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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You need to implement incremental training for a model with very large historical data that cannot be fully reprocessed daily. Describe an architecture for incremental updates, data retention, and validation, including how to avoid drift accumulation and how to roll back problematic incremental updates.

HardTechnical

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Training with mixed precision (fp16/amp) leads to NaNs and exploding loss on a complex model. Walk through diagnostic steps and fixes you would attempt (loss scaling, gradient clipping, optimizer changes, verifying batchnorm/statistics) to restore numerical stability.

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 model originally designed for batch inference (large micro-batches) must be converted to support low-latency single-request streaming. Describe required changes to feature computation, serving infrastructure, and model artifact choices to support low p99 latencies.

EasyTechnical

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Your inference cluster occasionally loses the model process. Design a simple fallback strategy that guarantees service continuity with minimal accuracy loss: include steps for detection, routing, and graceful degradation.

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