Message brokers (Kafka, RabbitMQ) in Microservices - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Message Broker Roles

Which of the following best describes the primary role of a message broker like Kafka or RabbitMQ in a microservices architecture?

A It facilitates asynchronous communication by routing messages between services.

B It directly processes user requests and returns responses synchronously.

C It stores large files and serves them to clients on demand.

D It manages database transactions and ensures ACID compliance.

💡 Hint

Think about how services talk to each other without waiting for immediate replies.

Message brokers enable services to send messages asynchronously, decoupling the sender and receiver. This improves scalability and fault tolerance.

❓ Architecture

intermediate

Choosing Between Kafka and RabbitMQ

You need to design a system that processes a high volume of streaming data with strict ordering guarantees. Which message broker is more suitable and why?

A Kafka, because it is optimized for high-throughput, ordered, and durable event streaming.

B Kafka, because it uses push-based delivery to consumers for low latency.

C RabbitMQ, because it stores messages on disk for long-term archival.

D RabbitMQ, because it supports complex routing and flexible message patterns.

💡 Hint

Consider which broker is designed for streaming large volumes of ordered data efficiently.

Kafka is designed for high-throughput, ordered event streaming with durable storage, making it ideal for streaming data pipelines.

❓ scaling

advanced

Scaling Message Brokers for High Availability

Which approach best ensures high availability and fault tolerance in a Kafka cluster?

A Use RabbitMQ instead because Kafka cannot replicate data.

B Use multiple Kafka brokers with replication factor greater than one for each partition.

C Deploy a single Kafka broker with multiple partitions on one server.

D Disable replication and rely on client retries to handle failures.

💡 Hint

Think about how Kafka keeps copies of data to survive broker failures.

Kafka achieves high availability by replicating partitions across multiple brokers. If one broker fails, replicas serve the data.

❓ tradeoff

advanced

Tradeoffs Between Push and Pull Messaging Models

RabbitMQ uses a push model to deliver messages to consumers, while Kafka uses a pull model. What is a key tradeoff of the pull model used by Kafka?

A Pull model reduces consumer control over message flow, causing overload.

B Pull model increases latency because consumers must request messages actively.

C Pull model allows consumers to control consumption rate, improving backpressure handling.

D Pull model requires brokers to maintain consumer state, increasing complexity.

💡 Hint

Consider how consumers manage message flow and avoid being overwhelmed.

Kafka's pull model lets consumers fetch messages at their own pace, enabling better handling of slow consumers and backpressure.

❓ estimation

expert

Estimating Kafka Cluster Capacity

You expect to handle 30,000 messages per second, each 1 KB in size, with a replication factor of 3 and retention of 7 days. Approximately how much storage capacity (in TB) should your Kafka cluster have to store all replicas?

A About 18 TB

B About 1.8 TB

C About 378 TB

D About 54 TB

💡 Hint

Calculate total data per day, multiply by retention days and replication factor, then convert to TB.

30,000 messages/sec × 1 KB = 30 MB/sec (≈0.03 GB/sec). Per day: 0.03 GB/sec × 86,400 sec ≈ 2.6 TB raw data. With replication factor 3: 2.6 × 3 ≈ 7.8 TB/day. For 7 days retention: 7.8 × 7 ≈ 54 TB total storage for all replicas.