Pass the Confluent Certified Developer CCDAK Questions and answers with ExamsMirror

To reset offsets for anexisting consumer group, you must use thekafka-consumer-groups.shtool with the --reset-offsets and --to-earliest flags.

FromKafka 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 worksif no committed offset exists.

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

Retention settings don’t affect committed offsets.

A:Kafka batches storecompression metadata in their headers, indicating how the batch was compressed.

B:Compression is controlled via theproducer config compression.type, which defaults to none. Producers explicitly set Gzip or other formats.

FromKafka Producer Docs:

“Compression is done by the producer, and the type is stored in the message batch metadata. The broker does not recompress.”

C is false: messages are serializedbeforecompression.

D is false: messages without keys are distributed acrossmultiple partitions.

The official CLI utility for consuming messages from Kafka topics is kafka-console-consumer.sh. It connects to the broker, consumes messages, and prints them to standard output.

FromKafka CLI Tools Documentation:

"kafka-console-consumer.sh is used to read data from a Kafka topic and write it to standard output."

The other options are not valid Kafka CLI tools.

If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.

FromKafka Docs > Heartbeats and Failures:

“If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance.”

Reducing max.poll.records mightslow consumption further. More consumers won’t help since each partition already has a dedicated consumer.

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 increase consumerthroughput, one effective strategy is toincrease the amount of datafetched in each poll by raisingfetch.max.bytesormax.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.

FromKafka Consumer Config Docs:

“Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead.”

Removing consumers (A) may reduce parallelism.

Manual commit (C) adds complexity, not throughput.

Decreasing session timeout (D) risks unnecessary rebalances.

fetch-rate: Measures how frequently consumers fetch data — critical for monitoring throughput.

incoming-byte-rate: Shows how much data is being written to the broker per second — a keyproducer-side metric.

FromKafka JMX Metrics Guide:

“fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers.”

B, C, E, and F areapplication-layer business metrics, not Kafka performance metrics.

Session windowsare ideal for trackingperiods of activity separated by inactivity, such as user sessions.

FromKafka Streams Documentation > Windowing:

“A session window captures streams of events that areintermittentand separated by agap of inactivity.”

Tumbling/Hopping/Sliding windows are fixed in size

Session windows are dynamic and close after inactivity timeout

This makes them perfect for identifyinggaps in user interaction.

As documented in the official Apache Kafka Connect framework documentation, Kafka Connect workers form a logical cluster by using a Kafka consumer group. The configuration property that identifies this cluster is group.id.

All Kafka Connect workers configured with the same group.id automatically join the same Connect cluster. Kafka’s group coordination protocol is then used to distribute connector and task configurations among the workers, providing fault tolerance, scalability, and automatic rebalancing.

Option A (cluster.id) is a Kafka internal identifier used by brokers and is not configurable for Kafka Connect workers. Option B (worker.id) does not exist as a valid Kafka Connect configuration. Option D (connector.id) identifies individual connectors within a Connect cluster, not the worker cluster itself.

Therefore, the group.id property is the correct and officially documented configuration that determines which Kafka Connect cluster a worker belongs to.

For real-time applications, the key metric to monitor is consumer lag — the difference between the latest offset on the broker and the last committed offset by the consumer.

From Kafka Monitoring Guide:

“Consumer lag is the most important metric for real-time applications. It tells you how far behind the consumer is from the latest data in Kafka.”

B relates to replication, not client responsiveness.

C tracks group membership stability, not data delay.

D tracks input throughput, not processing latency.