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Confluent CCDAK Exam Syllabus Topics:

SectionObjectives
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 Producers and Consumers- Consumer API
          • 1. Consumer groups
            • 2. Rebalancing and offset commits
              - Producer API
              • 1. Reliability and acknowledgments
                • 2. Serialization and partitioning strategies
                  Kafka Ecosystem and Confluent Platform- Schema Registry
                  • 1. Avro, Protobuf, JSON Schema usage
                    - ksqlDB and connectors
                    • 1. Kafka Connect basics
                      • 2. Stream querying concepts
                        Kafka Streams and Processing- Kafka Streams API
                        • 1. Stream processing fundamentals
                          • 2. Stateful vs stateless processing
                            - Event processing patterns
                            • 1. Exactly-once semantics
                              • 2. Windowing and aggregation

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q62-Q67):

                                NEW QUESTION # 62
                                What's a Kafka partition made of?

                                Answer: C

                                Explanation:
                                Kafka partitions are made of segments (usually each segment is 1GB), and each segment has two corresponding indexes (offset index and time index)


                                NEW QUESTION # 63
                                A consumer wants to read messages from partitions 0 and 1 of a topic topic1. Code snippet is shown below.
                                consumer.subscribe(Arrays.asList("topic1"));
                                List<TopicPartition> pc = new ArrayList<>();
                                pc.add(new PartitionTopic("topic1", 0));
                                pc.add(new PartitionTopic("topic1", 1));
                                consumer.assign(pc);

                                Answer: B

                                Explanation:
                                subscribe() and assign() cannot be called by the same consumer, subscribe() is used to leverage the consumer group mechanism, while assign() is used to manually control partition assignment and reads assignment


                                NEW QUESTION # 64
                                (A stream processing application tracks user activity in online shopping carts, including items added, removed, and ordered throughout the day for each user.
                                You need to capture data to identify possible periods of user inactivity.
                                Which type of Kafka Streams window should you use?)

                                Answer: B

                                Explanation:
                                According to the official Apache Kafka Streams documentation, session windows are specifically designed to model periods of activity separated by gaps of inactivity. A session window groups events that occur close together in time and closes when no new events arrive within a defined inactivity gap.
                                In this use case, tracking user shopping cart behavior requires detecting when a user becomes inactive (for example, stops adding or removing items). Session windows naturally represent this pattern by creating a new window when activity resumes after a period of silence. This makes them ideal for modeling user sessions, cart abandonment detection, and inactivity analysis.
                                Hopping and tumbling windows (Options B and C) are fixed-time windows and are not well-suited for detecting inactivity, as they segment data based on clock time rather than behavior. Sliding windows are primarily used for continuous joins and do not explicitly model inactivity gaps.
                                Therefore, session windows are the correct and officially recommended choice for identifying periods of user inactivity in Kafka Streams applications.


                                NEW QUESTION # 65
                                What are two examples of performance metrics?
                                (Select two.)

                                Answer: A,C

                                Explanation:
                                * fetch-rate: Measures how frequently consumers fetch data - critical for monitoring throughput.
                                * incoming-byte-rate: Shows how much data is being written to the broker per second - a key producer-side metric.
                                FromKafka JMX Metrics Guide:
                                "fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers."
                                * B, C, E, and F areapplication-layer business metrics, not Kafka performance metrics.
                                Reference:Kafka Monitoring > JMX Metrics


                                NEW QUESTION # 66
                                Select the Kafka Streams joins that are always windowed joins.

                                Answer: A

                                Explanation:
                                Seehttps://docs.confluent.io/current/streams/developer-guide/dsl-api.html#joining


                                NEW QUESTION # 67
                                ......

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