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.

EasyTechnical

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When an interviewer introduces a new constraint mid-problem (for example: limit memory to 128MB, require 100ms inference latency, or change optimization objective from accuracy to recall), describe your structured step-by-step process you would follow to: 1) clarify ambiguous parts, 2) analyze impact on correctness and complexity, 3) propose concrete modifications to algorithms or models, and 4) communicate trade-offs and a fallback plan. Provide a short checklist you would use in interviews.

MediumTechnical

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Your production model shows slow degradation over months due to changing user behavior (concept drift). Describe methods to detect drift (statistical tests, performance monitoring) and outline adaptation strategies (online learning, importance weighting, periodic retraining, ensembles of specialists), including pros/cons, testing, and rollback strategies.

HardSystem Design

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Design a production ML inference platform that must serve 1 million requests per second with p99 latency under 10ms and allow hourly model updates. Provide architecture covering model storage and versioning, autoscaling strategies, caching and batching, consistency during rollouts, A/B testing, warmup of new models, hardware choices (CPU/GPU/TPU), and monitoring/alerting. Discuss trade-offs and fallback strategies.

MediumTechnical

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You observe heavy-tailed numeric features that create outliers dominating training. Propose transformations, robust scaling, and modeling strategies to handle heavy tails, and explain trade-offs in interpretability and downstream impact on metrics.

EasyTechnical

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When requirements change (for example stricter latency, extreme class imbalance, or higher cost of false positives), how do you select evaluation metrics and reporting to stakeholders? Give three concrete scenarios and the metric choices you would recommend, plus how you would convey trade-offs.

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