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

Comprehensive and Detailed Explanation From Exact Extract:

✅ C. Consumers can consume both committed and uncommitted transactions.By default, Kafka consumers only read committed messages if they are configured with isolation.level=read_committed. However, if configured as read_uncommitted, they can also consume uncommitted (potentially aborted) transactional messages.

From Kafka Documentation:

"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."

✅ D. Information about producers and their transactions is stored in the _transaction_state topic.Kafka uses an internal topic named __transaction_state to maintain metadata about producer transactions. This topic is essential for tracking the transaction lifecycle, fencing, and recovery.

From Kafka Internals:

“Kafka stores the state of active and completed transactions in an internal topic called __transaction_state.”

The Apache Kafka producer API is asynchronous by default, and proper error handling requires explicit mechanisms. The official documentation recommends using a callback function with producer.send() to determine whether a record was successfully acknowledged or failed due to an exception. The callback provides access to both RecordMetadata and any exception encountered during sending.

Additionally, certain errors—such as serialization errors or configuration issues—are thrown synchronously when calling producer.send(). These must be handled using a try/catch block that catches KafkaException or its subclasses.

Option A is invalid because ProducerRecord has no status() method. Option C is incorrect because producer.send() returns a Future, not a RecordMetadata object directly.

Thus, combining a callback for asynchronous errors and a try/catch block for synchronous errors is the correct and officially documented approach to ensure reliable producer error handling when using acks=all.

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Increasing tasks.max allows the connector to create multiple tasks, each responsible for processing different tables or table partitions. If only one task is used, adding more workers has no effect.

From Kafka Connect JDBC Connector Docs:

“Use tasks.max to configure how many tasks are created for the connector. Each task can process a subset of the data.”

A and C help only if the bottleneck is in Kafka, not Connect.

D is a large architectural change, not the first step.