Why distributed patterns solve common challenges in HLD - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why do distributed systems improve fault tolerance?

In a distributed system, why does spreading components across multiple machines improve fault tolerance?

A Because distributed systems avoid network communication, eliminating network failures.

B Because all machines share the same power source, reducing power failures.

C Because distributed systems use fewer resources, so failures are less likely.

D Because if one machine fails, others can continue working, preventing total system failure.

💡 Hint

Think about what happens when one part of a system stops working.

Distributing components means the system does not rely on a single machine. If one fails, others keep the system running, improving fault tolerance.

❓ Architecture

intermediate

How does data partitioning help scalability in distributed systems?

Which explanation best describes how data partitioning (sharding) helps a distributed system scale?

A It compresses data to reduce storage size on a single machine.

B It duplicates all data on every machine to increase read speed.

C It splits data into smaller parts stored on different machines, allowing parallel processing and reducing load on each machine.

D It moves all data to a central machine to simplify access.

💡 Hint

Consider how dividing work helps handle more users or data.

Partitioning data spreads it across machines, so each handles a smaller portion. This allows the system to handle more data and users by working in parallel.

❓ tradeoff

advanced

Tradeoffs of using eventual consistency in distributed systems

What is a key tradeoff when choosing eventual consistency over strong consistency in a distributed system?

A Eventual consistency improves availability but may show stale data temporarily.

B Eventual consistency requires all nodes to update simultaneously.

C Eventual consistency eliminates network delays completely.

D Eventual consistency guarantees immediate data accuracy but reduces system availability.

💡 Hint

Think about the balance between data freshness and system uptime.

Eventual consistency allows the system to stay available even if some nodes are slow or disconnected, but data may not be instantly up-to-date everywhere.

❓ scaling

advanced

How does load balancing improve performance in distributed systems?

Which option best explains how load balancing helps a distributed system handle more users efficiently?

A It distributes incoming requests evenly across servers to prevent any single server from becoming overloaded.

B It duplicates requests to all servers to ensure delivery.

C It queues requests on one server to process them in order.

D It sends all requests to the fastest server to maximize speed.

💡 Hint

Consider how spreading work helps avoid slowdowns.

Load balancing spreads requests evenly, so no server is overwhelmed. This keeps response times low and improves overall system performance.

❓ estimation

expert

Estimating capacity for a distributed messaging system

You design a distributed messaging system expected to handle 1 million messages per minute. Each message is 1 KB. What is the minimum network bandwidth in Mbps needed to support this load without delay?

A Approximately 1000 Mbps

B Approximately 134 Mbps

C Approximately 8 Mbps

D Approximately 16 Mbps

💡 Hint

Calculate total data per second and convert bytes to bits.

1 million messages/min = 1,000,000 / 60 ≈ 16,667 messages/sec. Each message is 1 KB = 1024 bytes = 8192 bits. Total bits per second = 16,667 * 8192 ≈ 136,533,333 bits/sec ≈ 134 Mbps (rounded).