CCAAK Dumps with Practice Exam Questions Answers

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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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 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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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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Event stream processing involves the transformation, filtering, enrichment, and joining of data as it streams through Kafka. Kafka provides two core technologies for this:

Kafka Streams: A client library for building applications and microservices that process and analyze data stored in Kafka.

ksqlDB: A SQL-based interface for building real-time applications that run continuous queries on Kafka streams.

From Confluent documentation:

“Kafka Streams and ksqlDB are the primary technologies for stream processing on Kafka.”

Schema Registry (A) provides schema storage and validation.

Confluent Replicator (D) is used for mirroring topics across clusters, not processing.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 211–222

Confluent Docs: ksqlDB Overview and Kafka Streams Developer Guide

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