Distributed Systems -- Practice Problems
Distributed Systems — Practice Problems
10 MCQ questions covering the CAP theorem, consistency models, Paxos/Raft consensus, replication strategies, fault tolerance, and logical clocks. Select an option to check your answer.
Intuition
A team of chefs in different kitchens: Distributed systems are like a team of chefs working in different kitchens trying to prepare the same meal, they need to communicate, agree on the recipe (consensus), handle it when someone’s oven breaks (fault tolerance), and serve the dish at the same time (consistency).
Why it matters: Every large-scale system, Google, Amazon, blockchain, is distributed. Understanding CAP theorem, consensus, and replication explains why these systems behave the way they do and what trade-offs they make.
The key insight: In distributed systems, you can’t have everything, the CAP theorem forces you to choose between consistency, availability, and partition tolerance, and real systems must pick which compromise fits their use case.
CAP Theorem and Consistency Models
Consensus, Replication, and Fault Tolerance
Additional Distributed Systems Questions
Common Mistakes
Confusing consistency with availability: Consistency means all nodes see the same data. Availability means every request gets a response. CAP theorem forces a trade-off during partitions.
Assuming eventual consistency is always acceptable: Eventual consistency may be fine for some applications but not for financial or medical systems. Consider your consistency requirements.
Forgetting that consensus requires majority: Paxos and Raft require a majority of nodes to agree. A minority partition cannot make progress. Don’t assume all nodes can always participate.