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.

HardSystem Design

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Design a distributed training architecture for training a 100-billion-parameter transformer over a cluster of 128 nodes. Discuss parameter sharding (ZeRO), model vs pipeline vs tensor parallelism, optimizer state management, network topology assumptions, and fault tolerance mechanisms.

MediumTechnical

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You have a limited compute budget for hyperparameter tuning. Describe efficient hyperparameter search strategies (e.g., Bayesian optimization, multi-fidelity, Hyperband) you would use, how to allocate budget between model families, and how to detect early poor trials.

MediumTechnical

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A few customers complain that the new recommendation model makes unexpected, out-of-distribution suggestions only for a small but important user segment. Describe how you would triage, reproduce, and fix the issue, including instrumentation, data collection, and short-term vs long-term fixes.

MediumTechnical

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Your production system must support A/B testing of multiple model variants under a constrained traffic split. Describe how you would design the experiment allocation, ensure statistically meaningful results with limited traffic, and handle multiple hypothesis corrections and early stopping.

HardTechnical

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You must convince non-technical stakeholders to accept replacing an expensive model with a cheaper alternative that lowers inference cost by 60% but reduces overall accuracy by 2% while improving latency. Draft a concise stakeholder-facing case including experiments to run, KPIs to report, risk/mitigation, rollback criteria, and a timeline.

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