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The CCDAK Certification Exam is designed to test the knowledge and skills of developers in building Kafka-based applications. CCDAK exam covers topics such as Kafka architecture, configuration, security, performance tuning, and application development using Kafka APIs. Confluent Certified Developer for Apache Kafka Certification Examination certification is vendor-neutral, meaning it is not tied to any particular distribution of Kafka, and is based on the most up-to-date version of Kafka available at the time of the exam.
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Confluent CCDAK is a certification exam designed for individuals who want to demonstrate their expertise in Apache Kafka and related technologies. Confluent Certified Developer for Apache Kafka Certification Examination certification is offered by Confluent, the company that created Kafka, and is recognized globally as a standard for Kafka developers. Confluent CCDAK Certification Exam is a comprehensive examination that tests the knowledge and skills of developers in Kafka.
NEW QUESTION # 49
You want to perform table lookups against a KTable everytime a new record is received from the KStream. What is the output of KStream-KTable join?
Answer: C
Explanation:
Here KStream is being processed to create another KStream.
NEW QUESTION # 50
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
Answer: A,B
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 # 51
You are sending messages to a Kafka cluster in JSON format and want to add more information related to each message:
Format of the message payload
Message creation time
A globally unique identifier that allows the message to be traced through the systemWhere should this additional information be set?
Answer: C
Explanation:
Kafka message headers are the right place to include metadata such as:
Payload format (e.g., schema version)
Timestamps (custom)
Trace or correlation IDs
From Kafka Producer API Docs:
"Headers are a map of key-value pairs that can be used to include metadata alongside the message, without modifying the message body." Keys determine partitioning and ordering Values are the message payload Brokers do not store arbitrary metadata fields Reference: Kafka ProducerRecord API > Headers
NEW QUESTION # 52
To enhance compression, I can increase the chances of batching by using
Answer: B
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 # 53
(You are developing a Java application that includes a Kafka consumer.
You need to integrate Kafka client logs with your own application logs.
Your application is using the Log4j2 logging framework.
Which Java library dependency must you include in your project?)
Answer: C
Explanation:
According to the official Apache Kafka client documentation, Kafka uses SLF4J (Simple Logging Facade for Java) for all client-side logging. SLF4J is only an abstraction layer and requires a concrete logging implementation to route log messages to the actual logging framework used by the application.
Since the application uses Log4j2, an SLF4J-to-Log4j2 binding must be provided. The correct and officially recommended dependency is org.apache.logging.log4j:log4j-slf4j-impl. This dependency acts as the SLF4J implementation and forwards all Kafka client log messages to Log4j2, ensuring consistent logging across the application.
Option B is incorrect because it targets the legacy Log4j 1.2 framework, which is deprecated and incompatible with Log4j2. Option C is insufficient because Log4j2 alone does not satisfy Kafka's SLF4J dependency. Option D is incorrect because Kafka intentionally does not include a logging implementation transitively, allowing applications to choose their preferred logging framework.
Therefore, including the SLF4J implementation for Log4j2 is required to properly integrate Kafka client logs.
NEW QUESTION # 54
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