CCAAK Practice Exam Questions with Answers Confluent Certified Administrator for Apache Kafka Certification

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 uses ZooKeeper as its distributed coordination system. ZooKeeper helps manage cluster metadata and leader election.

A ? Correct: Brokers retrieve leader/partition assignments from ZooKeeper.

D ? Correct: ZooKeeper is used to elect the controller broker responsible for cluster-wide administrative tasks.

From Kafka documentation:

“ZooKeeper maintains metadata for Kafka, including broker registration and controller election.”

B ? Incorrect: Broker scaling is not handled via ZooKeeper.

C ? Incorrect: Replication across clusters is not related to ZooKeeper.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189–191

Apache Kafka Documentation: ZooKeeper Integration

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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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Kafka Connect uses internal topics to manage its state. When running in distributed mode, it relies on the following internal topics:

connect-configs — stores connector and task configurations

connect-offsets — stores source connector offsets

connect-status — stores status information (running, failed, etc.) for connectors and tasks

Option A appears misspelled as “connect-confiqs” — it should be “connect-configs”. Assuming it is a typo, the correct answer includes it.

Incorrect options:

connect-distributed and connect-standalone are not internal topics but execution modes for Kafka Connect.

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 8, p. 249

Apache Kafka Documentation: “Kafka Connect - Distributed Mode Configuration”

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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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The kafka-consumer-groups.sh script is used to monitor consumer group offsets and lag. It shows the current offset, log end offset, and lag for each partition consumed by a consumer group.

From Kafka documentation:

“You can use kafka-consumer-groups.sh with the --describe flag to get the offset lag per consumer group.”

A ? Incorrect: No such built-in script.

B ? Correct: Official script for viewing consumer group lag.

C ? Incorrect: Does not exist as a built-in Kafka tool.

D ? Incorrect: Used for partition reassignment, unrelated to lag monitoring.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 308

Apache Kafka Documentation: kafka-consumer-groups.sh usage

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 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

In Kafka, a partition is a fundamental unit of parallelism and storage. Each topic in Kafka consists of one or more partitions, which are ordered, immutable sequences of records. Importantly, partitions themselves are broken down into segments on the disk. According to the official Kafka documentation:

“A partition is made up of one or more segment files on disk. Each segment is a continuous sequence of messages and each segment file is named after the offset of the first message it contains.”(Apache Kafka Documentation, "Log Basics" section).

Each partition resides in its own directory under the log.dirs configuration on the broker. The documentation states:“The log for each topic partition is stored in its own directory under the log.dirs configuration.”

Given these official clarifications:

Option A is incorrectbecause a partition is not represented by a single segment; rather, it can have multiple segments (as log segments are created to manage file sizes and facilitate compaction or deletion).

Option B is correctas it accurately states that a partition consists of multiple segments.

Option C is correctas all segments for a partition reside in a single directory.

Option D is incorrectbecause partition size is determined by the sum of all its segments, not just the largest one.

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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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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