CCAAK Exam Dumps - Confluent Certified Administrator for Apache Kafka

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

‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾

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

‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾

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 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

‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾

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.

‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾