CCDAK Quiz Torrent: Confluent Certified Developer for Apache Kafka Certification Examination - CCDAK Exam Guide & CCDAK Test Braindumps

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Confluent CCDAK Exam Overview:

Certification Vendor:Confluent
Exam Name:Confluent Certified Developer for Apache Kafka Certification Examination
Exam Number:CCDAK
Passing Score:Pass / Fail
Real Exam Qty:60
Exam Format:Matching, List order, Multiple-choice
Exam Price:$150 USD
Certificate Validity Period:2 years
Available Languages:English
Related Certifications:Confluent Certified Administrator for Apache Kafka (CCAAK)
Confluent Cloud Certified Operator (CCAC)
Exam Duration:90 minutes
Recommended Training:Confluent Documentation
Confluent Developer Training for Apache Kafka
Exam Registration:Confluent Certification Portal
Sample Questions:Confluent CCDAK Sample Questions
Exam Way:Remote proctored online exam; requires webcam, microphone, Google Chrome browser
Pre Condition:No mandatory prerequisites; recommended experience: 6+ months developing applications with Apache Kafka
Official Syllabus URL:https://www.confluent.io/certification/

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Confluent CCDAK Certification Exam is an essential certification for developers who want to demonstrate their expertise in Kafka and related technologies. Confluent Certified Developer for Apache Kafka Certification Examination certification is designed to test the knowledge and skills of developers in Kafka and is recognized globally as a standard for Kafka developers. Developers who pass the exam can demonstrate their expertise in Kafka and can be confident in their skills and knowledge.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q83-Q88):

NEW QUESTION # 83
(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?)

Answer: A

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 # 84
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=1 can't produce?

Answer: A

Explanation:
min.insync.replicas does not impact producers when acks=1 (only when acks=all)


NEW QUESTION # 85
What happens if you write the following code in your producer? producer.send(producerRecord).get()

Answer: C

Explanation:
Using Future.get() to wait for a reply from Kafka will limit throughput.


NEW QUESTION # 86
A consumer has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to
2311. The consumer group never committed offsets for the topic before. Where will the consumer read from?

Answer: D

Explanation:
Latest means that data retrievals will start from where the offsets currently end


NEW QUESTION # 87
A consumer wants to read messages from a specific partition of a topic. How can this be achieved?

Answer: C

Explanation:
assign() can be used for manual assignment of a partition to a consumer, in which case subscribe() must not be used. Assign() takes a collection of TopicPartition object as an argument https://kafka.apache.org/23/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#assign-java.util.Collection-


NEW QUESTION # 88
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