Problem Solving Under Constraints Questions

Assess how candidates identify, prioritize, and resolve problems when faced with limited time, limited resources, changing requirements, or unclear information. This includes execution discipline to maintain delivery and unblock teams, pragmatic adaptation of designs or plans to meet constraints, handling ambiguity by making reasonable assumptions and iterating, communicating trade offs and risks to stakeholders, and demonstrating creative but practical solutions that preserve core quality objectives. It also covers applied troubleshooting for realistic business problems such as calculating retention cohorts, reconciling datasets of differing granularity, or debugging data quality and pipeline issues, with emphasis on clearly explaining approach, assumptions, and recovery steps.

EasyTechnical

0 practiced

Your small data science team has three competing high-impact requests all due in two weeks. Explain a prioritization framework you would use to decide which to deliver, how you'd involve stakeholders in the decision, and how you'd document trade-offs and chosen scope so the team can execute with clarity.

EasySystem Design

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A Senior PM needs a one-page dashboard summarizing a new model's performance, top risks, and recommended next actions for an executive meeting tomorrow. You have 4 hours to prepare. Describe which metrics, visualizations, uncertainties, and short notes you would include and explicitly state what you would omit to meet the time constraint. Explain how you'd structure the narrative for non-technical execs.

HardTechnical

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After deployment you detect subtle label leakage that inflated offline metrics — some features were derived from future events. A hiring freeze means you cannot retrain immediately. Propose immediate mitigations to reduce business risk (feature gates, scoring-time filters), a prioritized remediation plan for retraining and model revalidation when possible, and monitoring to detect recurring leakage automatically.

MediumTechnical

0 practiced

A stakeholder requests a new billing metric mid-quarter; implementing it requires data pipeline changes and legal review which will take weeks. Propose a phased plan: an interim approximate metric to satisfy business needs, compliance checks to run in parallel, and the timeline/criteria for rolling out the definitive metric. Include communication to billing and finance teams about risks.

HardTechnical

0 practiced

Design an observational causal analysis to estimate the effect of a 10% price increase on conversion rate using only web logs and transactions with limited history and measurement error. Specify identification strategy (e.g., difference-in-differences, instrumental variables, synthetic controls), key assumptions, robustness checks, sensitivity analyses (e.g., Rosenbaum bounds), and how you'd present uncertainty to product and finance stakeholders.

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