    - Event processing patterns
                    • 1. Windowing and aggregation
                      • 2. Exactly-once semantics
                        Topic 4: 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

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q31-Q36):

                                NEW QUESTION # 31
                                You have a Kafka cluster and all the topics have a replication factor of 3. One intern at your company stopped a broker, and accidentally deleted all the data of that broker on the disk. What will happen if the broker is restarted?

                                Answer: D

                                Explanation:
                                Kafka replication mechanism makes it resilient to the scenarios where the broker lose data on disk, but can recover from replicating from other brokers. This makes Kafka amazing!


                                NEW QUESTION # 32
                                What isn't an internal Kafka Connect topic?

                                Answer: B

                                Explanation:
                                connect-configs stores configurations, connect-status helps to elect leaders for connect, and connect-offsets store source offsets for source connectors


                                NEW QUESTION # 33
                                What will happen if a producer tries to send messages to a topic that does not exist in the Kafka cluster?

                                Answer: A


                                NEW QUESTION # 34
                                How do you create a topic named test with 3 partitions and 3 replicas using the Kafka CLI?

                                Answer: A

                                Explanation:
                                As of Kafka 2.3, the kafka-topics.sh command can take --bootstrap-server localhost:9092 as an argument.
                                You could also use the (now deprecated) option of --zookeeper localhost:2181.


                                NEW QUESTION # 35
                                You have a topic with four partitions. The application reads from it using two consumers in a single consumer group.
                                Processing is CPU-bound, and lag is increasing.
                                What should you do?

                                Answer: A

                                Explanation:
                                If the application is CPU-bound and lagging, adding more consumers to the group will allow better parallel processing, especially since the topic has 4 partitions, allowing up to 4 active consumers.
                                From Kafka Consumer Group Docs:
                                "Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized." B may help but requires repartitioning and coordination.
                                C or D affects how much data is polled, not how fast it's processed.
                                Reference: Kafka Consumer Concepts > Parallelism and Scaling


                                NEW QUESTION # 36
                                ......

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