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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.

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.
MediumTechnical
0 practiced
You run a multi-arm test with five treatment variants. Explain the multiple comparisons problem and describe at least two strategies (with pros and cons) to control false positives when making decisions across the variants.
MediumTechnical
0 practiced
You plan a multi-treatment experiment and suspect interactions between treatments (non-additive effects). How would you design the experiment and analysis to detect and quantify interaction effects? Discuss sample size implications and multiple-testing concerns.
HardTechnical
0 practiced
Explain strategies for running multiple overlapping experiments (e.g., product A and B running in parallel on the same users) to avoid interference. Discuss crossed experiments, factorial designs, orthogonalization, and how to attribute effects correctly in analysis.
MediumTechnical
0 practiced
Define guardrail metrics for experiments. Give three examples of guardrail metrics, explain why each is important, and propose thresholding rules and monitoring cadence for each to detect unintended harms quickly after launch.

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