Free Practice Questions for Confluent CCAAK Exam

Kafka Connect allows parallelism via tasks. The setting tasks.max=1 limits the connector to a single task, meaning it can only process one table at a time. To increase throughput and reduce lag, the number of tasks should be increased, allowing parallel processing of multiple tables.

From Kafka documentation:

“A single connector can have multiple tasks to maximize parallelism. Increasing tasks.max allows the connector to process more data in parallel.”

A → Increasing Connect nodes won’t help unless more tasks are configured.

B → Correct: Enables parallelism for each table.

C → Unrelated to source connector lag.

D → Increasing replication factor doesn’t improve connector performance.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 8, p. 245–246

Apache Kafka Documentation: Kafka Connect Concepts – tasks.max

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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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Kafka's Controller is a broker that also performs controller duties. It is not a dedicated node. Therefore, it continues to host topics, partitions, and leaders just like any other broker.

From Kafka documentation:

“The controller is one of the brokers in the cluster and continues to perform all normal broker functions.”

A → Correct: The controller is also a normal broker with partitions.

B/C/D → Incorrect: Replication and consumer fetches are not tied to the controller.

Page Reference:

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

Apache Kafka Documentation: Kafka Controller Behavior

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Kafka does not enforce a single compression format per topic or partition. Each producer can independently choose whether to compress its messages and what compression type to use (e.g., gzip, snappy, lz4). These compressed messages coexist with uncompressed ones in the same topic.

From Kafka documentation:

“Producers can individually configure compression settings. Kafka stores messages in the format they are received, so compressed and uncompressed messages can exist in the same partition.”

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 164–165

Apache Kafka Documentation: Producer Config – compression.type

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Kafka performs an authorization check every time a client attempts to access a resource (topics, consumer groups, etc.), ensuring real-time enforcement of security policies.

From Kafka documentation:

“Kafka checks authorization for every operation, every time the resource is accessed.”

This behavior ensures that any changes to ACLs are applied immediately and consistently.

Page Reference:

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

Apache Kafka Documentation: “Kafka Security - Authorization”

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Partitions are Kafka’s core mechanism for scaling and distributing load. Each partition of a topic is distributed across brokers. Producers and consumers interact with specific partitions, and Kafka automatically balances those partitions across available brokers.

From Kafka documentation:

“Partitions allow Kafka to parallelize data and distribute load across brokers and clients.”

A → Correct: Distributes data and load

B → Incorrect: Replicas are for fault tolerance, not load balancing

C → Incorrect: Offsets track read progress, not distribute load

D → Incorrect: Connectors are part of Kafka Connect, not used for client balancing

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 132–134

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