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.
MediumTechnical
32 practiced
Compare strategies for handling severe class imbalance when training under compute constraints: random under/oversampling, class-weighted loss, focal loss, and framing as anomaly detection. For each method, discuss computational cost, tuning complexity, expected impact on model calibration, and suitability for short-term production deployment.
EasyTechnical
34 practiced
Explain the difference between Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). For each type, describe the practical implications for model training and a pragmatic prioritization of fixes when you have limited time and resources before productionizing a model (e.g., imputation, dropping data, collecting more data).
EasyTechnical
44 practiced
You have daily sales table (sales_by_day: date, product_id, total_revenue) and hourly web events (web_events: hour_ts, user_id, product_id, event_type). A stakeholder asks for daily conversion rate (purchases / visits) by product within 2 hours. Describe a pragmatic approach to reconcile the granularity mismatch, including SQL/pseudocode steps, assumptions about attribution windows, and how you'd flag uncertainty for stakeholders.
EasyTechnical
35 practiced
You ran an A/B test with limited traffic; observed uplift is not statistically significant but leadership wants to make a decision. Describe how you'd analyze the test under low power, how you'd convey uncertainty and risk to stakeholders, and pragmatic next steps (e.g., extend the test, use sequential testing, adopt a conservative rollout, or perform sensitivity analysis).
MediumTechnical
35 practiced
Design an MVP to forecast weekly demand for a product with a two-week deadline. Constraints: compute budget limited, 2 years of historical data (some seasonality), and need a daily-updating dashboard. Describe feature set, model class, validation approach, expected error tolerances, deployment approach, and how you'll iterate once the MVP is live.
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