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Confluent - CCDAK - Confluent Certified Developer for Apache Kafka Certification

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Confluent - CCDAK - Confluent Certified Developer for Apache Kafka Certification

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Confluent, the company that created Kafka, offers training and certification programs for professionals who want to learn and demonstrate their expertise in Kafka. The CCDAK Certification Exam is one of the many programs offered by Confluent. With the growing popularity of Kafka among enterprises, the CCDAK certification can help professionals advance their careers in big data and real-time processing.
Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam is a globally recognized certification exam that validates the skills and knowledge of developers in building and managing Apache Kafka based solutions. Confluent Certified Developer for Apache Kafka Certification Examination certification exam is designed to test the candidate's understanding of the core concepts of Apache Kafka, including Kafka architecture, messaging patterns, and stream processing.
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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam is a comprehensive certification program that validates the skills and expertise of developers who work with Apache Kafka. CCDAK Exam is designed to test the knowledge and abilities of developers in designing, developing, and deploying Kafka-based applications. Confluent Certified Developer for Apache Kafka Certification Examination certification program is offered by Confluent, the company behind Apache Kafka, and is recognized globally as a mark of excellence in Kafka development.
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q169-Q174):NEW QUESTION # 169
Which is true about topic compaction?
  • A. When a client produces a new event with an existing key, the broker immediately deletes the offset of the existing event.
  • B. Compaction will keep exactly one message per key after compaction of inactive log segments.
  • C. When a client produces a new event with an existing key, the old value is overwritten with the new value in the compacted log segment.
  • D. Topic compaction does not remove old events; instead, when clients consume events from a compacted topic, they store events in a hashmap that maintains the latest value.
Answer: B
Explanation:
Log compactionensures that Kafka retains at least thelatest value per keyin a topic. Compaction happensin the backgroundand removes older records with the same keyin inactive log segments, not immediately.
From theKafka Documentation > Log Compaction:
"Kafka guarantees that thelast message for each keywill be retained in the log after compaction, even if earlier messages with the same key are deleted." So, D is correct. A is incorrect because compaction does not overwrite; it's a background process. B is incorrect-deletion is not immediate. C incorrectly suggests client-side hashmap behavior.
Reference:Apache Kafka Log Compaction Docs

NEW QUESTION # 170
What is a consequence of increasing the number of partitions in an existing Kafka topic?
  • A. The acknowledgment process will increase latency for producers using acks=all.
  • B. Records with the same key could be located in different partitions.
  • C. Existing data will be redistributed across the new number of partitions temporarily increasing cluster load.
  • D. Consumers will need to process data from more partitions which will significantly increase consumer lag.
Answer: D
Explanation:
Increasing partitions increasesparallelism, but also means:
* Consumers in a group may have to handlemore partitions, especially if the number of consumers is lower than the number of partitions.
* This can result inincreased lag, especially under high load.
FromKafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate."
* A is false:existing data is not redistributed.
* B is false:records with the same key always map to the same partitionbased on hash.
* D is not directly impacted by the partition count.
Reference:Kafka Topic Management > Adding Partitions

NEW QUESTION # 171
What happens when broker.rack configuration is provided in broker configuration in Kafka cluster?
  • A. Each rack contains all the topics and partitions, effectively making Kafka highly available
  • B. Replicas for a partition are spread across different racks
  • C. Replicas for a partition are placed in the same rack
  • D. You can use the same broker.id as long as they have different broker.rack configuration
Answer: B
Explanation:
Partitions for newly created topics are assigned in a rack alternating manner, this is the only change broker.
rack does

NEW QUESTION # 172
You are creating a Kafka Streams application to process retail data.
Match the input data streams with the appropriate Kafka Streams object.

Answer:
Explanation:

Explanation:
* Table# Product, Customers
* Stream# Orders_Placed, Shipment_Of_Orders
* Tablesrepresent slowly changing,reference data- e.g., customer profiles, product info.
* Streamsrepresentreal-time event data- e.g., orders placed, shipments processed.
FromKafka Streams Documentation:
"Use KTable for reference datasets, and KStream for event-based processing such as orders or logs." Reference:Kafka Streams API Concepts

NEW QUESTION # 173
Which of the following is not an Avro primitive type?
  • A. string
  • B. date
  • C. int
  • D. long
  • E. null
Answer: B
Explanation:
date is a logical type

NEW QUESTION # 174
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