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Confluent CCDAK Exam Syllabus Topics:

SectionObjectives
Kafka Streams and Processing- Kafka Streams API
  • 1. Stateful vs stateless processing
    • 2. Stream processing fundamentals
      - Event processing patterns
      • 1. Windowing and aggregation
        • 2. Exactly-once semantics
          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 Fundamentals- Kafka architecture and core concepts
                • 1. Brokers, topics, partitions
                  • 2. Replication and fault tolerance
                    - Data flow in Kafka
                    • 1. Offset management
                      • 2. Producer and consumer model
                        Kafka Producers and Consumers- Producer API
                        • 1. Reliability and acknowledgments
                          • 2. Serialization and partitioning strategies
                            - Consumer API
                            • 1. Consumer groups
                              • 2. Rebalancing and offset commits

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                                Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q82-Q87):

                                NEW QUESTION # 82
                                (You are writing a producer application and need to ensure proper delivery.
                                You configure the producer with acks=all.
                                Which two actions should you take to ensure proper error handling?
                                Select two.)

                                Answer: A,B

                                Explanation:
                                The Apache Kafka producer API is asynchronous by default, and proper error handling requires explicit mechanisms. The official documentation recommends using a callback function with producer.send() to determine whether a record was successfully acknowledged or failed due to an exception. The callback provides access to both RecordMetadata and any exception encountered during sending.
                                Additionally, certain errors-such as serialization errors or configuration issues-are thrown synchronously when calling producer.send(). These must be handled using a try/catch block that catches KafkaException or its subclasses.
                                Option A is invalid because ProducerRecord has no status() method. Option C is incorrect because producer.
                                send() returns a Future<RecordMetadata>, not a RecordMetadata object directly.
                                Thus, combining a callback for asynchronous errors and a try/catch block for synchronous errors is the correct and officially documented approach to ensure reliable producer error handling when using acks=all.


                                NEW QUESTION # 83
                                What is true about replicas ?

                                Answer: B

                                Explanation:
                                Replicas are passive - they don't handle produce or consume request. Produce and consume requests get sent to the node hosting partition leader.


                                NEW QUESTION # 84
                                You are experiencing low throughput from a Java producer. You monitor the Kafka metrics and notice that the metrics for that producer show low I/O thread ratio and low I/O thread wait ratio.
                                What is most likely the cause of the slow producer performance?

                                Answer: D


                                NEW QUESTION # 85
                                You are running a Kafka Streams application in a Docker container managed by Kubernetes, and upon application restart, it takes a long time for the docker container to replicate the state and get back to processing the dat a. How can you improve dramatically the application restart?

                                Answer: A

                                Explanation:
                                Although any Kafka Streams application is stateless as the state is stored in Kafka, it can take a while and lots of resources to recover the state from Kafka. In order to speed up recovery, it is advised to store the Kafka Streams state on a persistent volume, so that only the missing part of the state needs to be recovered.


                                NEW QUESTION # 86
                                What exceptions may be caught by the following producer? (select two)
                                ProducerRecord<String, String> record =
                                new ProducerRecord<>("topic1", "key1", "value1");
                                try {
                                producer.send(record);
                                } catch (Exception e) {
                                e.printStackTrace();
                                }

                                Answer: B,D

                                Explanation:
                                These are the client side exceptions that may be encountered before message is sent to the broker, and before a future is returned by the .send() method.


                                NEW QUESTION # 87
                                ......

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