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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According to the official Apache Kafka Connect documentation, RegexRouter is the SMT specifically designed to dynamically change the destination topic name of records produced by a connector. It works by applying a regular expression to the original topic name and rewriting it to a new topic name.

This makes RegexRouter the correct choice when some records must be routed to a different topic, typically in combination with connector-level logic, predicates, or multiple connectors. It is commonly used for topic renaming, topic versioning, or routing records to alternate topics.

InsertField (Option B) only adds metadata fields (such as topic, partition, or timestamp) to the record payload and does not affect routing. TombstoneHandler (Option C) is used to manage null-value records, especially with compacted topics. HeaderFrom (Option D) copies fields into headers but does not change the target topic.

Therefore, RegexRouter is the only SMT that directly supports changing the output topic, as documented in the Kafka Connect SMT reference.

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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.