Confluent CCAAK Exam With Confidence Using Practice Dumps

Kafka uses a pull model for consumers. The consumer polls the broker for available data. This contrasts with push models (used in traditional messaging systems), where the server pushes messages to consumers.

From Kafka documentation:

“Kafka’s consumers use a pull model, where the consumer requests messages from the broker at its own pace.”

A → Incorrect: Kafka does not push messages.

B → Incorrect: Kafka is a publish-subscribe system, but not the model used for message delivery to consumers.

C → Correct: Consumers explicitly pull messages.

D → Invalid in Kafka terminology.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 126

Apache Kafka Documentation: Consumer Design Overview

To increase throughput, Kafka recommends allowing larger batches and giving more time for the producer to accumulate records into batches:

batch.size: Increasing this allows more records to be sent in a single request.

linger.ms: Increasing this adds a delay so that the producer can accumulate more messages into a batch.

From Kafka documentation:

“Increasing both batch.size and linger.ms allows the producer to send larger, more efficient batches, improving throughput at the cost of slightly increased latency.”

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 162–163

Apache Kafka Documentation: Producer Configuration Parameters

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Each Kafka partition is associated with log files and index files that are opened and maintained by the broker. Increasing the number of partitions increases the number of file descriptors (open files). Therefore, it is critical to verify that the broker's system-level max open file count (ulimit -n) is sufficiently high.

From Kafka documentation:

“Each partition uses multiple open files. When the number of partitions increases significantly, make sure to increase the max open file descriptors allowed for the Kafka process.”

C → Correct: Prevents file descriptor exhaustion.

A/B/D → Unrelated to partition count scalability.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 192

Apache Kafka Documentation: Production Deployment and Scaling Tips

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