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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q71-Q76):

NEW QUESTION # 71
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 1. What is the maximum number of brokers that can be down so that a producer with acks=all can still produce to the topic?

A. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
Two brokers can go down, and one replica will still be able to receive and serve data

NEW QUESTION # 72
Which configuration is used to determine which directory the connectors are stored in?

A. status.storage.topic
B. offset.storage.topic
C. group.id
D. plugin.path

Answer: D

NEW QUESTION # 73
An application is consuming messages from Kafka. You observe partitions being frequently reassigned to the consumer group from the application logs.
Which factors may be contributing to this? (Choose 2.)

A. An instance of the application is crashing and getting restarted
B. The number of partitions is not matching the number of application instances.
C. There is a slow consumer processing application.
D. There is a storage issue on the broker.

Answer: C

NEW QUESTION # 74
What's a Kafka partition made of?

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

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 # 75
You create a topic named stream-logs with:
* A replication factor of 3
* Four partitions
* Messages that are plain logs without a keyHow will messages be distributed across partitions?

A. All messages will be written to the same log segment.
B. Messages will be distributed among all the topic partitions with strict ordering.
C. Messages will be distributed round-robin among all the topic partitions.
D. The first message will always be written to partition 0.

Answer: C

Explanation:
If amessage key is not provided, Kafka's default partitioner usesround-robin distributionacross available partitions.
FromKafka Producer Design:
"If no key is provided, the default partitioner distributes messages round-robin to available partitions."
* A is incorrect - not all go to partition 0.
* C is invalid - Kafka doesn't group messages into one segment without a key.
* D is false - ordering isnot preservedacross partitions without a key.
Reference:Kafka Producer Partitioner Behavior

NEW QUESTION # 76
......

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