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Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) is a certification exam designed to test the skills and knowledge of developers who work with Apache Kafka. CCDAK exam is based on a set of industry-standard best practices and is designed to validate the skills of developers who work with Kafka. CCDAK Exam is designed to be challenging, but fair, and is intended to provide a reliable measure of a developer's ability to work with Kafka.
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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q13-Q18):
NEW QUESTION # 13
If I produce to a topic that does not exist, and the broker setting auto.create.topic.enable=true, what will happen?
- A. Kafka will automatically create the topic with the broker settings num.partitions and default.replication.factor
- B. Kafka will automatically create the topic with the indicated producer settings num.partitions and default.replication.factor
- C. Kafka will automatically create the topic with 1 partition and 1 replication factor
- D. Kafka will automatically create the topic with num.partitions=#of brokers and replication.factor=3
Answer: A
Explanation:
The broker settings comes into play when a topic is auto created
NEW QUESTION # 14
A producer application was sending messages to a partition with a replication factor of 2 by connecting to Broker 1 that was hosting partition leader. If the Broker 1 goes down, what will happen?
- A. The topic will be unavailable
- B. The producer will automatically produce to the broker that has been elected leader
- C. The producer will stop working
Answer: B
Explanation:
Once the client connects to any broker, it is connected to the entire cluster and in case of leadership changes, the clients automatically do a Metadata Request to an available broker to find out who is the new leader for the topic. Hence the producer will automatically keep on producing to the correct Kafka Broker
NEW QUESTION # 15
(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?)
- A. None, the correct dependency will be added transitively by the Kafka client
- B. SLF4J implementation for Log4j 1.2 (org.slf4j:slf4j-log4j12)
- C. Just the Log4j2 dependency of the application
- D. SLF4J implementation for Log4j2 (org.apache.logging.log4j:log4j-slf4j-impl)
Answer: D
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 # 16
Which actions will trigger partition rebalance for a consumer group? (select three)
- A. Add a new consumer to consumer group
- B. Add a broker to the cluster
- C. A consumer in a consumer group shuts down
- D. Remove a broker from the cluster
- E. Increase partitions of a topic
Answer: A,C,E
Explanation:
Rebalance occurs when a new consumer is added, removed or consumer dies or paritions increased.
NEW QUESTION # 17
What happens if you write the following code in your producer? producer.send(producerRecord).get()
- A. Compression will be increased
- B. Batching will be increased
- C. Throughput will be decreased
- D. It will force all brokers in Kafka to acknowledge the producerRecord
Answer: C
Explanation:
Using Future.get() to wait for a reply from Kafka will limit throughput.
NEW QUESTION # 18
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