Feature Success Measurement Questions
Focuses on measuring the impact of a single feature or product change. Key skills include defining a primary success metric, selecting secondary and guardrail metrics to detect negative side effects, planning measurement windows that account for ramp up and stabilization, segmenting users to detect differential impacts, designing experiments or observational analyses, and creating dashboards and reports for monitoring. Also covers rollout strategies, conversion and funnel metrics related to the feature, and criteria for declaring success or rollback.
HardTechnical
0 practiced
Design an early stopping policy for a six-week experiment that allows stopping for harm or clear benefit while controlling Type I error. Explain group-sequential methods (for example O'Brien-Fleming or Pocock boundaries), how many interim looks you'd schedule, how you'd derive adjusted p-value thresholds or alpha spending, and practical implementation considerations.
MediumTechnical
0 practiced
You must create a one-screen executive dashboard for a checkout redesign. Propose the top six metrics to include, specify the visualization type for each (e.g., sparkline, KPI card, bar chart), and provide a one-sentence justification for why each metric matters. Consider primary metric, guardrails, and an operational health indicator.
EasyTechnical
0 practiced
You're asked to define the primary success metric for a one-click checkout feature. Describe how you would choose the single primary metric, list two candidate primary metrics with trade-offs (for example conversion rate vs time-to-checkout), and explain how this metric maps to the business objective. Include how you would communicate and defend this choice to product and finance stakeholders.
HardTechnical
0 practiced
Explain the synthetic control method and when it's appropriate to measure the impact of a feature rolled out in a single region. Describe data requirements (donor pool), how the synthetic control is constructed (weights), diagnostics to check pre-treatment fit, running placebo tests, implementation outline (R/ Python packages), and limitations.
HardSystem Design
0 practiced
Design a scalable experiment platform that supports deterministic bucketing, traffic allocation, exposure logging, experiment metadata, and offline metric recomputation. Explain hashing strategy to ensure consistent assignment across services, how to support many variants and multiple simultaneous experiments, how to backfill metrics after instrumentation fixes, and how to version experiments and rollouts.
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