CCAAK Confluent Certified Administrator for Apache Kafka Questions and Answers

Kafka uses a pull model for consumers. The consumer polls the broker for available data. This contrasts with push models (used in traditional messaging systems), where the server pushes messages to consumers.

From Kafka documentation:

“Kafka’s consumers use a pull model, where the consumer requests messages from the broker at its own pace.”

A → Incorrect: Kafka does not push messages.

B → Incorrect: Kafka is a publish-subscribe system, but not the model used for message delivery to consumers.

C → Correct: Consumers explicitly pull messages.

D → Invalid in Kafka terminology.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 126

Apache Kafka Documentation: Consumer Design Overview

Unclean leader election allows Kafka to elect a non-in-sync replica as a new leader when all in-sync replicas are unavailable. This increases availability at the cost of possible message loss (sacrificing reliability).

From Kafka documentation:

“If unclean.leader.election.enable is set to true, a broker that is not in the ISR can be elected as leader, which increases availability but may lead to data loss.”

A → Correct: Improves availability when ISR is empty.

B → Best practice for quorum-based systems like ZooKeeper but not the setting that trades off reliability.

C → Controls batching latency, unrelated to availability.

D → Retention -1 means “retain forever,” which impacts storage, not HA.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 195

Apache Kafka Documentation: Topic Config – unclean.leader.election.enable

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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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In ksqlDB, INSERT VALUES is used to produce data directly into a topic that underlies a STREAM or TABLE. It is a DML command that appends new records into a Kafka topic.

From ksqlDB documentation:

“INSERT VALUES adds data into a stream or table. This data is persisted in the Kafka topic backing the stream or table.”

Option analysis:

A/B → SELECT queries retrieve data but do not write.

D → Defines a table, does not write data.

C → Correct: Actually writes (produces) data to Kafka.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 227–229

Confluent Docs: ksqlDB INSERT INTO and INSERT VALUES Statements

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Kafka topics are logical streams of data, and each topic is split into one or more partitions. Each partition belongs to exactly one topic.

From Kafka documentation:

“A topic is a category of messages, split into partitions. Each partition is an ordered, immutable sequence of messages, and each belongs to only one topic.”

A → Incorrect: A topic can have more than one partition.

B → Correct: Kafka uses partitions to scale topics.

C → Correct: Partitions belong to exactly one topic.

D/E → Incorrect: Invalid Kafka data model.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 121

Apache Kafka Documentation: Kafka Data Model Overview

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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 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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In Apache Kafka, rack awareness can be used to ensure better fault tolerance and availability by spreading partition replicas across brokers in different racks. The broker configuration property that explicitly indicates the rack a broker belongs to is broker.rack.

As per the official Apache Kafka documentation:

“To assign a broker to a specific rack, configure the broker.rack property in server.properties. This informs Kafka of the rack location for the broker.”

This is useful for features like rack-aware partition assignment and leader election. With broker.rack set, Kafka can make intelligent decisions to ensure that replicas are distributed across racks.

Other options:

client.rack is a client-side setting to inform Kafka of the client’s rack for fetching from the closest replica, not for brokers.

zookeeper.rack and kafka.rack do not exist in Kafka’s official configuration.

Page Reference:

Apache Kafka Documentation, “Rack Awareness” section.

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 6 (Kafka Cluster), p. 215.

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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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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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Kafka supports multiple listeners, allowing you to configure different security settings for internal and external traffic. For example, one listener can use PLAINTEXT for internal replication traffic, and another can use SASL_SSL for external client traffic.

From Kafka documentation:

“Kafka allows configuration of multiple listeners to segregate internal and external traffic, each with its own security protocol.”

B → Correct: Enables secure and isolated communication channels.

A → Unnecessary and inefficient.

C → Invalid: A single listener supports only one protocol.

D → Not a Kafka-level configuration.

Page Reference:

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

Apache Kafka Documentation: Security Configuration – Multiple Listeners

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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 a broker is shut down gracefully:

It flushes data from memory to disk to avoid log recovery upon restart.

It also notifies the controller to reassign leadership of the partitions it leads, ensuring high availability.

From Kafka documentation:

“A graceful shutdown will first flush logs to disk and then hand over leadership of partitions to ensure minimal disruption.”

C → Incorrect: Leader election happens only when the controller detects broker loss; it’s not guaranteed on restart.

D → Incorrect: Partition rebalancing is not automatic on broker restart unless manually initiated.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 306

Apache Kafka Documentation: Graceful Shutdown Behavior

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