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.

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

Explain what sublinear algorithms are and give three examples used in data science (count-min sketch, locality-sensitive hashing, coresets). For each example, describe space/time complexity, error bounds, and a practical scenario where you would pick it over an exact approach.

MediumTechnical

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A regulation requires storing prediction logs for 3 years for audit purposes, but storage budget is limited. Propose a storage and retention strategy that balances auditability, privacy, cost, and queryability. Consider compression, sampling, hashing PII, tiered storage, and re-computation options.

HardTechnical

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Formulate a constrained optimization problem where you maximize model accuracy subject to p99 latency and memory budget constraints. Discuss solution approaches such as Lagrangian relaxation, penalty methods, Pareto frontier search, and RL-based architecture search. Explain how you would present trade-offs to stakeholders and how to obtain a practical Pareto frontier.

MediumSystem Design

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Design a near real-time feature store for 10 million users that supports 100ms point lookups and 1M updates per minute, and that materializes both online and offline features. Describe storage choices, indexing, consistency guarantees, retention policies, hot/cold tiering, and how you would adapt when QPS or retention requirements change.

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