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

To send failed records to a dead letter queue (DLQ), you must configure:

errors.tolerance=all: Tells the connector to not fail on errors but handle them (e.g., send to DLQ).

errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.

errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.

From Kafka Connect Error Handling Docs:

“Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs. Headers can include failure metadata.”

Options D, E, F are related to logging, not DLQ behavior.

According to the Apache Kafka producer documentation, serialization is configured independently for message keys and values. The key and value can use different serializers depending on their data types.

In this scenario, the message key is a string, so the producer must be configured with org.apache.kafka.common.serialization.StringSerializer for the key. The value contains JSON, so a suitable JSON serializer must be configured for the value, such as a custom JSON serializer, a library-based serializer, or a byte-array serializer after converting the JSON to bytes.

Option B correctly reflects this separation by specifying a string serializer for the key and a JSON serializer for the value. Kafka does not automatically infer serializers, and setting a serializer to null is invalid. Additionally, there is no built-in “JSON” serializer for keys, making Option D incorrect.

Therefore, the correct approach—fully aligned with Kafka’s producer configuration model—is to explicitly configure a StringSerializer for the key and a JSON-capable serializer for the value.

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Kafka Connect can run in standalone mode or distributed mode. For simple tasks like reading logs from a file on a single host, standalone mode is recommended.

From Kafka Connect User Guide:

“Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file).”

Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.