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Confluent CCDAK Exam Syllabus Topics:

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

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q72-Q77):

                                NEW QUESTION # 72
                                (You are designing a stream pipeline to monitor the real-time location of GPS trackers, where historical location data is not required.
                                Each event has:
                                * Key: trackerId
                                * Value: latitude, longitude
                                You need to ensure that the latest location for each tracker is always retained in the Kafka topic.
                                Which topic configuration parameter should you set?)

                                Answer: A

                                Explanation:
                                According to the official Apache Kafka documentation, log compaction is the mechanism used to retain only the latest record for each key in a topic. By setting cleanup.policy=compact, Kafka ensures that older records with the same key are eventually removed, leaving only the most recent value for each key.
                                This behavior is exactly suited for use cases such as tracking the current state of an entity, including GPS tracker locations, user profiles, or configuration data. In this scenario, the key (trackerId) uniquely identifies a tracker, and the value represents its most recent latitude and longitude.
                                Option B (retention.ms=infinite) retains all historical data, which contradicts the requirement. Option C (min.
                                cleanable.dirty.ratio) controls when compaction runs, not what data is retained. Option D (retention.ms=0) would immediately delete all data and is unsafe.
                                Therefore, setting cleanup.policy=compact is the correct and officially documented solution to always retain the latest location per tracker.


                                NEW QUESTION # 73
                                What is returned by a producer.send() call in the Java API?

                                Answer: A

                                Explanation:
                                Seehttps://kafka.apache.org/21/javadoc/org/apache/kafka/clients/producer/KafkaProducer.html


                                NEW QUESTION # 74
                                A Zookeeper ensemble contains 5 servers. What is the maximum number of servers that can go missing and the ensemble still run?

                                Answer: C

                                Explanation:
                                majority consists of 3 zk nodes for 5 nodes zk cluster, so 2 can fail


                                NEW QUESTION # 75
                                How do Kafka brokers ensure great performance between the producers and consumers? (select two)

                                Answer: A,E

                                Explanation:
                                Kafka transfers data with zero-copy and sends the raw bytes it receives from the producer straight to the consumer, leveraging the RAM available as page cache


                                NEW QUESTION # 76
                                To get acknowledgement of writes to only the leader partition, we need to use the config...

                                Answer: B

                                Explanation:
                                Producers can set acks=1 to get acknowledgement from partition leader only.


                                NEW QUESTION # 77
                                ......

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