Understand the CAP theorem and how Consistency, Availability, and Partition Tolerance interact in distributed systems. Know different consistency models including strong consistency such as linearizability, eventual consistency, causal consistency, and session consistency, and how to apply them to different use cases. Be familiar with consensus protocols and distributed coordination primitives such as Raft and Paxos, quorum reads and writes, two phase commit and when to use them. Understand trade offs between consistency and availability under network partitions, patterns for hybrid approaches where different data uses different guarantees, and the product and developer experience implications such as latency, stale reads, and API contract clarity.
EasyTechnical
0 practiced
Explain quorum reads and writes in replicated storage. Given replication factor N and quorums R and W, state the condition(s) that guarantee strong consistency (no lost writes, reads see latest write). Provide numeric examples (e.g., N=3 with (R=2,W=2) vs (R=1,W=3)) and explain the trade-offs in latency and availability.
MediumTechnical
0 practiced
Discuss the trade-offs of using quorum-based replication with tunable consistency (e.g., Cassandra-style tunable R/W) versus adopting a strong-consistency system (e.g., Spanner). Consider developer productivity, transactional semantics, operational complexity, latency, and typical failure modes.
EasyTechnical
0 practiced
Explain the CAP theorem in your own words. Define Consistency, Availability, and Partition Tolerance, and give one practical example of a distributed system design that prioritizes CP, one that prioritizes AP, and explain what it means to "choose" one over the other during a network partition. Discuss real-world implications for SLAs, user experience, and how you would communicate this trade-off to product stakeholders.
HardSystem Design
0 practiced
Design a key-value service API and internal architecture that supports configurable per-key consistency levels: linearizable, causal, and eventual. Requirements: handle 10k RPS per region, multi-region replication, ability to tune consistency per key, and clear developer ergonomics in the client library. Describe components, client behavior, failure handling, and how you route or replicate data.
MediumTechnical
0 practiced
A follower in your leader-follower replicated store occasionally lags behind the leader, causing stale reads when clients are routed to it. Describe four strategies to reduce stale reads (e.g., read-from-leader, version checks, client-side staleness tolerance, read-repair) while minimizing impact on read throughput and tail latency. For each, describe trade-offs and best-use scenarios.
Unlock Full Question Bank
Get access to hundreds of CAP Theorem and Consistency Models interview questions and detailed answers.
