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Confluent CCDAK Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Kafka Streams | 20-25% | - Exactly-Once Processing - State Stores and Processors - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications |
| Developing with Kafka Producers | 15-20% | - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Exactly-Once Semantics - Producer Interceptors - Error Handling and Retry Logic - Partitioning Strategies - Idempotent Producers |
| Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Source and Sink Connectors - Converters and Transforms |
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Partition Assignment Strategies |
| Security | 5-10% | - Authorization (ACLs) - Encryption in Transit - Authentication (SASL, SSL/TLS) - Securing Schema Registry |
| Schema Management | 5-10% | - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas |
| Kafka Fundamentals | 10-15% | - Brokers and Clusters - Log Segments and Retention - Producer and Consumer Basics - Replication Factor and ISR - Topics, Partitions, and Offsets |
| Developing with Kafka Consumers | 15-20% | - Offset Management - Deserialization - Multi-threaded Consumers - Consumer Configuration and Tuning - Consumer Groups and Rebalancing - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors |
| Monitoring and Operations | 5-10% | - Consumer Lag and Health Checks - Cross-region Replication - Kafka Metrics and Monitoring - Log Compaction - Disaster Recovery |
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:
1. (You want to read messages from all partitions of a topic in every consumer instance of your application.
How do you do this?)
A) Use the assign() method with the topic name as argument.
B) Use the assign() method using all topic-partitions of the topic as argument.
C) Use the subscribe() method with an empty consumer group name configuration.
D) Use the subscribe() method with a regular expression argument.
2. You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?
A) Disable auto commit and have the consumers manually commit offsets.
B) Increase the number of bytes the consumers read with each fetch request.
C) Remove some consumers from the consumer group.
D) Decrease the session timeout of each consumer.
3. Which two statements are correct about transactions in Kafka?
(Select two.)
A) Information about producers and their transactions is stored in the _transaction_state topic.
B) All messages from a failed transaction will be deleted from a Kafka topic.
C) Transactions are only possible when writing messages to a topic with single partition.
D) Transactions guarantee at least once delivery of messages.
E) Consumers can consume both committed and uncommitted transactions.
4. (You need to send a JSON message on the wire. The message key is a string.
How would you do this?)
A) Specify a value serializer class for the JSON contents of the message's value. Set the key serializer class to JSON.
B) Specify a key serializer class for the JSON contents of the message's value. Set the value serializer class to null.
C) Specify a value serializer class for the JSON contents of the message's value. Set a key serializer for the string value.
D) Specify a value serializer class for the JSON contents of the message's value. Set the key serializer class to null.
5. (You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)
A) Set enable.exactly_once=true in the Kafka Streams configuration.
B) Set processing.guarantee=exactly_once_v2 in the Kafka Streams configuration.
C) Set enable.idempotence=true in the internal producer configuration of the Kafka Streams application.
D) Enable compaction on the output topic to handle duplicates.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A,E | Question # 4 Answer: C | Question # 5 Answer: B |



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