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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Apache Kafka Fundamentals	23%	- Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Configuration basics and cluster setup
Apache Kafka Streams	12%	- Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation - Error handling and state management - Windowing and time semantics
Kafka Connect	15%	- Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration
Application Observability	13%	- Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning
Application Testing	8%	- Integration testing and test clusters - Mocking and test utilities - Unit testing producers and consumers
Apache Kafka Application Development	28%	- Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q63-Q68):

NEW QUESTION # 63
How do Kafka brokers ensure great performance between the producers and consumers? (select two)

A. It compresses the messages as it writes to the disk
B. It does not transform the messages
C. It buffers the messages on disk, and sends messages from the disk reads
D. It leverages zero-copy optimisations to send data straight from the page-cache
E. It transforms the messages into a binary format

Answer: B,D

Explanation:
Kafka transfers data with zero-copy and sends the raw bytes it receives from the producer straight to the consumer, leveraging the RAM available as page cache

NEW QUESTION # 64
Which of the following errors are retriable from a producer perspective? (select two)

A. NOT_ENOUGH_REPLICAS
B. NOT_LEADER_FOR_PARTITION
C. TOPIC_AUTHORIZATION_FAILED
D. INVALID_REQUIRED_ACKS
E. MESSAGE_TOO_LARGE

Answer: A,B

Explanation:
Both of these are retriable errors, others non-retriable errors. See the full list of errors and their "retriable" status herehttps://kafka.apache.org/protocol#protocol_error_codes

NEW QUESTION # 65
(You create a topic with five partitions.
What can you assume about messages read from that topic by a single consumer group?)

A. Messages from one partition can be consumed by any of the consumers in a group for faster processing.
B. Messages can be consumed by a maximum of five consumers in the same consumer group.
C. All messages will be read from exactly one broker by the consumer group.
D. The consumer group can only read the same number of messages from all the partitions.

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 # 66
I am producing Avro data on my Kafka cluster that is integrated with the Confluent Schema Registry. After a schema change that is incompatible, I know my data will be rejected. Which component will reject the data?

A. The Kafka Producer itself
B. Zookeeper
C. The Confluent Schema Registry
D. The Kafka Broker

Answer: C

Explanation:
The Confluent Schema Registry is your safeguard against incompatible schema changes and will be the component that ensures no breaking schema evolution will be possible. Kafka Brokers do not look at your payload and your payload schema, and therefore will not reject data

NEW QUESTION # 67
A consumer has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to
2311. The consumer group never committed offsets for the topic before. Where will the consumer read from?

A. offset 45
B. it will crash
C. offset 0
D. offset 2311

Answer: D

Explanation:
Latest means that data retrievals will start from where the offsets currently end

NEW QUESTION # 68
......

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