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

0 practiced

A model that performed well in development now shows degraded performance when the input dataset grows and feature cardinality increases. Describe step-by-step how you would investigate and adapt preprocessing, feature engineering, model choice, and training strategy to restore performance under the new scale constraint.

HardTechnical

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The product team requires enforcing demographic parity across a protected attribute while minimizing drop in overall accuracy. Formulate this as a constrained optimization problem, compare pre-processing, in-processing and post-processing mitigation techniques, and discuss how you would evaluate trade-offs and ensure legal or ethical compliance.

MediumTechnical

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You have a large pool of unlabeled data and limited labeling budget. Describe practical approaches to train useful models under these constraints: weak supervision, pseudo-labeling, semi-supervised learning, active learning, and how you'd evaluate and monitor model quality over time.

MediumTechnical

0 practiced

You need to run multiple A/B tests simultaneously on related metrics with limited traffic. Explain statistical challenges (multiple comparisons, peeking, power) and propose a testing strategy that controls false positives while using traffic efficiently (consider sequential testing, alpha spending, hierarchical testing, or false discovery rate control).

EasyBehavioral

0 practiced

Tell me about a time you had to change a model or analytic approach because new constraints emerged mid-project (time, data, compliance, latency). Use the STAR format and emphasize how you clarified the constraint, chose a modified approach, and communicated trade-offs and monitoring changes.

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