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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is designed for developers who want to demonstrate their proficiency in Apache Kafka. Confluent Certified Developer for Apache Kafka Certification Examination certification exam is offered by Confluent, the creators of Apache Kafka, and is designed to test the knowledge and skills necessary to build mission-critical Kafka applications. CCDAK Exam covers a range of topics, including Kafka architecture, configuration, development, and operations.
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Confluent CCDAK Certification Exam is an industry-recognized certification that helps developers advance their careers and demonstrate their expertise in Apache Kafka. Confluent Certified Developer for Apache Kafka Certification Examination certification is offered by Confluent, a leading company in the Apache Kafka ecosystem. Confluent Certified Developer for Apache Kafka Certification Examination certification is a valuable asset for developers who want to work with Kafka-based applications and solutions, and it is highly regarded by organizations that use Kafka for their data processing needs. Confluent Certified Developer for Apache Kafka Certification Examination certification demonstrates a developer's proficiency in Kafka and helps them stand out in a competitive job market.
NEW QUESTION # 30
Your producer is producing at a very high rate and the batches are completely full each time. How can you improve the producer throughput? (select two)
Answer: A,E
Explanation:
Increase linger.ms
Explanation:
batch.size controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. Enabling compression can also help make more compact batches and increase the throughput of your producer. Linger.ms will have no effect as the batches are already full
NEW QUESTION # 31
In Kafka Streams, by what value are internal topics prefixed by?
Answer: D
Explanation:
In Kafka Streams, the application.id is also the underlying group.id for your consumers, and the prefix for all internal topics (repartition and state)
NEW QUESTION # 32
You need to set alerts on key broker metrics to trigger notifications when the cluster is unhealthy.
Which are three minimum broker metrics to monitor?
(Select three.)
Answer: A,B,D
Explanation:
These three metrics are critical for cluster health:
* OfflinePartitionsCount: Indicates partitions without active leaders - a sign of broker failure.
* ActiveControllerCount: There should beexactly one active controller. A count # 1 signals controller failure.
* UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected - risky for data loss.
FromKafka Monitoring Documentation:
"Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running."
* A is about topics pending deletion - not critical.
* E is a per-topic record metric, not broker-level.
Reference:Kafka Monitoring > Key JMX Metrics
NEW QUESTION # 33
(You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)
Answer: C
Explanation:
According to the official Apache Kafka documentation for Kafka Streams, exactly-once semantics (EOS) are controlled using the processing.guarantee configuration. To ensure that each input record is processed once and only once, and that the results are written atomically to the output topic, Kafka Streams provides built-in EOS support.
The correct and recommended configuration is processing.guarantee=exactly_once_v2, which is the default EOS implementation starting from Kafka 3.0. This mode uses Kafka's transactional APIs to atomically commit consumer offsets and produce output records within a single transaction. As documented, this prevents duplicates even in the presence of failures, retries, or application restarts.
Option A is incorrect because log compaction does not guarantee exactly-once processing; it only retains the latest record per key. Option B is insufficient because enable.idempotence alone does not handle consumer offset commits or state store consistency in Kafka Streams. Option C is invalid because no such configuration exists in Kafka Streams.
Therefore, setting processing.guarantee=exactly_once_v2 is the only fully correct and officially supported solution for achieving EOS in Kafka Streams applications.
NEW QUESTION # 34
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?
Answer: C
Explanation:
With acks=all and min.insync.replicas=2, Kafka requiresat least two in-sync replicasto acknowledge a write.
If onlyone broker is alive, the condition fails, andNotEnoughReplicasExceptionis thrown by the producer.
FromKafka 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 # 35
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