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Achieving the CCDAK Certification is a valuable credential for developers who work with Apache Kafka. Confluent Certified Developer for Apache Kafka Certification Examination certification demonstrates a high level of proficiency in working with Kafka and can help developers advance their careers and open up new job opportunities. Additionally, the certification is recognized by industry leaders and demonstrates a commitment to professional development and staying up-to-date with the latest trends and technologies in the field.

Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) is an exam designed for professionals seeking to validate their knowledge and skills in Apache Kafka. CCDAK Exam is administered by Confluent, a leading provider of enterprise data streaming platforms, and is intended for developers who have experience with Kafka and want to demonstrate their proficiency in the technology. The CCDAK exam is a crucial step for professionals who want to pursue a career in data streaming and messaging, and it is highly regarded in the industry.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q69-Q74):

NEW QUESTION # 69
What is a consequence of increasing the number of partitions in an existing Kafka topic?

A. Existing data will be redistributed across the new number of partitions temporarily increasing cluster load.
B. Records with the same key could be located in different partitions.
C. Consumers will need to process data from more partitions which will significantly increase consumer lag.
D. The acknowledgment process will increase latency for producers using acks=all.

Answer: C

Explanation:
Increasing partitions increasesparallelism, but also means:
* Consumers in a group may have to handlemore partitions, especially if the number of consumers is lower than the number of partitions.
* This can result inincreased lag, especially under high load.
FromKafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate."
* A is false:existing data is not redistributed.
* B is false:records with the same key always map to the same partitionbased on hash.
* D is not directly impacted by the partition count.
Reference:Kafka Topic Management > Adding Partitions

NEW QUESTION # 70
Producing with a key allows to...

A. Allow a Kafka Consumer to subscribe to a (topic,key) pair and only receive that data
B. Add more information to my message
C. Ensure per-record level security
D. Influence partitioning of the producer messages

Answer: D

Explanation:
Keys are necessary if you require strong ordering or grouping for messages that share the same key. If you require that messages with the same key are always seen in the correct order, attaching a key to messages will ensure messages with the same key always go to the same partition in a topic. Kafka guarantees order within a partition, but not across partitions in a topic, so alternatively not providing a key - which will result in round-robin distribution across partitions - will not maintain such order.

NEW QUESTION # 71
You want to send a message of size 3 MB to a topic with default message size configuration. How does KafkaProducer handle large messages?

A. KafkaProducer divides messages into sizes of message.max.bytes and sends them in order
B. MessageSizeTooLarge exception will be thrown, KafkaProducer will not retry and return exception immediately
C. KafkaProducer divides messages into sizes of max.request.size and sends them in order
D. MessageSizeTooLarge exception will be thrown, KafkaProducer retries until the number of retries are exhausted

Answer: B

Explanation:
MessageSizeTooLarge is not a retryable exception.

NEW QUESTION # 72
When is the onCompletion() method called?
private class ProducerCallback implements Callback {
@Override
public void onCompletion(RecordMetadata recordMetadata, Exception e) {
if (e != null) {
e.printStackTrace();
}
}
}
ProducerRecord<String, String> record =
new ProducerRecord<>("topic1", "key1", "value1");
producer.send(record, new ProducerCallback());

A. When the broker response is received
B. When the message is partitioned and batched successfully
C. When message is serialized successfully
D. When send() method is called

Answer: A

Explanation:
Callback is invoked when a broker response is received.

NEW QUESTION # 73
How do you read a table or stream from the beginning of a topic?

A. id as paymentId
FROM orders o
INNER JOIN payments p WITHIN 1 HOURS ON p.id = o.id
INNER JOIN shipments s WITHIN 2 HOURS ON s.id
= o.id;
B. id as orderId
C. CREATE STREAM shipped_orders AS
SELECT
D. itemId as itemId,
E. SET 'auto.offset.reset' = 'earliest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
F. SET 'auto.offset.reset' = 'latest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
G. id as shipmentId,
H. SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;

Answer: E

NEW QUESTION # 74
......

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