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

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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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.

Low I/O thread activity with blocked throughput often indicates that producer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.

From Kafka Producer Performance Guide:

“Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput.”

Compression (A) may slightly impact CPU but not I/O thread usage.

Large batches (B) improve throughput if managed correctly.

A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.