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Confluent CCDAK Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Partition Assignment Strategies - Topic Creation and Configuration |
| Developing with Kafka Producers | 15-20% | - Serialization (JSON, Avro, Protobuf) - Producer Interceptors - Exactly-Once Semantics - Error Handling and Retry Logic - Idempotent Producers - Partitioning Strategies - Producer Configuration and Tuning |
| Monitoring and Operations | 5-10% | - Cross-region Replication - Consumer Lag and Health Checks - Log Compaction - Kafka Metrics and Monitoring - Disaster Recovery |
| Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Exactly-Once Processing - Interactive Queries - State Stores and Processors |
| Schema Management | 5-10% | - Schema Evolution and Compatibility - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Protobuf and JSON Schemas |
| Security | 5-10% | - Authorization (ACLs) - Encryption in Transit - Authentication (SASL, SSL/TLS) - Securing Schema Registry |
| Kafka Connect | 10-15% | - Connector Configuration - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Source and Sink Connectors - Offset Management in Connect |
| Developing with Kafka Consumers | 15-20% | - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning - Offset Management - Multi-threaded Consumers - Consumer Interceptors - Consumer Groups and Rebalancing - Deserialization |
| Kafka Fundamentals | 10-15% | - Log Segments and Retention - Brokers and Clusters - Producer and Consumer Basics - Replication Factor and ISR - Topics, Partitions, and Offsets |
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:
You are writing a producer application and need to ensure proper delivery. You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
(Select two.)
- A. Check the value of ProducerRecord.status().
- B. Surround the call of producer.send() with a try/catch block to catch KafkaException.
- C. Use a callback argument in producer.send() where you check delivery status.
- D. Check that producer.send() returned a RecordMetadata object and is not null.
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(You are configuring a source connector that writes records to an Orders topic.
You need to send some of the records to a different topic.
Which Single Message Transform (SMT) is best suited for this requirement?)
- A. RegexRouter
- B. InsertField
- C. TombstoneHandler
- D. HeaderFrom
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What are two examples of performance metrics?
(Select two.)
- A. fetch-rate
- B. Number of active user sessions
- C. incoming-byte-rate
- D. total-login-attempts
- E. Time of last failed login
- F. Number of active users
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(You are writing to a Kafka topic with producer configuration acks=all.
The producer receives acknowledgements from the broker but still creates duplicate messages due to network timeouts and retries.
You need to ensure that duplicate messages are not created.
Which producer configuration should you set?)
- A. retries=2147483647max.in.flight.requests.per.connection=5enable.idempotence=false
- B. retries=0max.in.flight.requests.per.connection=5enable.idempotence=true
- C. retries=2147483647max.in.flight.requests.per.connection=1enable.idempotence=true
- D. enable.auto.commit=true
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(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 enable.idempotence=true in the internal producer configuration of the Kafka Streams application.
- C. Enable compaction on the output topic to handle duplicates.
- D. Set processing.guarantee=exactly_once_v2 in the Kafka Streams configuration.
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