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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. Rebalancing and offset commits
        • 2. Consumer groups
          Kafka Fundamentals- Kafka architecture and core concepts
          • 1. Brokers, topics, partitions
            • 2. Replication and fault tolerance
              - 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. Stateful vs stateless processing
                          • 2. Stream processing fundamentals
                            - 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 (Q38-Q43):

                                NEW QUESTION # 38
                                You have a Zookeeper cluster that needs to be able to withstand the loss of 2 servers and still be able to function. What size should your Zookeeper cluster have?

                                Answer: A

                                Explanation:
                                Your Zookeeper cluster needs to have an odd number of servers, and must maintain a majority of servers up to be able to vote. Therefore, a 2N+1 zookeeper cluster can survive to N zookeeper being down, so here the right answer is N=2, 2*N+1=5


                                NEW QUESTION # 39
                                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 # 40
                                Suppose you have 6 brokers and you decide to create a topic with 10 partitions and a replication factor of 3.
                                The brokers 0 and 1 are on rack A, the brokers 2 and 3 are on rack B, and the brokers 4 and 5 are on rack C.
                                If the leader for partition 0 is on broker 4, and the first replica is on broker 2, which broker can host the last replica? (select two)

                                Answer: B,E

                                Explanation:
                                When you create a new topic, partitions replicas are spreads across racks to maintain availability. Hence, the Rack A, which currently does not hold the topic partition, will be selected for the last replica


                                NEW QUESTION # 41
                                In the Kafka consumer metrics it is observed that fetch-rate is very high and each fetch is small. What steps will you take to increase throughput?

                                Answer: C

                                Explanation:
                                This will allow consumers to wait and receive more bytes in each fetch request.


                                NEW QUESTION # 42
                                StreamsBuilder builder = new StreamsBuilder();
                                KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
                                .mapValues(textLine -> textLine.toLowerCase())
                                .flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
                                .selectKey((key, word) -> word)
                                .groupByKey()
                                .count(Materialized.as("Counts"));
                                wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?

                                Answer: C

                                Explanation:
                                Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.


                                NEW QUESTION # 43
                                ......

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