Scalability and Future Extension Questions
Design systems that scale: handle 10 items, 1000 items, 10,000 items efficiently. Design for future feature additions without major refactoring. Use abstraction and interfaces to allow flexibility. Discuss how your solution would adapt if requirements changed. This shows you think beyond the immediate requirement.
EasyTechnical
36 practiced
Explain API versioning and list common strategies (URI-versioning, header-versioning, content negotiation/accept headers). For each strategy describe impacts on client compatibility, caching (CDNs), discoverability, and how that strategy supports future non-breaking extensions.
MediumTechnical
31 practiced
You must introduce a new required field into a public API while keeping older clients working. Describe concrete compatibility strategies (default values, opt-in headers, different versioned endpoints), server behavior to tolerate old clients, testing approaches, and a rollout plan to avoid breaking consumers.
EasyTechnical
32 practiced
How do feature flags support progressive rollout, A/B testing, and future extension of functionality? Describe types of flags (boolean, percentage rollout, targeted), their lifecycle (create → monitor → remove), and governance to avoid technical debt.
HardSystem Design
33 practiced
Architect a strategy to roll out a breaking change in a widely-used microservice API that can't be instantly migrated. Explain how to run two API versions in parallel, route traffic gradually, use adapters/proxies to translate between versions, employ consumer-driven contract testing, and minimize operational overhead while both versions coexist.
MediumTechnical
35 practiced
Implement a thread-safe in-memory token bucket rate limiter in Java or Python that supports per-user limits. Provide the public interface (e.g., initialize(capacity, refillRate), allow_request(user_id) -> bool), ensure O(1) request checks, and design the storage abstraction so it can be swapped for Redis later. Include concurrency considerations.
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