CCDAK Confluent Certified Developer for Apache Kafka Certification Examination Free Practice Exam Questions (2026 Updated)

To reset offsets for an existing consumer group, you must use the kafka-consumer-groups.sh tool with the --reset-offsets and --to-earliest flags.

From Kafka Consumer Group Tool Documentation:

“You can use the kafka-consumer-groups tool to reset offsets for a consumer group. This is required if the consumer has already committed offsets.”

Setting auto.offset.reset=earliest only works if no committed offset exists.

Starting a new consumer with the same group won’t reset offsets.

Retention settings don’t affect committed offsets.

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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A GlobalKTable is a replicated, read-only table available in full on all instances. It's best for reference or lookup datasets such as:

Product catalog

Customer profiles

Warehouse inventory

From Kafka Streams Developer Guide:

“Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate.”

Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.

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 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.

The batch.size config sets the maximum number of bytes to batch per partition before sending. This allows Kafka producers to amortize I/O and improve throughput.

From Kafka Producer Configuration Docs:

“batch.size is the maximum amount of data per partition the producer will batch before sending.”

buffer.memory sets total memory for the producer, not per-batch.

send.buffer.bytes is a TCP socket buffer, not a Kafka config.

max.block.ms controls blocking time, not size.