Complexity Analysis and Tradeoffs Questions

Evaluating algorithmic complexity and engineering trade offs between time, space, maintainability, and operational cost. Candidates should be able to express complexity using big O notation, reason about amortized cost, identify bottlenecks, compare alternative solutions and explain when to optimize or accept higher complexity. Include discussion of measurement, profiling, and pragmatic trade offs such as caching versus recomputation or memory versus latency.