CCDAK Exam Dumps : Confluent Certified Developer for Apache Kafka Certification Examination

The Apache Kafka Streams documentation clearly distinguishes between the Kafka Streams DSL and the Processor API. While the DSL is designed for common stream processing operations such as filtering, mapping, joining, and aggregations, it does not support fully custom processing logic.

For use cases that require custom transformations, fine-grained control over record processing, access to headers, timestamps, state stores, and punctuation, Kafka Streams provides the Processor API. This API allows developers to implement custom Processor, Transformer, or ValueTransformer classes and explicitly define the processing topology.

Option A (TopologyTestDriver) is a testing utility, not a development API. Option C (DSL) is higher-level and not suitable for advanced custom logic. Option D does not exist in Kafka.

Therefore, the correct and officially supported approach for defining custom processors in Kafka Streams is to use the Processor API.

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The official Apache Kafka Connect documentation explains that connectors run within the same worker JVM, and all logs are written through the worker’s logging framework (Log4j or Log4j2). To isolate logs for a specific connector, Kafka Connect supports connector-specific logging contexts, which can be routed to separate log files using logging configuration.

By modifying the log4j.properties (or log4j2.xml) file to define a dedicated logger and appender for the connector’s package or class name, you can direct logs from that connector to a separate file. This is the recommended and documented approach for debugging individual connectors in a shared Connect cluster.

Option A is incorrect because there is no generic “enable connector context” switch. Option B may increase verbosity but does not isolate logs per connector. Option D is false; Kafka Connect explicitly supports connector-level log separation through logging configuration.

Therefore, the correct approach is to configure a dedicated log appender for the specific connector.

As documented in the official Apache Kafka Connect framework documentation, Kafka Connect workers form a logical cluster by using a Kafka consumer group. The configuration property that identifies this cluster is group.id.

All Kafka Connect workers configured with the same group.id automatically join the same Connect cluster. Kafka’s group coordination protocol is then used to distribute connector and task configurations among the workers, providing fault tolerance, scalability, and automatic rebalancing.

Option A (cluster.id) is a Kafka internal identifier used by brokers and is not configurable for Kafka Connect workers. Option B (worker.id) does not exist as a valid Kafka Connect configuration. Option D (connector.id) identifies individual connectors within a Connect cluster, not the worker cluster itself.

Therefore, the group.id property is the correct and officially documented configuration that determines which Kafka Connect cluster a worker belongs to.