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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is designed for developers who wish to demonstrate their expertise in developing Apache Kafka-based applications using Confluent's platform. Confluent is a leading provider of Apache Kafka-based solutions, and the CCDAK Certification confirms that developers have the necessary skills to design, develop, and troubleshoot Kafka-based applications using Confluent's platform.
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NEW QUESTION # 52
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
Answer: A,C
Explanation:
SMTs (Single Message Transforms) arelightweight transformationsapplied to individual messages as they pass through Kafka Connect.
* Chaining SMTs: You can applymultiple SMTsin sequence by defining them in order in the connector config.
* Masking or modifying fieldsis acommon use case(e.g., redacting sensitive data).
FromKafka Connect Documentation:
"Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together."
* SMTsdo not perform joins(B is incorrect).
* Converters and SMTs areseparate concerns(D is incorrect).
Reference:Kafka Connect Transformations Guide
NEW QUESTION # 53
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?
Answer: C
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 # 54
(You create a topic with five partitions.
What can you assume about messages read from that topic by a single consumer group?)
Answer: B
Explanation:
The Apache Kafka documentation explains that Kafka's parallelism model is based on topic partitions and consumer groups. Within a single consumer group, each partition can be assigned to at most one consumer at a time. As a result, the maximum level of parallelism for a consumer group is equal to the number of partitions in the topic.
In this scenario, the topic has five partitions, which means that up to five consumers in the same consumer group can actively consume data in parallel. If more than five consumers are added, the extra consumers will remain idle because there are no additional partitions to assign.
Option B is incorrect because Kafka does not enforce equal message consumption across partitions; message distribution depends on producers and partitioning strategy. Option C is false because partitions may be hosted on different brokers, and consumers fetch data from multiple brokers. Option D is incorrect because a single partition cannot be consumed by multiple consumers within the same group simultaneously.
Therefore, the only valid assumption is that a maximum of five consumers can actively consume messages in the same consumer group.
NEW QUESTION # 55
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?
producer.send(record, new MyCallback(record));
Answer: D
NEW QUESTION # 56
You are experiencing low throughput from a Java producer.
Metrics show low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?
Answer: C
Explanation:
Low I/O thread activity with blocked throughput often indicates that producer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.
From Kafka Producer Performance Guide:
"Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput." Compression (A) may slightly impact CPU but not I/O thread usage.
Large batches (B) improve throughput if managed correctly.
A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.
Reference: Kafka Producer Metrics and Performance Tuning
NEW QUESTION # 57
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