CCAAK Confluent Certified Administrator for Apache Kafka Questions and Answers

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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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.

A message (also referred to as a record or event) is the smallest unit of data in Kafka. Each message consists of a key, value, timestamp, and metadata. Messages are stored in partitions and are identified by their offsets.

From Kafka documentation:

“The atomic unit of data in Kafka is a message, which includes a key, value, and headers.”

A → Logical grouping of messages.

B → Correct: Base-level data structure.

C → A topic holds partitions, not messages directly.

D → Refers to the position of a message, not the message itself.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 120

Apache Kafka Documentation: Message Structure

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

Apache Kafka's built-in ACL mechanism (SimpleAclAuthorizer) stores ACL data in ZooKeeper. ACLs are propagated to brokers from ZooKeeper and cached in memory for fast lookup.

From Kafka documentation:

“ACLs are stored in ZooKeeper under /kafka-acl and are loaded into each broker at startup and on updates.”

B → Correct: ZooKeeper stores ACLs centrally.

A → Brokers load ACLs but don’t persist them.

C & D → Do not store or manage ACLs.

Page Reference:

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

Apache Kafka Documentation: Authorizer Configuration

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 Replicator does not preserve source cluster offsets. Offsets in the destination DR cluster are generated independently, and there is no guarantee they match those from the source cluster.

From Confluent Replicator documentation:

“Replicator does not guarantee preservation of message offsets between source and destination. Offsets in the destination cluster are newly assigned.”

C → Correct: Offsets may differ even for the same data.

A → Incorrect: Lag is not the issue if offsets inherently differ.

B → Misleading: Kafka does not overwrite committed messages.

D → Invalid: Kafka messages are immutable; no "update" happens.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 313

Confluent Replicator Documentation: “Offset Mapping and Limitations”

Kafka Connect is a framework for connecting Kafka with external systems like databases, key-value stores, file systems, etc. It handles ingestion and egress of data using scalable and fault-tolerant connectors.

From Kafka documentation:

“Kafka Connect is a scalable and fault-tolerant component for efficiently streaming data between Kafka and other systems.”

A → Correct: Purpose-built for data integration.

B → For stream processing, not integration.

C → Manages schemas, not data movement.

D → For HTTP-based Kafka interaction.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 8, p. 239–241

Apache Kafka Documentation: Kafka Connect Overview

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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 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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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 quotas allow administrators to throttle producer and consumer throughput to avoid resource starvation. They are based on network and disk usage metrics, applied per client ID or user.

From Apache Kafka documentation:

“Quotas can be configured to control the rate at which data is produced or consumed to avoid one client from overwhelming the system.”

A → Correct: Prevents a single client from degrading cluster performance.

B → Incorrect: Kafka quotas are not for prioritization but fairness.

C → Incorrect: Quotas do not control connection limits.

D → Incorrect: Partition limits are managed via topic configs, not quotas.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 306–308

Apache Kafka Documentation: Quotas

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In Kafka, a Leader ID of -1 indicates that no broker is currently the leader for that partition. This typically happens when the only replica for the partition (broker 1 in this case) is offline or unavailable.

From Kafka documentation:

“If the leader for a partition is not available, the leader ID will be reported as -1.”

A → Correct: The broker hosting the sole replica is down.

B/C/D → Incorrect and unrelated to leader election behavior.

Page Reference:

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

Apache Kafka Documentation: Topic Description Output and Leader Election

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