All CCDAK Test Inside Confluent Questions

According to the official Apache Kafka Connect and Confluent JDBC Source Connector documentation, the correct connector class for a JDBC source connector is io.confluent.connect.jdbc.JdbcSourceConnector. This connector is used to read data from relational databases and publish each table as a Kafka topic.

To read all rows from a specific table, the configuration must include table.whitelist (or table.include.list in newer versions) with the table name, and a topic.prefix to determine the Kafka topic name. In this case, using topic.prefix=dbl- with table.whitelist=orders results in records being written to the dbl-orders topic, which matches the requirement.

Options A, B, and C are invalid because they reference a non-existent connector class (DdbcSourceConnector), contain unsupported properties such as topic.whitelist for a source connector, or include malformed/incorrect JDBC parameters. Additionally, blacklisting tables does not ensure that only the orders table is read.

Therefore, option D is the only configuration that is syntactically correct, uses the proper connector class, and aligns with the official Kafka Connect JDBC Source Connector documentation.

Using assign() gives manual control over partition assignment. In this mode:

B is true: The application chooses partitions directly without involving the group coordinator.

C is true: Since no group coordination occurs, no rebalancing happens if a consumer dies.

From Kafka Consumer API Docs:

“assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered.”

A is false: subscribe() and assign() are mutually exclusive.

D is incorrect: Partitions don't need to be equal across topics.

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.

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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