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Confluent CCDAK Exam Syllabus Topics:

Section	Objectives
Topic 1: Kafka Fundamentals	- Kafka architecture and core concepts 1. Brokers, topics, partitions 2. Replication and fault tolerance - Data flow in Kafka 1. Offset management 2. Producer and consumer model
Topic 2: Kafka Ecosystem and Confluent Platform	- Schema Registry 1. Avro, Protobuf, JSON Schema usage - ksqlDB and connectors 1. Kafka Connect basics 2. Stream querying concepts
Topic 3: Kafka Streams and Processing	- Kafka Streams API 1. Stateful vs stateless processing 2. Stream processing fundamentals - Event processing patterns 1. Windowing and aggregation 2. Exactly-once semantics
Topic 4: Kafka Producers and Consumers	- Consumer API 1. Consumer groups 2. Rebalancing and offset commits - Producer API 1. Reliability and acknowledgments 2. Serialization and partitioning strategies

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q20-Q25):

NEW QUESTION # 20
If I want to send binary data through the REST proxy to topic "test_binary", it needs to be base64 encoded. A consumer connecting directly into the Kafka topic

A. avro data
B. json data
C. base64 encoded data, it will need to decode it
D. "test_binary" will receive
E. binary data

Answer: E

Explanation:
On the producer side, after receiving base64 data, the REST Proxy will convert it into bytes and then send that bytes payload to Kafka. Therefore consumers reading directly from Kafka will receive binary data.

NEW QUESTION # 21
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));

A. When a broker sends an acknowledgement to the producer
B. When the producer puts the message into its socket buffer
C. When a consumer sends an acknowledgement to the producer
D. When the producer batches the message

Answer: A

NEW QUESTION # 22
You are developing a Java application using a Kafka consumer.
You need to integrate Kafka's client logs with your own application's logs using log4j2.
Which Java library dependency must you include in your project?

A. SLF4J implementation for Log4j2 (org.apache.logging.log4j:log4j-slf4j-impl)
B. Just the log4j2 dependency of the application
C. None, the right dependency will be added by the Kafka client dependency by transitivity.
D. SLF4J implementation for Log4j 1.2 (org.slf4j:slf4j-log4j12)

Answer: A

Explanation:
Kafka clients use SLF4J as a logging facade. To redirect Kafka logs to log4j2, you must include the SLF4J binding for log4j2 in your classpath:
"org.apache.logging.log4j:log4j-slf4j-impl bridges SLF4J to Log4j2 so that Kafka's logs integrate into your application's Log4j2 configuration." Option A is for Log4j 1.x, which is outdated.
Option C is incorrect; Kafka clients don't include logging bindings.
Option D is insufficient without the SLF4J bridge.
Reference: Kafka Logging Integration Practices

NEW QUESTION # 23
What is a consequence of increasing the number of partitions in an existing Kafka topic?

A. Existing data will be redistributed across the new number of partitions temporarily increasing cluster load.
B. Records with the same key could be located in different partitions.
C. Consumers will need to process data from more partitions which will significantly increase consumer lag.
D. The acknowledgment process will increase latency for producers using acks=all.

Answer: C

Explanation:
Increasing partitions increases parallelism, but also means:
Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.
This can result in increased lag, especially under high load.
From Kafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate." A is false: existing data is not redistributed.
B is false: records with the same key always map to the same partition based on hash.
D is not directly impacted by the partition count.
Reference: Kafka Topic Management > Adding Partitions

NEW QUESTION # 24
How often is log compaction evaluated?

A. Every time a new partition is created
B. Every time a message is flushed to disk
C. Every time a segment is closed
D. Every time a message is sent to Kafka

Answer: C

Explanation:
Log compaction is evaluated every time a segment is closed. It will be triggered if enough data is "dirty" (see dirty ratio config)

NEW QUESTION # 25
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