Free Confluent CCAAK Practice Exam with Questions & Answers

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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To support gradual migration to SSL, Kafka supports configuring multiple listeners. You can run both a PLAINTEXT listener (for legacy clients) and an SSL listener (for new clients), allowing dual access.

From Kafka documentation:

“You can configure multiple listeners on a broker with different security protocols. This allows rolling out SSL while supporting legacy connections.”

A → Incorrect: Modifying the current listener breaks current clients.

B → Misleading: Changing advertised listeners affects how clients connect, which breaks backward compatibility.

C → Correct: Dual listeners allow phased SSL migration.

D → SSL must be configured on both ends to be effective.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 305

Apache Kafka Documentation: Configuring Multiple Listeners

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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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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”

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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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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Kafka’s default authorizer implementation is the SimpleAclAuthorizer, which stores ACLs in ZooKeeper and reads them on each access.

From Kafka documentation:

“Apache Kafka includes an out-of-the-box authorizer implementation that stores ACLs in ZooKeeper.”

A → RBAC is Confluent-specific and not stored in ZooKeeper.

B → Correct: Built-in ACL mechanism uses ZooKeeper.

C → Ranger is an external plugin, not bundled with Apache Kafka.

D → LDAP is used for authentication, not authorization.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 9, p. 285

Apache Kafka Documentation: ACLs and Authorizers

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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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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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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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