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

Correct Matches

path.format→Only valid for some Source Connectors

value.converter→Valid for all Source Connectors

input.path→Only valid for some Source Connectors

errors.retry.timeout→Valid for all Source Connectors

tasks.max→Valid for all Source Connectors

path.formatThis is a connector-specific configuration, commonly used by file-based or storage-based source connectors (for example, HDFS, S3, or FilePulse connectors). It isnot part of the Kafka Connect framework itself, so it is only valid for some source connectors.

value.converterThis is aworker-level configurationthat defines how record values are converted (JSON, Avro, Protobuf, etc.). It applies toall connectorsrunning on the worker unless overridden at the connector level.

input.pathThis configuration is specific to file-based source connectors (e.g., FileStreamSourceConnector). It isnot universally supported, so it is only valid for some source connectors.

errors.retry.timeoutThis is part of Kafka Connect’serror handling framework, introduced to support retries and tolerance of transient errors. It is ageneric Connect configurationand valid for all connectors.

tasks.maxThis is amandatory, generic connector configurationthat controls the maximum number of tasks a connector may create. It is valid forall source (and sink) connectors.

max.poll.interval.ms defines the maximum delay between invocations of poll() before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.

From Kafka Consumer Configuration Reference:

“max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed.”

session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.

fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.

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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