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The CCDAK exam covers a range of topics related to Kafka, including architecture, configuration, performance tuning, security, and more. CCDAK exam consists of multiple-choice questions and requires candidates to demonstrate their ability to apply their knowledge to real-world scenarios. CCDAK exam is administered online and can be taken from anywhere in the world. Candidates have 90 minutes to complete the exam, and they must score at least 70% to pass.

Confluent CCDAK Exam is intended for individuals who have a good understanding of Kafka and have experience with Kafka development. CCDAK exam is a great opportunity for developers to showcase their Kafka skills and improve their career prospects. A successful candidate will receive a digital badge and a certificate that can be shared on professional networking sites such as LinkedIn.

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To prepare for the Confluent CCDAK Exam, developers can attend the Confluent Developer Training or take online courses, such as the Confluent Cloud Fundamentals for Developers course, which is available on Udemy. They can also refer to the official documentation and practice using Kafka clusters, producers, and consumers. There are also practice exams available online to help developers prepare for the certification exam.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q87-Q92):

NEW QUESTION # 87
Select all that applies (select THREE)

A. min.insync.replicas only matters if acks=all
B. min.insync.replicas matters regardless of the values of acks
C. min.insync.replicas is a producer setting
D. min.insync.replicas is a topic setting
E. acks is a producer setting
F. acks is a topic setting

Answer: A,D,E

Explanation:
acks is a producer setting min.insync.replicas is a topic or broker setting and is only effective when acks=all

NEW QUESTION # 88
Which of the following statements are true regarding the number of partitions of a topic?

A. We can remove partitions in a topic by removing a broker
B. We can remove partitions in a topic using the kafka-topics.sh command
C. The number of partitions in a topic cannot be altered
D. We can add partitions in a topic by adding a broker to the cluster
E. We can add partitions in a topic using the kafka-topics.sh command

Answer: E

Explanation:
We can only add partitions to an existing topic, and it must be done using the kafka-topics.sh command

NEW QUESTION # 89
How will you read all the messages from a topic in your KSQL query?

A. KSQL reads from the beginning of a topic, by default.
B. KSQL reads from the end of a topic. This cannot be changed.
C. Use KSQL CLI to set auto.offset.reset property to earliest

Answer: C

Explanation:
Consumers can set auto.offset.reset property to earliest to start consuming from beginning. For KSQL, SET
'auto.offset.reset'='earliest';

NEW QUESTION # 90
An application is writing AVRO messages using Schema Registry to topic t1. During this process, the Schema Registry becomes unavailable for a few seconds.
What is the expected impact to the application?

A. Since messages are cached by the producer, the application will only get an error if the producer is sending the batch at that time.
B. Since the broker will eventually replicate the message Schema, there will not be an error.
C. The application may not have any impact, unless it is writing messages with a new Schema Definition.
D. All messages within that time will receive an error.

Answer: C

NEW QUESTION # 91
Kafka producers can batch messages going to the same partition.
Which statement is correct about producer batching?

A. Producers have a separate background thread for each batch.
B. Producers can only batch messages of the same size.
C. Producers can include multiple batches in a single request to a broker.
D. Two or more broker failures will automatically disable batching on the producer.

Answer: C

Explanation:
Kafka producers can group messages destined for different partitions into separate batches, and send multiple batches in one request to optimize network usage.
From Kafka Producer Internals:
"The producer maintains buffers (batches) for each partition and can send multiple of these batches together in a single request to the broker." Message size is not a constraint for batching (A is false).
Broker failures don't disable batching (B is false).
There is no separate thread per batch (C is false).
Reference: Kafka Producer Architecture and Internals

NEW QUESTION # 92
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