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The CCDAK Exam is ideal for Kafka developers who are passionate about this technology and looking for new challenges. Kafka developers who pass the exam can differentiate themselves from other developers and obtain recognition as experts in Kafka-based solutions. Confluent Certified Developer for Apache Kafka Certification Examination certification can enable developers to gain new career opportunities and increase their salary. The CCDAK exam helps organizations looking for qualified Kafka professionals identify the right candidates for their projects.
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. Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q27-Q32):NEW QUESTION # 27
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. Records with the same key could be located in different partitions.
C. The acknowledgment process will increase latency for producers using acks=all.
D. Existing data will be redistributed across the new number of partitions temporarily increasing cluster load.
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 # 28
Which partition assignment minimizes partition movements between two assignments?
A. RangeAssignor
B. RoundRobinAssignor
C. PartitionAssignor
D. StickyAssignor
Answer: D
Explanation:
The StickyAssignor tries to minimize partition movement by preserving existing assignments as much as possible while still achieving a balanced assignment. This improves consumer stability and reduces rebalances.
From the Kafka Consumer Assignor Documentation:
"The StickyAssignor attempts to preserve as many existing assignments as possible, which helps minimize partition movement between rebalances." RoundRobinAssignor focuses on even distribution, not stability.
RangeAssignor groups partitions by topic and assigns them consecutively, but can lead to imbalances.
PartitionAssignor is an abstract base class, not an assignor used directly.
Reference: Kafka Consumer Assignor Docs
NEW QUESTION # 29
A client connects to a broker in the cluster and sends a fetch request for a partition in a topic. It gets an exception Not Leader For Partition Exception in the response. How does client handle this situation?
A. Send metadata request to the same broker for the topic and select the broker hosting the leader replica
B. Send metadata request to Zookeeper for the topic and select the broker hosting the leader replica
C. Get the Broker id from Zookeeper that is hosting the leader replica and send request to it
D. Send fetch request to each Broker in the cluster
Answer: A
Explanation:
In case the consumer has the wrong leader of a partition, it will issue a metadata request. The Metadata request can be handled by any node, so clients know afterwards which broker are the designated leader for the topic partitions. Produce and consume requests can only be sent to the node hosting partition leader.
NEW QUESTION # 30
(You are designing a stream pipeline to monitor the real-time location of GPS trackers, where historical location data is not required.
Each event has:
* Key: trackerId
* Value: latitude, longitude
You need to ensure that the latest location for each tracker is always retained in the Kafka topic.
Which topic configuration parameter should you set?)
A. retention.ms=0
B. retention.ms=infinite
C. min.cleanable.dirty.ratio=-1
D. cleanup.policy=compact
Answer: D
Explanation:
According to the official Apache Kafka documentation, log compaction is the mechanism used to retain only the latest record for each key in a topic. By setting cleanup.policy=compact, Kafka ensures that older records with the same key are eventually removed, leaving only the most recent value for each key.
This behavior is exactly suited for use cases such as tracking the current state of an entity, including GPS tracker locations, user profiles, or configuration data. In this scenario, the key (trackerId) uniquely identifies a tracker, and the value represents its most recent latitude and longitude.
Option B (retention.ms=infinite) retains all historical data, which contradicts the requirement. Option C (min.
cleanable.dirty.ratio) controls when compaction runs, not what data is retained. Option D (retention.ms=0) would immediately delete all data and is unsafe.
Therefore, setting cleanup.policy=compact is the correct and officially documented solution to always retain the latest location per tracker.
NEW QUESTION # 31
You are writing a producer application and need to ensure proper delivery. You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
(Select two.)
A. Check the value of ProducerRecord.status().
B. Check that producer.send() returned a RecordMetadata object and is not null.
C. Use a callback argument in producer.send() where you check delivery status.
D. Surround the call of producer.send() with a try/catch block to catch KafkaException.
Answer: C,D
Explanation:
For proper delivery handling with acks=all:
* Usecallbackto log or act on success/failure.
* Usetry/catchto handle synchronous exceptions like serialization errors or network failures.
FromKafka Producer Documentation:
"Errors can be caught either via the returned Future<RecordMetadata> or via the callback interface. For fatal errors, use a try/catch block around the send call." Option B is incorrect because send() returns a Future, not RecordMetadata directly.
Option D is invalid - ProducerRecord has no method called status().
Reference:Kafka Producer Error Handling and Callback APIs
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