CCAAK Sample Questions Answers

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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Apache Kafka supports several security protocols for configuring broker listeners, which define how clients connect to brokers securely or insecurely. As per the official Apache Kafka documentation (“Security Overview” and “Broker Configuration” sections):

PLAINTEXT:“PLAINTEXT – Unauthenticated, unencrypted connections.”This is the default protocol when no security is configured.

SSL:“SSL – SSL authentication and encryption.”It provides secure communication between clients and brokers using SSL/TLS.

SASL_SSL:“SASL_SSL – SASL authentication and SSL encryption.”This combines SASL-based authentication with SSL encryption for secure and authenticated connections.

SASL_PLAINTEXTis also supported, but it was not listed in your options.

Option C (SASL)is not a valid protocol on its own—it’s a mechanism used within SASL_PLAINTEXT or SASL_SSL.

Option E (GSSAPI)is a SASL mechanism (often used with Kerberos), not a protocol.

Therefore, the correct answers are:

A. PLAINTEXT

B. SSL

D. SASL_SSL

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 9 (“Kafka Security”), p. 282–283.

Apache Kafka Documentation, “Security Overview” and “Configuring Broker Listeners”.

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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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

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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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

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 architecture maintains exactly one active Controller at any given time, regardless of the number of brokers. The Controller is responsible for administrative tasks like partition leader election and topic creation. The remaining brokers are replicas, ready to take over if the Controller fails.

From Kafka documentation:

“At any given time, there is only one active controller in the Kafka cluster.”

Even in a cluster with 4 brokers, only 1 broker acts as the Controller.

Page Reference:

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

Apache Kafka Documentation: Controller Role Description

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Kafka relies on ZooKeeper heartbeats to track broker health. If the broker JVM is unresponsive due to long GC pauses, ZooKeeper won’t receive timely heartbeats and will mark the broker as dead.

From Kafka Documentation:

“If the garbage collection pause is too long, the broker will not send heartbeats to ZooKeeper and will be considered dead. Leadership and partition reassignments will then occur.”

B → Correct: Broker marked dead, causes partition leadership transfers.

A → Incorrect: Log cleaner thread is unrelated to JVM pauses.

C → Incorrect: Clients may be impacted, but this is not the first result.

D → Incorrect: This is a normal OS function, not caused by GC issues.

Page Reference:

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

Kafka Official Docs: "ZooKeeper Timeout and Broker Health"

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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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Kafka supports topic deletion via the kafka-topics.sh tool if the broker property delete.topic.enable=true. This is the recommended and cleanest method to delete a topic and its data from the cluster.

From Kafka documentation:

“To delete a topic, use kafka-topics.sh with --delete flag, provided delete.topic.enable is set to true on the broker.”

A → Correct: Official method.

B → Misunderstood use of null keys (used for compaction).

C → Not recommended; causes inconsistent state.

D → Deletes consumer state, not the topic itself.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 198

Apache Kafka Documentation: Topic Deletion

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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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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.