CCDAK Confluent Certified Developer for Apache Kafka Certification Examination Questions and Answers

The ConsumerRebalanceListener lets youhandle partition assignments and revocationsduring rebalances. This is critical for managingoffsets,stateful processing, orexternal transactions.

FromKafka 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 partitionsassigned to the consumer change.

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

To send failed records to adead letter queue (DLQ), you must configure:

errors.tolerance=all: Tells the connector tonot failon errors but handle them (e.g., send to DLQ).

errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.

errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.

FromKafka Connect Error Handling Docs:

“Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs. Headers can include failure metadata.”

Options D, E, F are related tologging, not DLQ behavior.

Kafka Connectsink connectorsuse thestandard Kafka consumer mechanismto track offsets, which means offsets are stored in the__consumer_offsets internal topic.

FromKafka Connect Documentation:

“Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer.”

The other options refer tosource connectorsor worker configuration.

offsets.storage.topic is relevant forsource connectors.

SMTs (Single Message Transforms) arelightweight transformationsapplied to individual messages as they pass through Kafka Connect.

Chaining SMTs: You can applymultiple SMTsin sequence by defining them in order in the connector config.

Masking or modifying fieldsis acommon use case(e.g., redacting sensitive data).

FromKafka Connect Documentation:

“Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together.”

SMTsdo not perform joins(B is incorrect).

Converters and SMTs areseparate concerns(D is incorrect).

Frequentrebalancesin a consumer group occur when:

Consumers are too slowand miss heartbeats (A), or

Instances crash or restart, triggering group membership changes (D)

FromKafka Consumer Group Coordination Docs:

“If a consumer fails to send a heartbeat in time (due to slowness or crash), it is removed from the group, causing a rebalance.”

Option B (partition mismatch) affectsload balancing, not rebalance frequency.

Option C (broker storage) doesn't trigger consumer rebalances.

In Kafka Streams,the number of stream tasks = number of input partitions × num.stream.threads × number of instances,but only as many as the number of partitions can actively consume at once.

However, in this case,Kafka Streams assigns one task per partition, and because there are 5 partitions and 15 threads (3 instances × 5 threads),15 tasks are created, andall 15 can be activedepending on processing topology.

FromKafka Streams Developer Guide:

"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance."

So,15 stream tasks will be created and 15 will be actively consuming.

The offset in the RecordMetadata object returned by the producerrepresents the position of the record in the partition— i.e., thesequential IDassigned by Kafka once the message is committed.

FromKafka Producer API Documentation:

“The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker.”

D refers to metadata.partition(), not offset().

B and C are unrelated to how Kafka handles committed offsets.

For proper delivery handling with acks=all:

Usecallbackto log or act on success/failure.

Usetry/catchto handle synchronous exceptions like serialization errors or network failures.

FromKafka Producer Documentation:

“Errors can be caught either via the returned Future or via the callback interface. For fatal errors, use a try/catch block around the send call.”

Option B is incorrect because send() returns a Future, not RecordMetadata directly.

Option D is invalid — ProducerRecord has no method called status().

To ensureexactly-once deliveryand avoid duplicates even during retries:

enable.idempotence=trueensures deduplication on the broker

retries=2147483647allows unlimited retries on retriable errors

max.in.flight.requests.per.connection=5is themaximum value that preserves message order with idempotence

FromKafka Producer Config Docs:

“To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection ≤ 5.”

A is unrelated (consumer-side)

C disables retries

D disables idempotence, leading to duplicates

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

FromKafka 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'sno skip() methodin the Kafka consumer API.

D results in service interruption — not ideal for resiliency.

Kafkamessage headersare the right place to includemetadatasuch as:

Payload format (e.g., schema version)

Timestamps (custom)

Trace or correlation IDs

FromKafka Producer API Docs:

“Headers are a map of key-value pairs that can be used to include metadata alongside the message, without modifying the message body.”

Keys determine partitioning and ordering

Values are the message payload

Brokers do not store arbitrary metadata fields

Unit Testing→ MockProducer

Integration Testing→ Testcontainers

Performance Testing→ Trogdor

Mock Data Generation→ Connect Datagen

MockProducer: Simulates a Kafka producer in unit tests (no real broker interaction).

Testcontainers: Spawns Kafka in Docker forreal environment testing.

Trogdor: Kafka’s built-inperformance load testingframework.

Connect Datagen: Createssample source recordsfor test and demo purposes.

FromKafka Developer Tools Guide:

“Kafka developers commonly use MockProducer for unit tests, Testcontainers for integration, and Trogdor for performance tests.”

Each of these settings plays a key role inKafka Connect offset management:

offset.flush.interval.ms=60000 → Offset commits happen every 60 seconds.

offset.flush.timeout.ms=500 → The flush operation times out if it takes more than 500 ms.

offset.storage.topic=connect-offsets → This is the internal topic for storing source offsets.

offset.storage.replication.factor=-1 → This uses thebroker's default replication factor(commonly set via default.replication.factor).

FromKafka Connect Docs:

“If offset.storage.replication.factor=-1, the value will be determined by the broker's default replication factor.”

To change the replication factor of an existing topic, you must perform apartition reassignment, because the replication factor is not directly modifiable via kafka-topics.sh. Thekafka-reassign-partitions.shtool is the official command-line utility to handle partition reassignments, including changing the replication factor.

This tool allows you to generate a reassignment JSON specifying new replica assignments per partition and then apply it.

From official documentation:

“To change the replication factor, use the kafka-reassign-partitions tool to assign the partitions to more or fewer brokers.”

Neither kafka-topics.sh nor kafka-reassign-topics.sh exists for this purpose, and kafka-recreate-topic.sh isnotan official Kafka tool.

Kafka Connectsource connectorsuse theoffset.storage.topicconfiguration parameter to define where to storeoffsets that track the connector's positionin the source system (e.g., database, file, etc.).

FromKafka Connect Worker Configuration:

“offset.storage.topic specifies the topic in Kafka where the connector stores offsets for each source partition.”

This allows Kafka Connect toresumefrom the correct position on restart.

The other options (B–D) are not valid Kafka configs.