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[General] CCDAK VCE dumps & CCDAK preparation labs & CCDAK VCE files

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【General】 CCDAK VCE dumps & CCDAK preparation labs & CCDAK VCE files

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The CCDAK exam covers a wide range of topics, including Kafka architecture, data modeling, stream processing, and security. CCDAK examination comprises multiple-choice questions and hands-on exercises that require the candidate to demonstrate their practical knowledge of Kafka. Successful completion of the CCDAK exam certifies that the individual has the expertise to design, build, and maintain Kafka-based solutions, making them a valuable asset to any organization that uses Kafka for data processing and messaging.
The CCDAK Exam mainly focuses on Kafka core concepts and API usage, including Kafka architecture, topics, partitions, replication, and the Kafka producer and consumer API. Kafka Connect and Kafka Streams API are two important components of the Kafka ecosystem, and candidates have to demonstrate their proficiency in these areas too. The Kafka Connect API is used for building connectors that transfer data between Kafka and other data sources, while the Kafka Streams library is used for stream processing in Kafka.
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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q54-Q59):NEW QUESTION # 54
How will you set the retention for the topic named 'Aumy-topic' to 1 hour?
  • A. Set the broker config log.retention.ms to 3600000
  • B. Set the consumer config retention.ms to 3600000
  • C. Set the topic config retention.ms to 3600000
  • D. Set the producer config retention.ms to 3600000
Answer: C
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 # 55
(You are writing to a Kafka topic with producer configuration acks=all.
The producer receives acknowledgements from the broker but still creates duplicate messages due to network timeouts and retries.
You need to ensure that duplicate messages are not created.
Which producer configuration should you set?)
  • A. retries=2147483647max.in.flight.requests.per.connection=1enable.idempotence=true
  • B. enable.auto.commit=true
  • C. retries=2147483647max.in.flight.requests.per.connection=5enable.idempotence=false
  • D. retries=0max.in.flight.requests.per.connection=5enable.idempotence=true
Answer: A
Explanation:
The official Apache Kafka producer documentation states that setting enable.idempotence=true guarantees that messages are written to a partition exactly once, even in the presence of retries caused by network failures or broker timeouts. This feature prevents duplicate records by assigning producer sequence numbers and validating them on the broker side.
For idempotent producers to work correctly, retries must be enabled, which is why Kafka recommends a very large value such as retries=2147483647. Additionally, limiting max.in.flight.requests.per.connection to 1 ensures strict ordering during retries, preventing message reordering in older Kafka versions and providing the safest configuration.
Option A is irrelevant to producers. Option B explicitly disables idempotence, which causes duplicates.
Option D disables retries, which increases the risk of message loss.
Therefore, the correct and fully documented solution to eliminate duplicate messages is enabling idempotence with retries and a safe in-flight request limit, as shown in Option C.

NEW QUESTION # 56
A topic has three replicas and you set min.insync.replicas to 2. If two out of three replicas are not available, what happens when a produce request with acks=all is sent to broker?
  • A. Produce request will block till one of the two unavailable partition is available again.
  • B. NotEnoughReplicasException will be returned
  • C. Produce request is honored with single in-sync replica
Answer: B
Explanation:
With this configuration, a single in-sync replica becomes read-only. Produce request will receive NotEnoughReplicasException.

NEW QUESTION # 57
(What are two stateless operations in the Kafka Streams API?
Select two.)
  • A. Reduce
  • B. Join
  • C. GroupBy
  • D. Filter
Answer: C,D
Explanation:
In the Kafka Streams API, operations are classified as stateless or stateful based on whether they require maintaining local state stores. According to the official Kafka Streams documentation, stateless operations process each record independently, without storing or accessing prior records.
Filter is a stateless operation because it evaluates each record individually and decides whether to pass it downstream. It does not require any state or historical context.
GroupBy is also considered stateless because it merely repartitions the stream by assigning a new key and forwarding records to downstream processors. While it triggers the creation of an internal repartition topic, the GroupBy operation itself does not maintain a state store.
In contrast, Reduce is a stateful operation because it aggregates records over time and requires maintaining intermediate results in a state store. Similarly, Join operations are stateful because Kafka Streams must buffer and store records from one or both input streams or tables to perform the join within a defined time window.
Thus, the correct stateless operations are Filter and GroupBy, as documented in the Kafka Streams developer guide.

NEW QUESTION # 58
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?
  • A. EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.
  • B. Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.
  • C. EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.
  • D. Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.
Answer: A
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 # 59
......
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