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Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) Certification Exam is a popular certification exam for developers who wish to demonstrate their proficiency in working with Apache Kafka. Apache Kafka is an open-source distributed event streaming platform that is widely used for building real-time data pipelines and streaming applications. The CCDAK certification exam is designed to test the skills and knowledge of developers who work with Kafka on a regular basis.
The Confluent CCDAK Exam is designed to test the candidate's knowledge in various areas such as Kafka architecture, deployment, Kafka configuration, message producers, message consumers, Kafka Streams, and Kafka Connect. CCDAK exam consists of 60 multiple-choice questions and must be completed within 90 minutes.
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Confluent CCDAK (Confluent Certified Developer for Apache Kafka) certification exam is designed for software developers who want to demonstrate their proficiency in building Kafka-based applications. Kafka is an open-source distributed messaging system that allows applications to exchange messages in real-time. It offers a highly scalable, fault-tolerant, and reliable platform for building large-scale distributed systems. The CCDAK Exam Tests the candidate's ability to work with Kafka and its ecosystem components e.g., producer, consumer, connectors, and streams API.
NEW QUESTION # 80
(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 # 81
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
Answer: B,D
Explanation:
SMTs (Single Message Transforms) are lightweight transformations applied to individual messages as they pass through Kafka Connect.
Chaining SMTs: You can apply multiple SMTs in sequence by defining them in order in the connector config.
Masking or modifying fields is a common use case (e.g., redacting sensitive data).
From Kafka Connect Documentation:
"Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together." SMTs do not perform joins (B is incorrect).
Converters and SMTs are separate concerns (D is incorrect).
Reference: Kafka Connect Transformations Guide
NEW QUESTION # 82
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: A
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 # 83
Using the Confluent Schema Registry, where are Avro schema stored?
Answer: A
Explanation:
The Schema Registry stores all the schemas in the _schemas Kafka topic
NEW QUESTION # 84
A topic receives all the orders for the products that are available on a commerce site. Two applications want to process all the messages independently - order fulfilment and monitoring. The topic has 4 partitions, how would you organise the consumers for optimal performance and resource usage?
Answer: A
Explanation:
two partitions groups - one for each application so that all messages are delivered to both the application. 4 consumers in each as there are 4 partitions of the topic, and you cannot have more consumers per groups than the number of partitions (otherwise they will be inactive and wasting resources)
NEW QUESTION # 85
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