Confluent CCAAK - Confluent Certified Administrator for Apache Kafka

The linger.ms property defines how long the producer will wait to fill up a batch before sending it. Combined with batch.size, it allows the producer to accumulate more messages, thus improving throughput.

From Kafka documentation:

“If batch.size is not yet reached, linger.ms causes the producer to wait for additional messages before sending the batch.”

A → send.buffer.bytes affects TCP socket size.

B → Correct: Improves batching by adding intentional delay.

C → Reduces bandwidth but not directly batching.

D → A timeout for message delivery, not related to batching.

Page Reference:

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

Apache Kafka Documentation: Producer Config – linger.ms

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Kafka consumers in a consumer group consume partitions exclusively. With 4 partitions and only 2 consumers, each consumer is processing 2 partitions. To increase parallelism, more consumers can be added to the group (up to 4) so that each consumer handles fewer partitions, which reduces the lag.

From Kafka documentation:

“Increasing the number of consumers in a group increases parallelism and helps reduce lag, assuming there are enough partitions.”

A → Ineffective unless followed by rebalancing and consumer scaling.

B → Would create a separate group and not share load.

C → Not applicable; consumers don’t acknowledge messages like producers.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 127–129

Apache Kafka Documentation: Consumer Group Behavior

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Kafka topics support two main cleanup policies:

delete: The default policy where Kafka deletes old log segments based on retention time or size.

compact: Enables log compaction, retaining only the latest value for each key.

From Kafka documentation:

“Valid cleanup.policy values are 'delete' and 'compact'.”

B → Invalid: 'default' is not a recognized cleanup policy.

D → Invalid: 'cleanup' is not a valid keyword in this context.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 196–197

Apache Kafka Documentation: Topic Configs – cleanup.policy

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In Kafka Connect, sink connectors consume data from Kafka topics and send it to external systems (e.g., databases, object storage, search indexes).

From Kafka documentation:

“Sink connectors receive data from Kafka topics and write it to external systems.”

A → Correct: Moves data out of Kafka.

B → Moves data into Kafka.

C → Not a connector type.

D → Not a standard connector classification.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 8, p. 241

Apache Kafka Documentation: Kafka Connect – Sink Connectors

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Kafka supports quotas to limit the broker resources each client can use. This is used to prevent one client from monopolizing cluster bandwidth.

From Kafka documentation:

“Quotas can be applied to both producers and consumers. These quotas are enforced based on the number of bytes per second, and they are applied to ensure fair usage of broker resources.”

A. Quotas → Correct: Admins can define quotas on throughput per client ID or user.

B. Consumer Groups → Incorrect: This controls parallelism in consumption but not resource fairness.

C. Rebalancing → Incorrect: This helps with load balancing but not with resource limitation.

D. ACLs → Incorrect: ACLs define access permissions but not resource usage.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10 (“Administering Kafka”), p. 306–308

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Kafka supports multiple SASL mechanisms for authentication, including:

SASL/PLAIN – Username/password

SASL/GSSAPI – Kerberos-based

SASL/OAUTHBEARER – OAuth 2.0 token-based

From Apache Kafka documentation:

“Kafka supports the following SASL mechanisms: GSSAPI (Kerberos), PLAIN, SCRAM, OAUTHBEARER.”

B → SAML20 is not supported.

E → OTP is not a supported SASL mechanism.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 9, p. 280–284

Apache Kafka Documentation: Kafka SASL Authentication

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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)”.

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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When sizing a ksqlDB cluster, the following factors are crucial:

A. Data Schema: Complex schemas may require more CPU/memory for serialization/deserialization.

B. Number of Queries: Each persistent query consumes system resources (CPU, memory).

C. Number of Partitions: Determines parallelism and state store scalability.

From Confluent documentation:

“Cluster capacity planning depends on schema complexity, query concurrency, and partition count.”

D → Incorrect: Encryption impacts producer/broker, not ksqlDB sizing directly.

E → Incorrect: Retention is managed by Kafka, not ksqlDB.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 225–229

Confluent ksqlDB Documentation: Capacity Planning Guidelines

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