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Confluent CCDAK Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Application Development | 28% | - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing |
| Topic 2: Apache Kafka Streams | 12% | - Stream processing operations: map, filter, join, aggregation - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Error handling and state management |
| Topic 3: Kafka Connect | 15% | - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns |
| Topic 4: Application Observability | 13% | - Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning |
| Topic 5: Apache Kafka Fundamentals | 23% | - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Configuration basics and cluster setup |
| Topic 6: Application Testing | 8% | - Mocking and test utilities - Integration testing and test clusters - Unit testing producers and consumers |
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:
(Which configuration is valid for deploying a JDBC Source Connector to read all rows from the orders table and write them to the dbl-orders topic?)
- A. {"name": "jdbc-source","connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&password=pas","topic.prefix":
"dbl-","table.whitelist": "orders"} - B. {"name": "jdbc-source","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&useAutoAuth=true","topic.
prefix": "dbl-","table.whitelist": "orders"} - C. {"name": "dbl-orders","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&password=pas","topic.prefix":
"dbl-","table.blacklist": "ord*"} - D. {"name": "orders-connect","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl","topic.whitelist": "orders","auto.create":
"true"}
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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. Surround the call of producer.send() with a try/catch block to catch KafkaException.
- B. Check that producer.send() returned a RecordMetadata object and is not null.
- C. Check the value of ProducerRecord.status().
- D. Use a callback argument in producer.send() where you check delivery status.
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You create a producer that writes messages about bank account transactions from tens of thousands of different customers into a topic.
Your consumers must process these messages with low latency and minimize consumer lag Processing takes ~6x longer than producing Transactions for each bank account must be processed in orderWhich strategy should you use?
- A. Use the timestamp of the message's arrival as its key.
- B. Use a combination of the bank account number and the transaction timestamp as the message key.
- C. Use a unique identifier such as a universally unique identifier (UUID) as the message key.
- D. Use the bank account number found in the message as the message key.
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The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?
- A. The number of bytes that overflowed beyond a producer batch of messages
- B. The sequential ID of the message committed into a partition
- C. Its position in the producer's batch of messages
- D. The ID of the partition to which the message was committed
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A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.
- A. 15 created, 15 actively consuming
- B. 5 created, 5 actively consuming
- C. 5 created, 1 actively consuming
- D. 15 created, 5 actively consuming
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