CCAAK Confluent Certified Administrator for Apache Kafka Questions and Answers

Confluent Schema Registry supports the following schema types for use with Kafka:

Avro

Protobuf

JSON Schema

These schemas are officially supported for subject versioning, compatibility checks, and REST interface integration.

From Confluent documentation:

“The Confluent Schema Registry supports Avro, JSON Schema, and Protobuf formats.”

Incorrect Options:

gRPC is not supported as a schema format.

Thrift is not supported in Schema Registry.

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 208–210

Confluent Schema Registry Documentation: Overview Section (Schema Types)

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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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The linger.ms property defines how long the producer will wait to fill up a batch before sending it. Combined with batch.size, it allows the producer to accumulate more messages, thus improving throughput.

From Kafka documentation:

“If batch.size is not yet reached, linger.ms causes the producer to wait for additional messages before sending the batch.”

A → send.buffer.bytes affects TCP socket size.

B → Correct: Improves batching by adding intentional delay.

C → Reduces bandwidth but not directly batching.

D → A timeout for message delivery, not related to batching.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 162

Apache Kafka Documentation: Producer Config – linger.ms

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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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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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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 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

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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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 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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Event stream processing is ideal for real-time and near-real-time scenarios. Kafka Streams and ksqlDB are commonly used in use cases requiring immediate reaction to data.

A → Correct: Fraud detection systems benefit from low-latency anomaly detection.

B → Correct: Real-time product recommendations based on user activity require continuous processing.

C → Incorrect: End-of-day processing is batch-oriented, not real-time.

D → Correct: Real-time alerting and log anomaly detection are classic streaming use cases.

E → Incorrect: Historical dashboards rely on batch aggregation and are not continuous processing.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 210–215

Confluent Documentation: Use Cases for Kafka Streams and ksqlDB

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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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