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The CCDAK certification exam consists of a series of multiple-choice questions that assess the candidate's knowledge in various aspects of Kafka development. CCDAK exam covers topics such as Kafka architecture, Kafka APIs, Kafka Connect, Kafka Streams, and Kafka Security. Candidates must pass the exam with a minimum score of 70% to earn the CCDAK certification. Confluent Certified Developer for Apache Kafka Certification Examination certification is valid for two years and can be renewed by passing the recertification exam. The CCDAK Certification program is an excellent way for developers to demonstrate their Kafka development skills, increase their marketability, and advance their careers in the field of big data and streaming technologies.

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Confluent Certified Developer for Apache Kafka (CCDAK) certification exam is designed for individuals who want to demonstrate their expertise in using Apache Kafka to build data streaming applications. Confluent Certified Developer for Apache Kafka Certification Examination certification is offered by Confluent, a leading provider of data streaming platforms and solutions. The CCDAK Certification Exam is an industry-recognized validation of an individual's knowledge and skills in designing, developing, and deploying event-driven, real-time streaming applications using Apache Kafka.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q49-Q54):

NEW QUESTION # 49
Kafka is configured with following parameters - log.retention.hours = 168 log.retention.minutes = 168 log.
retention.ms = 168 How long will the messages be retained for?

A. 168 ms
B. 168 hours
C. Broker will not start due to bad configuration
D. 168 minutes

Answer: A

Explanation:
If more than one similar config is specified, the smaller unit size will take precedence.

NEW QUESTION # 50
A consumer starts and has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group has committed the offset 643 for the topic before. Where will the consumer read from?

A. offset 2311
B. offset 643
C. offset 45
D. it will crash

Answer: B

Explanation:
The offsets are already committed for this consumer group and topic partition, so the property auto.offset.reset is ignored

NEW QUESTION # 51
Select all the way for one consumer to subscribe simultaneously to the following topics - topic.history, topic.sports, topic.politics? (select two)

A. consumer.subscribe(Pattern.compile("topic\..*"));
B. consumer.subscribePrefix("topic.");
C. consumer.subscribe(Arrays.asList("topic.history", "topic.sports", "topic.politics"));
D. consumer.subscribe("topic.history"); consumer.subscribe("topic.sports"); consumer.subscribe("topic.politics");

Answer: A,C

Explanation:
Multiple topics can be passed as a list or regex pattern.

NEW QUESTION # 52
Which of the following is true regarding thread safety in the Java Kafka Clients?

A. One Producer can be safely used in multiple threads
B. One Consumer needs to run in one thread
C. One Consumer can be safely used in multiple threads
D. One Producer needs to be run in one thread

Answer: A,B

Explanation:
KafkaConsumer is not thread-safe, KafkaProducer is thread safe.

NEW QUESTION # 53
(You create an Orders topic with 10 partitions.
The topic receives data at high velocity.
Your Kafka Streams application initially runs on a server with four CPU threads.
You move the application to another server with 10 CPU threads to improve performance.
What does this example describe?)

A. Vertical Scaling
B. Plain Scaling
C. Horizontal Scaling
D. Scaling Out

Answer: A

Explanation:
This scenario describes vertical scaling, as defined in the Apache Kafka Streams documentation and general distributed systems terminology. Vertical scaling refers to adding more resources (CPU, memory, or disk) to a single machine to increase processing capacity.
In this example, the Kafka Streams application is moved from a server with four CPU threads to one with ten CPU threads, increasing the available compute resources without increasing the number of application instances. Kafka Streams can take advantage of additional CPU threads through its num.stream.threads configuration, allowing more partitions to be processed in parallel within the same application instance.
Horizontal scaling (also known as scaling out) would involve adding more application instances across multiple servers, not upgrading a single server. Option D is therefore incorrect, and Option C is not a recognized scaling term.
Thus, this example clearly illustrates vertical scaling, where performance is improved by increasing the capacity of a single node.

NEW QUESTION # 54
......

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