The Confluent Certified Administrator for Apache Kafka (CCAAK)

Let’s break it down step by step using official Kafka architecture principles:

Total partitions: 10

Each partition stores: 25 GB per day

Total data for the topic per day (no replication): 10 partitions × 25 GB = 250 GB

Replication factor: 3 → three copies of each partition across the brokers

Each partition is replicated across three brokers. Since the replication factor is three, each broker will have one full replica of one-third of the partitions. In a balanced cluster with three brokers:

Each broker stores a replica of every partition (since replication factor = number of brokers).

So, each broker will store the full 250 GB of data for that topic.

Hence:

Each broker will consume: 250 GB

This is confirmed in the official Apache Kafka documentation:

“A replica is a fully-qualified copy of a partition. When the replication factor equals the number of brokers, each broker contains a full copy of the topic data.”

“Each broker stores the total size of data for the topic, regardless of which partitions it leads.”

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 6 (Kafka Cluster), p. 209–210.

Apache Kafka Documentation, “Replication” and “Storage” sections.

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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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Kafka's Controller is a broker that also performs controller duties. It is not a dedicated node. Therefore, it continues to host topics, partitions, and leaders just like any other broker.

From Kafka documentation:

“The controller is one of the brokers in the cluster and continues to perform all normal broker functions.”

A → Correct: The controller is also a normal broker with partitions.

B/C/D → Incorrect: Replication and consumer fetches are not tied to the controller.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 190

Apache Kafka Documentation: Kafka Controller Behavior

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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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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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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 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 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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