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The CCDAK Certification Exam is meant for developers, architects, and engineers who have experience with Kafka development, and are looking to validate their expertise in the field. CCDAK exam provides a recognized validation of Kafka proficiency, which is valuable for both individuals and organizations. For individuals, the certification can help to enhance their career prospects and demonstrate their expertise to potential employers. For organizations, the certification can help to identify skilled Kafka developers and ensure that they have the necessary expertise to meet business needs.
Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is a highly regarded certification program that is designed to test an individual’s knowledge of Apache Kafka and its associated technologies. Confluent Certified Developer for Apache Kafka Certification Examination certification program is offered by Confluent, a leading company in the field of event streaming platforms, and is aimed at developers who want to demonstrate their expertise in building applications that utilize the Kafka ecosystem.
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The CCDAK Exam is a challenging test that covers a wide range of topics related to Apache Kafka, including cluster setup and configuration, producer and consumer APIs, Kafka Streams, and Kafka Connect. CCDAK exam is designed to assess a candidate's ability to work with Apache Kafka in real-world scenarios, and to test their understanding of the platform's architecture, functionality, and best practices.
NEW QUESTION # 71
(You started a new Kafka Connect worker.
Which configuration identifies the Kafka Connect cluster that your worker will join?)
Answer: D
Explanation:
As documented in the official Apache Kafka Connect framework documentation, Kafka Connect workers form a logical cluster by using a Kafka consumer group. The configuration property that identifies this cluster is group.id.
All Kafka Connect workers configured with the same group.id automatically join the same Connect cluster.
Kafka's group coordination protocol is then used to distribute connector and task configurations among the workers, providing fault tolerance, scalability, and automatic rebalancing.
Option A (cluster.id) is a Kafka internal identifier used by brokers and is not configurable for Kafka Connect workers. Option B (worker.id) does not exist as a valid Kafka Connect configuration. Option D (connector.id) identifies individual connectors within a Connect cluster, not the worker cluster itself.
Therefore, the group.id property is the correct and officially documented configuration that determines which Kafka Connect cluster a worker belongs to.
NEW QUESTION # 72
Which two statements are correct when assigning partitions to the consumers in a consumer group using the assign() API?
(Select two.)
Answer: A,B
Explanation:
Using assign() gives manual control over partition assignment. In this mode:
B is true: The application chooses partitions directly without involving the group coordinator.
C is true: Since no group coordination occurs, no rebalancing happens if a consumer dies.
From Kafka Consumer API Docs:
"assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered." A is false: subscribe() and assign() are mutually exclusive.
D is incorrect: Partitions don't need to be equal across topics.
Reference: Kafka Consumer Java API > assign() and subscribe()
NEW QUESTION # 73
What is the default maximum size of a message the Apache Kafka broker can accept?
Answer: D
Explanation:
The default maximum message size that a Kafka broker accepts is1MB (1,048,576 bytes), controlled by the config propertymessage.max.bytes.
FromKafka Broker Configuration Docs:
"The default maximum message size is 1MB. To accept larger messages, configure message.max.bytes and the producer's max.request.size." Producers also have a matching limit via max.request.size, and consumers via fetch.message.max.bytes.
Reference:Kafka Broker Configuration > message.max.bytes
NEW QUESTION # 74
You are building a system for a retail store selling products to customers.
Which three datasets should you model as a GlobalKTable?
(Select three.)
Answer: A,C,E
Explanation:
A GlobalKTable is a replicated, read-only table available in full on all instances. It's best for reference or lookup datasets such as:
Product catalog
Customer profiles
Warehouse inventory
From Kafka Streams Developer Guide:
"Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate." Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.
Reference: Kafka Streams Concepts > GlobalKTable
NEW QUESTION # 75
To enhance compression, I can increase the chances of batching by using
Answer: D
Explanation:
linger.ms forces the producer to wait before sending messages, hence increasing the chance of creating batches that can be heavily compressed.
NEW QUESTION # 76
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