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Confluent CCDAK Exam Overview:

Certification Vendor:Confluent
Exam Name:Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam
Exam Number:CCDAK
Certificate Validity Period:3 years
Real Exam Qty:60
Passing Score:70%
Related Certifications:Confluent Certified Administrator for Apache Kafka (CCAK)
Confluent Certified Developer for Apache Kafka (CCDAK)
Available Languages:English
Exam Format:Multiple select, Multiple choice
Exam Price:$150 USD
Exam Duration:90 minutes
Recommended Training:Kafka Fundamentals Course
Apache Kafka Developer Training (Confluent)
Exam Registration:Confluent Certification Portal
Confluent Training and Exams
Sample Questions:Confluent CCDAK Sample Questions
Exam Way:Online proctored exam or authorized testing center
Pre Condition:Basic understanding of Java or another JVM language and familiarity with distributed systems concepts is recommended.
Official Syllabus URL:https://www.confluent.io/certification/

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The CCDAK Certification Exam is a valuable tool for both developers and organizations looking to build and maintain Kafka-based applications. With its comprehensive coverage of Kafka concepts and hands-on exercises, the exam provides a thorough evaluation of a developer's skills and knowledge, making it a highly respected and sought-after certification in the industry.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are using JDBC source connector to copy data from 3 tables to three Kafka topics. There is one connector created with max.tasks equal to 2 deployed on a cluster of 3 workers. How many tasks are launched?

Answer: B

Explanation:
here, we have three tables, but the max.tasks is 2, so that's the maximum number of tasks that will be created


NEW QUESTION # 65
StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
.mapValues(textLine -> textLine.toLowerCase())
.flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
.selectKey((key, word) -> word)
.groupByKey()
.count(Materialized.as("Counts"));
wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?

Answer: D

Explanation:
Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.


NEW QUESTION # 66
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?

producer.send(record, new MyCallback(record));

Answer: A


NEW QUESTION # 67
Which partition assignment minimizes partition movements between two assignments?

Answer: A

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 # 68
(You need to send a JSON message on the wire. The message key is a string.
How would you do this?)

Answer: B

Explanation:
According to the Apache Kafka producer documentation, serialization is configured independently for message keys and values. The key and value can use different serializers depending on their data types.
In this scenario, the message key is a string, so the producer must be configured with org.apache.kafka.
common.serialization.StringSerializer for the key. The value contains JSON, so a suitable JSON serializer must be configured for the value, such as a custom JSON serializer, a library-based serializer, or a byte-array serializer after converting the JSON to bytes.
Option B correctly reflects this separation by specifying a string serializer for the key and a JSON serializer for the value. Kafka does not automatically infer serializers, and setting a serializer to null is invalid.
Additionally, there is no built-in "JSON" serializer for keys, making Option D incorrect.
Therefore, the correct approach-fully aligned with Kafka's producer configuration model-is to explicitly configure a StringSerializer for the key and a JSON-capable serializer for the value.


NEW QUESTION # 69
......

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