According to the official Apache Kafka client documentation, Kafka uses SLF4J (Simple Logging Facade for Java) for all client-side logging. SLF4J is only an abstraction layer and requires a concrete logging implementation to route log messages to the actual logging framework used by the application.

Since the application uses Log4j2, an SLF4J-to-Log4j2 binding must be provided. The correct and officially recommended dependency is org.apache.logging.log4j:log4j-slf4j-impl. This dependency acts as the SLF4J implementation and forwards all Kafka client log messages to Log4j2, ensuring consistent logging across the application.

Option B is incorrect because it targets the legacy Log4j 1.2 framework, which is deprecated and incompatible with Log4j2. Option C is insufficient because Log4j2 alone does not satisfy Kafka’s SLF4J dependency. Option D is incorrect because Kafka intentionally does not include a logging implementation transitively, allowing applications to choose their preferred logging framework.

Therefore, including the SLF4J implementation for Log4j2 is required to properly integrate Kafka client logs.

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According to the official Apache Kafka Streams documentation, Kafka Streams relies on internal topics (such as changelog topics and repartition topics) to store state, task assignments, and application metadata. These topics are essential for fault tolerance, rebalancing, and state recovery.

When auto-topic creation is disabled, Kafka Streams cannot automatically create the required internal topics unless they already exist. In this situation, the Streams application will fail during startup with a fatal, non-retriable exception, indicating that the necessary internal topics could not be created.

Kafka Streams does not pause or wait for manual topic creation, nor does it operate without storing metadata. Instead, it explicitly requires these topics to exist or be creatable. While administrators can manually pre-create the required internal topics with the correct configuration, failure to do so causes the application to terminate.

Therefore, the correct and documented behavior is that the Kafka Streams application terminates with a non-retriable exception when auto-topic creation is disabled and required internal topics are missing.

To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.

From Kafka Consumer Error Handling Docs:

“On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record.”

A does not prevent re-processing the bad record.

C is invalid; there's no skip() method in the Kafka consumer API.

D results in service interruption — not ideal for resiliency.

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.

Kafka Streams uses transactional producers to guarantee exactly-once semantics (EOS). This ensures that both the output records and state store updates are committed atomically, avoiding duplication or partial writes.

From Kafka Streams Documentation > Processing Guarantees:

“Kafka Streams leverages Kafka’s transactional APIs to commit the output records and internal state updates as a single atomic unit, thereby providing exactly-once semantics.”

Option A is incorrect because log compaction is not disabled for EOS.

Option C incorrectly describes a checkpointing system Kafka Streams does not use.

Option D refers to deduplication, which is not how EOS is achieved in Streams.

According to the official Apache Kafka consumer semantics documentation, at-most-once delivery means that records may be lost but must never be processed more than once. To achieve this guarantee, a consumer must commit offsets before processing records. If the application crashes after committing but before processing, messages are skipped rather than reprocessed, which satisfies at-most-once semantics.

Option B correctly implements this pattern. It disables automatic offset commits (enable.auto.commit=false) and explicitly calls commitAsync() immediately after polling and before processing records. This ensures that offsets are advanced regardless of whether processing succeeds, eliminating the possibility of duplicate processing.

Option C represents at-least-once semantics, because offsets are committed after processing, which can lead to duplicates on failure. Options A and D rely on automatic commits or incorrect ordering and do not provide strict at-most-once guarantees.

The Kafka documentation explicitly states that manual offset management with commits before processing is required for at-most-once delivery.

Therefore, Option B is the best and officially documented configuration and code skeleton to avoid duplicate message processing.

According to the official Apache Kafka Connect REST API documentation, restarting a connector can be done at different granularities: connector-level, task-level, or both. By default, calling POST /connectors/{name}/restart only restarts the connector instance, not its tasks.

To restart the connector and all of its tasks, the request must explicitly include the query parameter includeTasks=true. This instructs Kafka Connect to restart the connector and all associated tasks, regardless of whether they are currently running or failed.

Option B (onlyFailed=true) limits the restart to only failed tasks, which does not meet the requirement to restart all tasks. Option C restarts only the connector instance, leaving tasks untouched. Option D restarts a single specific task and does not restart the connector itself.

Therefore, the correct and officially documented REST request to restart the connector instance and all tasks is:

POST /connectors/s3-connector/restart?includeTasks=true

This ensures a full restart of the connector and its execution context across the Kafka Connect cluster.