Feature Analysis and Launch Evaluation Questions
Designing and applying evaluation frameworks to measure feature success and inform launch decisions. Topics include defining success metrics, experimentation design and basic A over B testing concepts, setting evaluation timeframes, identifying confounding factors, cohort and funnel analysis, instrumentation requirements, and how to iterate based on results. Candidates should be able to propose metrics, describe trade offs in evaluation design, and explain how launch evaluation influences product prioritization.
MediumTechnical
0 practiced
Design a cohort retention analysis using survival analysis concepts. Describe what a Kaplan-Meier curve shows, how to handle censoring, and how you would test difference in survival between control and treatment cohorts.
HardTechnical
0 practiced
Describe an offline evaluation pipeline to backtest a recommendation model before deploying personalization features. Include dataset splitting strategies, metrics to approximate online business metrics (e.g., NDCG vs CTR vs revenue), and how to simulate online effects like position bias or feedback loops.
EasyTechnical
0 practiced
Define type I and type II errors in the context of feature experiments. Give an example of each (for example, shipping a harmful feature or failing to ship a beneficial feature) and describe practical ways a data scientist can reduce each type of error when designing tests.
MediumTechnical
0 practiced
Given a funnel dataset, describe how you would prioritize which funnel steps to optimize. Provide at least three quantitative signals (e.g., absolute loss, potential revenue impact, elasticities) and explain how you would combine them into a recommendation for engineering investment.
EasyTechnical
0 practiced
A product team asks you to instrument a new feature. List the minimal events and properties you would require in the event schema to support feature evaluation and future analysis. Explain why each event/property is necessary and how it supports experimentation, cohorting, and funnel analysis. Include identifiers, timestamps, and any privacy considerations.
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