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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q75-Q80):

NEW QUESTION # 75
The kafka-console-consumer CLI, when used with the default options

A. does not use a group id
B. uses a random group id
C. always uses the same group id

Answer: B

Explanation:
If a group is not specified, the kafka-console-consumer generates a random consumer group.

NEW QUESTION # 76
How does a consumer commit offsets in Kafka?

A. It interacts with the Group Coordinator broker
B. It directly sends a message to the __consumer_offsets topic
C. It directly commits the offsets in Zookeeper

Answer: A

Explanation:
Consumers do not directly write to the __consumer_offsets topic, they instead interact with a broker that has been elected to manage that topic, which is the Group Coordinator broker

NEW QUESTION # 77
What's a Kafka partition made of?

A. One file and two indexes
B. One file
C. One file and two indexes per segment
D. One file and one index

Answer: C

Explanation:
Kafka partitions are made of segments (usually each segment is 1GB), and each segment has two corresponding indexes (offset index and time index)

NEW QUESTION # 78
A producer just sent a message to the leader broker for a topic partition. The producer used acks=1 and therefore the data has not yet been replicated to followers. Under which conditions will the consumer see the message?

A. Never, the produce request will fail
B. Right away
C. When the high watermark has advanced
D. When the message has been fully replicated to all replicas

Answer: C

Explanation:
The high watermark is an advanced Kafka concept, and is advanced once all the ISR replicates the latest offsets. A consumer can only read up to the value of the High Watermark (which can be less than the highest offset, in the case of acks=1)

NEW QUESTION # 79
How do you read a table or stream from the beginning of a topic?

A. id as paymentId
FROM orders o
INNER JOIN payments p WITHIN 1 HOURS ON p.id = o.id
INNER JOIN shipments s WITHIN 2 HOURS ON s.id
= o.id;
B. SET 'auto.offset.reset' = 'earliest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
C. CREATE STREAM shipped_orders AS
SELECT
D. SET 'auto.offset.reset' = 'latest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
E. itemId as itemId,
F. id as orderId
G. id as shipmentId,
H. SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;

Answer: B

NEW QUESTION # 80
......

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