Vce CCAAK Questions Latest

Replication in Kafka ensures that partition data is copied across multiple brokers. This guarantees durability (data survives broker failure) and availability (other replicas can serve as leader if current leader fails).

From Kafka documentation:

“Replication ensures that data is not lost if a broker crashes and that messages remain available even during broker outages.”

B → Correct: Ensures persisted data even after failure.

C → Correct: Allows partition leader failover.

A → Incorrect: Replication may slightly reduce performance.

D → Incorrect: Replication is not a security mechanism.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189–191

Apache Kafka Documentation: Replication Overview

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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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In Kafka, the Controller is responsible for managing partition leadership. When a broker that was leader for certain partitions goes down, the Controller immediately detects the failure and:

Triggers a new leader election among the in-sync replicas (ISRs) of those partitions.

Updates the partition metadata to reflect the new leaders and notifies the cluster.

Kafka documentation states:

“When a broker fails, the controller triggers a leader election for all partitions previously led by the failed broker.”

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 190–191

Apache Kafka Documentation: “Kafka Controller and Leader Election”

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Upon detecting a broker failure, the Controller performs leader election among in-sync replicas and updates the new leader and ISR information in ZooKeeper. It then broadcasts this metadata update to all brokers in the cluster.

From Kafka documentation:

“The controller writes the new leader and ISR list to ZooKeeper and informs all brokers so they can update their metadata cache.”

B → Correct: New metadata written to ZooKeeper.

C → Correct: Brokers are updated via metadata propagation.

A → Incorrect: The Controller makes the decision, not ZooKeeper.

D → Incorrect: Producers and consumers learn about changes via brokers, not directly.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 190–191

Apache Kafka Documentation: Controller Behavior & ZooKeeper Interactions

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