Optimization and Technical Trade Offs Questions

Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.

EasyTechnical

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Describe cache hit rate and miss cost, and explain how you would choose TTL and invalidation strategy for a read-heavy user-profile service where updates happen every few minutes. Include strategies to prevent cache stampede and stale reads during updates.

EasyTechnical

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Explain Big-O, Big-Theta, and Big-Omega notation in the context of backend engineering, and give one concrete example for each (for instance: cache lookup, database scan, and scheduled batch job). Explain why worst-case (Big-O) and average-case both matter when setting SLAs and capacity planning.

MediumSystem Design

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For a mid-size product expecting 500k monthly active users, discuss the tradeoffs of starting with a monolith versus microservices in terms of performance, operational overhead, developer productivity, deployment velocity, and long-term scalability. Provide a recommended approach and a migration strategy if growth requires decomposition.

EasyTechnical

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As a Solutions Architect, how do you balance maintainability and performance when designing an API or backend service? Provide clear principles, decision criteria, and a concrete example where you'd accept lower performance in favor of maintainability and why that trade-off is reasonable.

MediumTechnical

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Compare token bucket and leaky bucket algorithms for API rate limiting. Discuss centralized vs distributed enforcement, accuracy vs latency tradeoffs, memory and storage requirements, and how you'd enforce per-user limits at a scale of 100k RPS across multiple regions.

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