Apache Kafka does not impose any schema or message format; it treats records as byte arrays on the wire. Serialization is therefore the responsibility of the producer. For lightweight XML messages, the most common and recommended approach is to represent the XML as a UTF-8 encoded string and use Kafka’s built-in StringSerializer.

The official Kafka producer documentation provides StringSerializer as the standard serializer for textual data formats, including XML and JSON. This serializer converts Java String objects into byte arrays using UTF-8 encoding, making it simple, efficient, and interoperable with a wide range of consumers.

Option A is incorrect because Kafka does not provide a built-in XmlSerializer. Option C (ByteSerializer) could technically work, but it would require manually converting the XML string to a byte array, which adds unnecessary complexity for lightweight XML payloads. Option D (VoidSerializer) is used only when no value is sent and is not applicable here.

Therefore, using StringSerializer for the value field is the correct and officially supported approach for sending lightweight XML messages in Kafka.

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The onCompletion() method of Kafka’s Callback interface is executed once the broker acknowledges the message. This includes success or failure, and it is invoked asynchronously by the producer.

From Kafka Java Client API Documentation:

“The onCompletion method will be called when the record sent to the server has been acknowledged, or when an error occurs.”

This ensures that the record was sent and acknowledged, not just added to a batch or local buffer.

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.

The ConsumerRebalanceListener lets you handle partition assignments and revocations during rebalances. This is critical for managing offsets, stateful processing, or external transactions.

From Kafka Consumer Rebalance Docs:

“Implementing ConsumerRebalanceListener allows your application to take action before and after partitions are reassigned.”

A is true: It lets your app react when partitions assigned to the consumer change.

B, C, and D are unrelated to consumer rebalancing directly.

1st: Place the connector's JAR file in the directory specified by plugin.path

2nd: Restart the Kafka Connect cluster

3rd: Verify using REST API (/connector-plugins)

4th: Configure the connector

5th: (Repeat of 4th, duplicate step)

1st → Place the connector’s JAR file in the directory specified by the plugin.path configuration.

2nd → Restart the Kafka Connect cluster.

3rd → Verify that the connector is installed by listing all available connectors using the Kafka Connect REST API (/connector-plugins).

4th → Configure the connector using a JSON or properties file with the necessary settings.

5th → Configure the connector using a JSON or properties file with the necessary settings.

Kafka Connect requires that custom connectors be placed in the directory defined by plugin.path. After restarting the cluster, you can use the REST API to confirm availability and then deploy the connector configuration.

From Kafka Connect Documentation:

“After placing the JAR in the plugin.path, you must restart the Connect cluster to pick it up. Use the /connector-plugins REST endpoint to verify.”

The duplication of configuration is an error in the question options and should occur only once.

The Apache Kafka monitoring and operations documentation highlights several controller-level metrics that are critical indicators of cluster health.

ActiveControllerCount must always be 1 in a healthy cluster. A value of 0 indicates no active controller, while values greater than 1 indicate a serious split-brain condition. This metric is fundamental for cluster stability.

OfflinePartitionsCount is one of the most important health metrics. Any value greater than 0 means that some partitions have no available leader, making them unavailable for producers and consumers. This directly indicates a degraded or unhealthy cluster.

UncleanLeaderElectionsPerSec tracks the rate of unclean leader elections. Unclean elections can cause data loss, and any non-zero value is a strong signal of broker failures or misconfiguration and should immediately trigger alerts.

The remaining options are not minimum health indicators. LastCommittedRecordOffset is not a standard health metric, and TopicsToDeleteCount only indicates pending administrative operations, not cluster instability.

Therefore, the three minimum and officially recommended broker metrics for monitoring Kafka cluster health are UncleanLeaderElectionsPerSec, ActiveControllerCount, and OfflinePartitionsCount.

unclean.leader.election.enable=false → Prevents data loss by only considering in-sync replicas when rebalancing.

replication.factor → Specifies how many redundant copies of partitions are distributed across brokers.

min.insync.replicas=2 → Sets the standard for the number of partition instances that must keep up with the latest committed message.

unclean.leader.election.enable=false ensures that only in-sync replicas can be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.

replication.factor defines how many brokers will maintain copies of each partition, directly impacting durability and availability.

min.insync.replicas determines how many replicas must acknowledge a write when acks=all is used, enforcing write durability.

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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For Kafka Streams to perform joins, topics must be co-partitioned, meaning:

They have the same number of partitions (A), and

They use the same partitioning strategy (typically by key) (B)

From Kafka Streams Documentation > Joins:

“Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner.”

Value schema (C) and retention time (D) have no bearing on join compatibility.

max.poll.interval.ms defines the maximum delay between invocations of poll() before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.

From Kafka Consumer Configuration Reference:

“max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed.”

session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.

fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.