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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Kafka Fundamentals	10-15%	- Brokers and Clusters - Log Segments and Retention - Producer and Consumer Basics - Replication Factor and ISR - Topics, Partitions, and Offsets
Schema Management	5-10%	- Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility - Protobuf and JSON Schemas
Security	5-10%	- Encryption in Transit - Authorization (ACLs) - Securing Schema Registry - Authentication (SASL, SSL/TLS)
Kafka Connect	10-15%	- Converters and Transforms - Source and Sink Connectors - Single Message Transformations (SMT) - Offset Management in Connect - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks)
Developing with Kafka Consumers	15-20%	- Deserialization - Consumer Configuration and Tuning - Consumer Interceptors - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Consumer Groups and Rebalancing
Developing with Kafka Producers	15-20%	- Error Handling and Retry Logic - Idempotent Producers - Producer Configuration and Tuning - Exactly-Once Semantics - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Producer Interceptors
Monitoring and Operations	5-10%	- Log Compaction - Cross-region Replication - Disaster Recovery - Kafka Metrics and Monitoring - Consumer Lag and Health Checks
Kafka Streams	20-25%	- Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Exactly-Once Processing - Testing Kafka Streams Applications - State Stores and Processors
Installing and Configuring Kafka	5-10%	- ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Topic Creation and Configuration - Partition Assignment Strategies

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q39-Q44):

NEW QUESTION # 39
What happens if you write the following code in your producer? producer.send(producerRecord).get()

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

Answer: C

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

NEW QUESTION # 40
A bank uses a Kafka cluster for credit card payments. What should be the value of the property unclean.leader.election.enable?

A. TRUE
B. FALSE

Answer: B

Explanation:
Setting unclean.leader.election.enable to true means we allow out-of-sync replicas to become leaders, we will lose messages when this occurs, effectively losing credit card payments and making our customers very angry.

NEW QUESTION # 41
To allow consumers in a group to resume at the previously committed offset, I need to set the proper value for...

A. enable.auto.commit
B. group.id
C. value.deserializer
D. auto.offset.resets

Answer: B

Explanation:
Setting a group.id that's consistent across restarts will allow your consumers part of the same group to resume reading from where offsets were last committed for that group

NEW QUESTION # 42
Which is an optional field in an Avro record?

A. fields
B. name
C. namespace
D. doc

Answer: D

Explanation:
doc represents optional description of message

NEW QUESTION # 43
We want the average of all events in every five-minute window updated every minute. What kind of Kafka Streams window will be required on the stream?

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

Answer: C

Explanation:
A hopping window is defined by two propertiesthe window's size and its advance interval (aka "hop"), e.g., a hopping window with a size 5 minutes and an advance interval of 1 minute.

NEW QUESTION # 44
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