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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 Ecosystem and Confluent Platform- ksqlDB and connectors
          • 1. Stream querying concepts
            • 2. Kafka Connect basics
              - Schema Registry
              • 1. Avro, Protobuf, JSON Schema usage
                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
                        Kafka Producers and Consumers- Consumer API
                        • 1. Rebalancing and offset commits
                          • 2. Consumer groups
                            - 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 (Q90-Q95):

                                NEW QUESTION # 90
                                Where are the ACLs stored in a Kafka cluster by default?

                                Answer: D

                                Explanation:
                                ACLs are stored in Zookeeper node /kafka-acls/ by default.


                                NEW QUESTION # 91
                                What will happen if a producer tries to send messages to a topic that does not exist in the Kafka cluster?

                                Answer: D


                                NEW QUESTION # 92
                                (You are experiencing low throughput from a Java producer.
                                Kafka producer metrics show a low I/O thread ratio and low I/O thread wait ratio.
                                What is the most likely cause of the slow producer performance?)

                                Answer: D

                                Explanation:
                                According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer's network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.
                                This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.
                                An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer's sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.
                                Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.


                                NEW QUESTION # 93
                                A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
                                You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.

                                Answer: B

                                Explanation:
                                In Kafka Streams,the number of stream tasks = number of input partitions × num.stream.threads × number of instances,but only as many as the number of partitions can actively consume at once.
                                However, in this case,Kafka Streams assigns one task per partition, and because there are 5 partitions and
                                15 threads (3 instances × 5 threads),15 tasks are created, andall 15 can be activedepending on processing topology.
                                FromKafka Streams Developer Guide:
                                "Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance." So,15 stream tasks will be created and 15 will be actively consuming.
                                Reference:Apache Kafka Streams Documentation > Concepts > Tasks and Threads


                                NEW QUESTION # 94
                                You need to set alerts on key broker metrics to trigger notifications when the cluster is unhealthy.
                                Which are three minimum broker metrics to monitor?
                                (Select three.)

                                Answer: A,B,C

                                Explanation:
                                These three metrics are critical for cluster health:
                                OfflinePartitionsCount: Indicates partitions without active leaders - a sign of broker failure.
                                ActiveControllerCount: There should be exactly one active controller. A count # 1 signals controller failure.
                                UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected - risky for data loss.
                                From Kafka Monitoring Documentation:
                                "Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running." A is about topics pending deletion - not critical.
                                E is a per-topic record metric, not broker-level.
                                Reference: Kafka Monitoring > Key JMX Metrics


                                NEW QUESTION # 95
                                ......

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