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Confluent CCDAK Exam Syllabus Topics:

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

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q84-Q89):

                                NEW QUESTION # 84
                                (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 # 85
                                Once sent to a topic, a message can be modified

                                Answer: A

                                Explanation:
                                Kafka logs are append-only and the data is immutable


                                NEW QUESTION # 86
                                How will you read all the messages from a topic in your KSQL query?

                                Answer: A

                                Explanation:
                                Consumers can set auto.offset.reset property to earliest to start consuming from beginning. For KSQL, SET
                                'auto.offset.reset'='earliest';


                                NEW QUESTION # 87
                                Which of the following event processing application is stateless? (select two)

                                Answer: C,D

                                Explanation:
                                Stateless means processing of each message depends only on the message, so converting from JSON to Avro or filtering a stream are both stateless operations


                                NEW QUESTION # 88
                                You need to collect logs from a host and write them to a Kafka topic named 'logs-topic'. You decide to use Kafka Connect File Source connector for this task.
                                What is the preferred deployment mode for this connector?

                                Answer: D

                                Explanation:
                                Kafka Connect can run in standalone mode or distributed mode. For simple tasks like reading logs from a file on a single host, standalone mode is recommended.
                                From Kafka Connect User Guide:
                                "Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file)." Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.
                                Reference: Kafka Connect User Guide > Deployment Modes


                                NEW QUESTION # 89
                                ......

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