Business Context and Metrics Understanding Questions
Understand the broader business context for technical or operational work and identify relevant performance metrics. This includes recognizing the key performance indicators for different functions, translating technical outcomes into business impact, scoping a problem with success metrics and constraints, and using metrics to prioritize trade offs. Candidates should demonstrate how they would frame a problem in business terms before proposing technical or operational solutions.
HardTechnical
0 practiced
Translate the strategic business goal 'increase enterprise ARR by 20%' into a 12-month ML roadmap. List measurable ML objectives, KPIs, resource allocation (team and infra), milestones (discovery, pilot, scale), and risk mitigation steps tied specifically to upsell and churn reduction use cases.
MediumTechnical
0 practiced
Explain how to use funnel analysis (impression → click → add-to-cart → purchase) to identify which stage an ML model should focus on improving. Specify the key metrics at each stage, instrumentation needed, and how to estimate the ROI of moving each stage by a small percentage.
HardTechnical
0 practiced
Propose a metric hierarchy for an ML-driven search product: specify one north-star metric, three leading indicators, and several operational metrics. Explain how you would use this hierarchy to prioritize work, design experiments, and set quarterly OKRs.
EasyTechnical
0 practiced
Given tables users(user_id, signup_date) and purchases(user_id, purchase_date, amount), write a SQL query (Postgres-compatible) to compute 7-day cohort conversion rate by signup week (cohort = DATE_TRUNC('week', signup_date)). Explain assumptions about timezones and users with no purchases and how you'd count unique converted users.
HardTechnical
0 practiced
Define and write the SQL to compute 'Incremental Revenue per Active User (IRPAU)' using tables events(user_id,event_type,event_time), transactions(user_id,amount,transaction_time), and experiment_assignments(user_id,variant,assigned_at). Explain assumptions and how you would estimate incremental revenue causally from experiment data.
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