Handling Problem Variations and Constraints Questions

This topic covers the ability to adapt an initial solution when interviewers introduce follow up questions, new constraints, alternative optimization goals, or larger input sizes. Candidates should quickly clarify the changed requirement, analyze how it affects correctness and complexity, and propose concrete modifications such as changing algorithms, selecting different data structures, adding caching, introducing parallelism, or using approximation and heuristics. They should articulate trade offs between time complexity, space usage, simplicity, and robustness, discuss edge case handling and testing strategies for the modified solution, and describe incremental steps and fallbacks if the primary approach becomes infeasible. Interviewers use this to assess adaptability, problem solving under evolving requirements, and clear explanation of design decisions.

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You have an O(n^2) algorithm that works for n up to 10^4. The interviewer increases n to 10^7 and asks you to make it feasible. Describe the systematic steps you'd take to adapt the solution: clarify exact constraints, spot algorithmic bottlenecks, propose faster algorithms or approximations, and discuss trade-offs (accuracy vs time). Give a concrete example of replacing an O(n^2) step with an O(n log n) or O(n) alternative.

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You're asked to implement a search function that tolerates typos. You choose a BK-tree for edit-distance lookups which works well for small alphabets. The interviewer increases scale to millions of indexed strings and tight latency requirements. Propose adaptations (e.g., n-gram indexes, metric-space partitioning, embedding-based approaches), discuss index size and query speed trade-offs, and explain how to evaluate quality of fuzzy matches.

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A solution uses a global lock to protect shared state and works under light load. The interviewer then simulates high contention and asks you to redesign for high throughput on multicore machines. Explain techniques to reduce contention: sharding, lock striping, fine-grained locks, optimistic concurrency, lock-free data structures, and how to measure success. Consider fairness and starvation issues.

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You implemented a shortest-path algorithm for weighted graphs using Dijkstra's algorithm implemented with a binary heap. The interviewer increases constraints and says the graph is extremely sparse but the edge weights are small integers bounded by C (e.g., 0..100). Suggest algorithmic improvements leveraging small integer weights, explain complexity gains, and discuss memory and correctness implications.

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You're implementing a file-processing pipeline that currently reads entire files into memory. The interviewer changes the requirement: files can be arbitrarily large (multi-GB) and must be processed with bounded memory and in streaming fashion, possibly across compressed chunks. Describe concrete streaming strategies, how to handle partial records at buffer boundaries, and how to support fault-tolerant checkpoints for long pipelines.

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