CCAAK Exam Dumps - Confluent Certified Administrator Questions and Answers

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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The Kafka REST Proxy, provided as part of Confluent Platform, allows clients to interact with Kafka over REST APIs. When securing communication between REST Proxy and REST clients, it uses TLS (HTTPS).

As per the official Confluent documentation for REST Proxy security:

“The REST Proxy supports HTTPS to encrypt communication between REST clients and the REST Proxy.”

TLS/SSL encryption ensures that data in transit is secure and private.

Other options listed, such as MD5 (a hashing algorithm), SCRAM (an authentication mechanism), and Kerberos (an authentication protocol), do not provide direct encryption of the communication channel itself for REST Proxy. They can be part of broader security configurations (e.g., SCRAM for Kafka brokers, Kerberos for SASL authentication), butTLS (HTTPS)is the explicit mechanism for encrypting data between the REST Proxy and its clients.

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 9 (“Kafka Security”), Section “REST Proxy Security” (p. 288–289).

Confluent Platform REST Proxy Documentation, “Encryption (HTTPS)”.

With min.insync.replicas=3 and acks=all, a partition must have at least 3 in-sync replicas (ISRs) for the producer to succeed. With one broker down (broker 0), only 2 ISRs remain → not enough to meet the requirement. Therefore, producers cannot write to affected partitions.

However, for some partitions where the leader and replicas are on brokers 1 and 2, the full ISR (3) may still be present (if broker 0 was not a replica). In this scenario, exactly 6 of the 9 partitions may still have ISR=3 depending on replica distribution.

From Kafka documentation:

“When acks=all and min.insync.replicas is not met, the broker will reject the produce request with an exception.”

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 159–161

Apache Kafka Documentation: Topic Config – min.insync.replicas

Event stream processing is ideal for real-time and near-real-time scenarios. Kafka Streams and ksqlDB are commonly used in use cases requiring immediate reaction to data.

A → Correct: Fraud detection systems benefit from low-latency anomaly detection.

B → Correct: Real-time product recommendations based on user activity require continuous processing.

C → Incorrect: End-of-day processing is batch-oriented, not real-time.

D → Correct: Real-time alerting and log anomaly detection are classic streaming use cases.

E → Incorrect: Historical dashboards rely on batch aggregation and are not continuous processing.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 210–215

Confluent Documentation: Use Cases for Kafka Streams and ksqlDB

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A → Correct: Kafka uses ZooKeeper's ephemeral node (/controller) to elect a single controller. If the node disappears (e.g., due to GC pause or failure), a new controller is elected.“Kafka uses an ephemeral znode in ZooKeeper to hold the identity of the controller.” — Apache Kafka docs

B → Correct: The controller assigns partition leaders after startup or during rebalancing.

C → Correct: The controller epoch is a monotonically increasing number used to detect outdated controller messages.“Kafka uses a controller epoch to avoid split-brain situations. A new controller will increase the epoch.”

Incorrect options:

D → Wrong: Broker ID is not used as epoch.

E → Wrong: Only one active controller at a time, not one per broker.

F → Wrong: Partition assignment to consumers is handled by the consumer coordinator, not the controller.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189–192

Apache Kafka Documentation: “Kafka Controller Design”

Kafka ensures message immutability once data is written to disk. It guarantees that the message cannot be altered post-write, ensuring strong integrity.

From Kafka documentation:

“Kafka provides strong durability and immutability guarantees. Once a message is written and acknowledged, it cannot be modified.”

A → Incorrect: Offsets are immutable. Producers cannot overwrite messages.

C → Partially true for performance but not about message integrity.

D → Metadata changes do not affect message content or integrity.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 122–123

Apache Kafka Documentation: Message Durability & Immutability

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