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[Hardware] Confluent CCDAK High Passing Score | Test CCDAK Valid

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【Hardware】 Confluent CCDAK High Passing Score | Test CCDAK Valid

Posted at 5/17/2026 12:08:55      View：58 | Replies：0        Print      Only Author   [Copy Link] 1#
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The CCDAK Certification Exam is a comprehensive and globally recognized certification program that validates an individual's knowledge and skills in Apache Kafka technology. It is designed for developers who work with Kafka and want to demonstrate their expertise in the field. Achieving the certification is a valuable asset for both individuals and organizations, as it ensures that developers have the necessary skills to develop and maintain Kafka-based applications.
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q23-Q28):NEW QUESTION # 23
You are working on a Kafka cluster with three nodes. You create a topic named orders with:
replication.factor = 3
min.insync.replicas = 2
acks = allWhat exception will be generated if two brokers are down due to network delay?
  • A. NotLeaderForPartitionException
  • B. NetworkException
  • C. NotEnoughReplicasException
  • D. NotCoordinatorException
Answer: C
Explanation:
With acks=all and min.insync.replicas=2, Kafka requires at least two in-sync replicas to acknowledge a write.
If only one broker is alive, the condition fails, and NotEnoughReplicasException is thrown by the producer.
From Kafka Producer Exception Docs:
"NotEnoughReplicasException is thrown when the number of in-sync replicas is insufficient to satisfy acks=all with min.insync.replicas." NetworkException is generic and not raised here.
NotCoordinatorException is related to consumer group coordination.
NotLeaderForPartitionException is unrelated unless accessing an unassigned leader.
Reference: Kafka Producer Error Handling

NEW QUESTION # 24
A customer has many consumer applications that process messages from a Kafka topic. Each consumer application can only process 50 MB/s. Your customer wants to achieve a target throughput of 1 GB/s. What is the minimum number of partitions will you suggest to the customer for that particular topic?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: D
Explanation:
each consumer can process only 50 MB/s, so we need at least 20 consumers consuming one partition so that 50 * 20 = 1000 MB target is achieved.

NEW QUESTION # 25
What is a consequence of increasing the number of partitions in an existing Kafka topic?
  • A. Consumers will need to process data from more partitions which will significantly increase consumer lag.
  • B. Existing data will be redistributed across the new number of partitions temporarily increasing cluster load.
  • C. The acknowledgment process will increase latency for producers using acks=all.
  • D. Records with the same key could be located in different partitions.
Answer: A
Explanation:
Increasing partitions increases parallelism, but also means:
Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.
This can result in increased lag, especially under high load.
From Kafka 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 partition based on hash.
D is not directly impacted by the partition count.
Reference: Kafka Topic Management > Adding Partitions

NEW QUESTION # 26
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?
  • A. EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.
  • B. Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.
  • C. Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.
  • D. EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.
Answer: D
Explanation:
Kafka Streams uses transactional producers to guarantee exactly-once semantics (EOS). This ensures that both the output records and state store updates are committed atomically, avoiding duplication or partial writes.
From Kafka Streams Documentation > Processing Guarantees:
"Kafka Streams leverages Kafka's transactional APIs to commit the output records and internal state updates as a single atomic unit, thereby providing exactly-once semantics." Option A is incorrect because log compaction is not disabled for EOS.
Option C incorrectly describes a checkpointing system Kafka Streams does not use.
Option D refers to deduplication, which is not how EOS is achieved in Streams.
Reference: Kafka Streams Processing Guarantees

NEW QUESTION # 27
How will you set the retention for the topic named 'Aumy-topic' to 1 hour?
  • A. Set the topic config retention.ms to 3600000
  • B. Set the producer config retention.ms to 3600000
  • C. Set the consumer config retention.ms to 3600000
  • D. Set the broker config log.retention.ms to 3600000
Answer: A
Explanation:
retention.ms can be configured at topic level while creating topic or by altering topic. It shouldn't be set at the broker level (log.retention.ms) as this would impact all the topics in the cluster, not just the one we are interested in

NEW QUESTION # 28
......
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