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Confluent is a company that provides a distributed streaming platform built on Apache Kafka. The Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam is designed for developers who want to demonstrate their knowledge and skills in building and managing Kafka-based applications using Confluent's platform. CCDAK exam is an industry-recognized certification that validates a developer's ability to work with Kafka and Confluent.

The CCDAK Certification Exam is designed to test the proficiency of developers in building Kafka-based solutions. CCDAK exam covers various topics such as Kafka architecture, configuration, messaging, API, and security. By achieving the CCDAK certification, developers can demonstrate their understanding of Kafka concepts and their ability to build scalable and fault-tolerant Kafka applications.

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The Confluent Certified Developer for Apache Kafka Certification Examination certification is an opportunity for developers to demonstrate their understanding of Kafka and its ecosystem components, as well as their hands-on experience in building Kafka-based applications. Confluent Certified Developer for Apache Kafka Certification Examination certification exam validates the candidate's Kafka knowledge, skillset, and ability to develop solutions that solve real-world Kafka use cases. Moreover, the CCDAK Certification is recognized worldwide, making it a valuable credential that can help software developers secure their next Kafka development role.

Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q77-Q82):

NEW QUESTION # 77
You need to set alerts on key broker metrics to trigger notifications when the cluster is unhealthy.
Which are three minimum broker metrics to monitor?
(Select three.)

A. kafka.controller:type=KafkaController,name=TopicsToDeleteCount
B. kafka.controller:type=KafkaController,name=OfflinePartitionsCount
C. kafka.controller:type=KafkaController,name=LastCommittedRecordOffset
D. kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec
E. kafka.controller:type=KafkaController,name=ActiveControllerCount

Answer: B,D,E

Explanation:
These three metrics are critical for cluster health:
* OfflinePartitionsCount: Indicates partitions without active leaders - a sign of broker failure.
* ActiveControllerCount: There should beexactly one active controller. A count # 1 signals controller failure.
* UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected - risky for data loss.
FromKafka Monitoring Documentation:
"Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running."
* A is about topics pending deletion - not critical.
* E is a per-topic record metric, not broker-level.
Reference:Kafka Monitoring > Key JMX Metrics

NEW QUESTION # 78
If I supply the setting compression.type=snappy to my producer, what will happen? (select two)

A. The Kafka brokers have to de-compress the data
B. The Kafka brokers have to compress the data
C. The Producers have to compress the data
D. The Consumers have to compress the data
E. The Consumers have to de-compress the data

Answer: E

Explanation:
Kafka transfers data with zero copy and no transformation. Any transformation (including compression) is the responsibility of clients.

NEW QUESTION # 79
How do you read a table or stream from the beginning of a topic?

A. CREATE STREAM shipped_orders AS
SELECT
B. id as orderId
C. SET 'auto.offset.reset' = 'latest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
D. id as shipmentId,
E. SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
F. itemId as itemId,
G. SET 'auto.offset.reset' = 'earliest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
H. 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;

Answer: G

NEW QUESTION # 80
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?

A. Modify the log4j.properties file to add a dedicated log appender for the connector.
B. Change the log level to DEBUG to have connector context information in logs.
C. Make no change, there is no way to find logs other than by stopping all the other connectors.
D. Modify the log4j.properties file to enable connector context.

Answer: A

Explanation:
To isolate logs for a specific connector, you can configurea separate logger and appenderin theConnect worker's log4j.propertiesfile, using the connector's name as the logging context.
FromKafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties."
* A and C change verbosity but don't separate logs.
* D is false; targeted logging is possible.
Reference:Kafka Connect > Logging and Debugging

NEW QUESTION # 81
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?

A. buffer.memory
B. batch.size
C. send.buffer.bytes
D. max.block.size

Answer: B

Explanation:
Thebatch.sizeconfig sets themaximum number of bytes to batch per partitionbefore sending. This allows Kafka producers toamortize I/Oand improve throughput.
FromKafka Producer Configuration Docs:
"batch.size is the maximum amount of data per partition the producer will batch before sending."
* buffer.memory sets total memory for the producer, not per-batch.
* send.buffer.bytes is aTCP socket buffer, not a Kafka config.
* max.block.ms controls blocking time, not size.
Reference:Kafka Producer Configs > batch.size

NEW QUESTION # 82
......

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Exam Confluent CCDAK Cram Review & Test CCDAK Preparation

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q77-Q82):

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