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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q29-Q34):

NEW QUESTION # 29
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 # 30
Which of the following is true regarding thread safety in the Java Kafka Clients?

A. One Producer can be safely used in multiple threads
B. One Consumer can be safely used in multiple threads
C. One Producer needs to be run in one thread
D. One Consumer needs to run in one thread

Answer: A,D

Explanation:
KafkaConsumer is not thread-safe, KafkaProducer is thread safe.

NEW QUESTION # 31
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=all can't produce?

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

Answer: D

Explanation:
acks=all and min.insync.replicas=2 means we must have at least 2 brokers up for the partition to be available

NEW QUESTION # 32
A stream processing application is tracking user activity in online shopping carts.
You want to identify periods of user inactivity.
Which type of Kafka Streams window should you use?

A. Sliding
B. Session
C. Hopping
D. Tumbling

Answer: B

Explanation:
Session windowsare ideal for trackingperiods of activity separated by inactivity, such as user sessions.
FromKafka Streams Documentation > Windowing:
"A session window captures streams of events that areintermittentand separated by agap of inactivity."
* Tumbling/Hopping/Sliding windows are fixed in size
* Session windows are dynamic and close after inactivity timeout
This makes them perfect for identifyinggaps in user interaction.
Reference:Kafka Streams Developer Guide > Session Windows

NEW QUESTION # 33
What happens if you write the following code in your producer? producer.send(producerRecord).get()

A. It will force all brokers in Kafka to acknowledge the producerRecord
B. Throughput will be decreased
C. Compression will be increased
D. Batching will be increased

Answer: B

Explanation:
Using Future.get() to wait for a reply from Kafka will limit throughput.

NEW QUESTION # 34
......

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