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The CCDAK certification exam covers a range of topics related to Kafka, including basic Kafka concepts, Kafka architecture, Kafka APIs, Kafka Streams, and Kafka Connect. Candidates who pass the exam will be able to demonstrate their proficiency in working with Kafka, and will be able to use the certification as a valuable credential in their careers. CCDAK exam is designed to be challenging, but with the right preparation and study, any developer can pass the exam and earn the CCDAK Certification.

Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is designed to test the knowledge and skills of developers working with Apache Kafka. The CCDAK exam is a vendor-neutral certification that is recognized globally and is considered as the industry standard for Kafka developers. Passing the exam is a great way to demonstrate your expertise in Kafka and can help you stand out in a competitive job market.

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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is an industry-recognized certification that enables professionals to demonstrate their understanding of Apache Kafka and its ecosystem. CCDAK examination is developed and administered by Confluent, a leading provider of Apache Kafka-based solutions. The CCDAK Certification is designed for developers who work with Apache Kafka and want to validate their skills and knowledge of the platform.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q16-Q21):

NEW QUESTION # 16
You have a Kafka client application that has real-time processing requirements.
Which Kafka metric should you monitor?

Answer: C

Explanation:
For real-time applications, the key metric to monitor is consumer lag - the difference between the latest offset on the broker and the last committed offset by the consumer.
From Kafka Monitoring Guide:
"Consumer lag is the most important metric for real-time applications. It tells you how far behind the consumer is from the latest data in Kafka." B relates to replication, not client responsiveness.
C tracks group membership stability, not data delay.
D tracks input throughput, not processing latency.
Reference: Kafka Operations > Monitoring > Consumer Lag


NEW QUESTION # 17
What exceptions may be caught by the following producer? (select two)
ProducerRecord<String, String> record =
new ProducerRecord<>("topic1", "key1", "value1");
try {
producer.send(record);
} catch (Exception e) {
e.printStackTrace();
}

Answer: A,D

Explanation:
These are the client side exceptions that may be encountered before message is sent to the broker, and before a future is returned by the .send() method.


NEW QUESTION # 18
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: B

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


NEW QUESTION # 19
You are creating a Kafka Streams application to process retail data.
Match the input data streams with the appropriate Kafka Streams object.

Answer:

Explanation:

Explanation:
* Table# Product, Customers
* Stream# Orders_Placed, Shipment_Of_Orders
* Tablesrepresent slowly changing,reference data- e.g., customer profiles, product info.
* Streamsrepresentreal-time event data- e.g., orders placed, shipments processed.
FromKafka Streams Documentation:
"Use KTable for reference datasets, and KStream for event-based processing such as orders or logs." Reference:Kafka Streams API Concepts


NEW QUESTION # 20
You use Kafka Connect with the JDBC source connector to extract data from a large database and push it into Kafka.
The database contains tens of tables, and the current connector is unable to process the data fast enough.
You add more Kafka Connect workers, but throughput doesn't improve.
What should you do next?

Answer: C

Explanation:
Increasing tasks.max allows the connector to create multiple tasks, each responsible for processing different tables or table partitions. If only one task is used, adding more workers has no effect.
From Kafka Connect JDBC Connector Docs:
"Use tasks.max to configure how many tasks are created for the connector. Each task can process a subset of the data." A and C help only if the bottleneck is in Kafka, not Connect.
D is a large architectural change, not the first step.
Reference: Kafka Connect JDBC Connector Configuration


NEW QUESTION # 21
......

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