Confluent CCDAK Dumps Questions Answers

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.

To ensure exactly-once delivery and avoid duplicates even during retries:

enable.idempotence=true ensures deduplication on the broker

retries=2147483647 allows unlimited retries on retriable errors

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

From Kafka 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

If the application is CPU-bound and lagging, adding more consumers to the group will allow better parallel processing, especially since the topic has 4 partitions, allowing up to 4 active consumers.

From Kafka Consumer Group Docs:

“Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized.”

B may help but requires repartitioning and coordination.

C or D affects how much data is polled, not how fast it’s processed.