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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Application Testing	8%	- Unit testing producers and consumers - Integration testing and test clusters - Mocking and test utilities
Topic 2: Kafka Connect	15%	- Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns
Topic 3: Apache Kafka Fundamentals	23%	- Configuration basics and cluster setup - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Delivery semantics: at-most-once, at-least-once, exactly-once
Topic 4: Apache Kafka Application Development	28%	- Producer API: configuration, serialization, partitioning, error handling - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution
Topic 5: Apache Kafka Streams	12%	- Stream processing operations: map, filter, join, aggregation - Windowing and time semantics - Error handling and state management - Streams API concepts: topology, processors, state stores
Topic 6: Application Observability	13%	- Error diagnosis and troubleshooting - Tracing and performance tuning - Logging and monitoring metrics

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

NEW QUESTION # 20
(You started a new Kafka Connect worker.
Which configuration identifies the Kafka Connect cluster that your worker will join?)

A. connector.id
B. worker.id
C. group.id
D. cluster.id

Answer: C

Explanation:
As documented in the official Apache Kafka Connect framework documentation, Kafka Connect workers form a logical cluster by using a Kafka consumer group. The configuration property that identifies this cluster is group.id.
All Kafka Connect workers configured with the same group.id automatically join the same Connect cluster.
Kafka's group coordination protocol is then used to distribute connector and task configurations among the workers, providing fault tolerance, scalability, and automatic rebalancing.
Option A (cluster.id) is a Kafka internal identifier used by brokers and is not configurable for Kafka Connect workers. Option B (worker.id) does not exist as a valid Kafka Connect configuration. Option D (connector.id) identifies individual connectors within a Connect cluster, not the worker cluster itself.
Therefore, the group.id property is the correct and officially documented configuration that determines which Kafka Connect cluster a worker belongs to.

NEW QUESTION # 21
A topic has three replicas and you set min.insync.replicas to 2. If two out of three replicas are not available, what happens when a produce request with acks=all is sent to broker?

A. NotEnoughReplicasException will be returned
B. Produce request will block till one of the two unavailable partition is available again.
C. Produce request is honored with single in-sync replica

Answer: A

Explanation:
With this configuration, a single in-sync replica becomes read-only. Produce request will receive NotEnoughReplicasException.

NEW QUESTION # 22
Which of the following is a push query?

A. CREATE STREAM STUDENTS (ID STRING KEY, SCORE INT)
WITH (kafka_topic='students_topic', value_format='JSON', partitions=4);
B. SELECT windowstart, windowend, item_id, SUM(quantity)
FROM orders
WINDOW TUMBLING (SIZE 20 SECONDS)
GROUP BY item_id
EMIT CHANGES;
C. CREATE TABLE LEFT_TABLE (ID BIGINT PRIMARY KEY, NAME varchar, VALUE bigint) WITH (kafka_topic='left_topic', value_format='JSON', partitions=4);
D. SELECT *
FROM NUMBER_OF_TESTS
WHERE ID='10';

Answer: B

NEW QUESTION # 23
You are experiencing low throughput from a Java producer. You monitor the Kafka metrics and notice that the metrics for that producer show low I/O thread ratio and low I/O thread wait ratio.
What is most likely the cause of the slow producer performance?

A. There is a bad data link layer (layer 2) connection from the producer to the cluster.
B. Compression is enabled.
C. The producer is sending large batches of messages.
D. The producer code has an expensive callback function.

Answer: D

NEW QUESTION # 24
You have a Kafka cluster and all the topics have a replication factor of 3. One intern at your company stopped a broker, and accidentally deleted all the data of that broker on the disk. What will happen if the broker is restarted?

A. The broker will start, and won't be online until all the data it needs to have is replicated from other leaders
B. The broker will start, and won't have any data. If the broker comes leader, we have a data loss
C. The broker will start, and other topics will also be deleted as the broker data on the disk got deleted
D. The broker will crash

Answer: A

Explanation:
Kafka replication mechanism makes it resilient to the scenarios where the broker lose data on disk, but can recover from replicating from other brokers. This makes Kafka amazing!

NEW QUESTION # 25
......

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