Experimentation and Product Validation Questions
Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.
EasyTechnical
0 practiced
Explain statistical power and why it matters when evaluating small effect-size model updates. What inputs are required to compute power for a binary conversion metric, and what practical heuristics would you use when variance estimates are uncertain or historical data is limited?
MediumSystem Design
0 practiced
Design an experimentation platform for ML model variants that supports A/B tests, feature flags, deterministic per-user bucketing, metric ingestion, real-time dashboards, safe rollouts, and automated rollback. Assume 100M daily active users and 5M daily experiment events. Outline core components (assignment service, event ingestion, metrics pipeline, storage), data schema sketches, rollout controls, monitoring features, and reliability considerations.
EasyTechnical
0 practiced
Explain practical differences between offline evaluation metrics (validation loss, BLEU, F1) and online A/B metrics (engagement, retention, revenue) when validating ML models. Provide two realistic scenarios where offline metrics would be misleading for product impact and describe experiment designs or logging that would detect such mismatches.
MediumTechnical
0 practiced
Compare frequentist and Bayesian approaches for A/B testing in production. Discuss decision rules and interpretation differences, how each handles sequential peeking, and which approach you would favor for incremental ML feature rollouts in a high-velocity environment. Include practical trade-offs such as priors, stakeholder interpretation, and engineering complexity.
MediumTechnical
0 practiced
Implement in Python a function that determines whether to stop an experiment early using an alpha-spending rule (choose either Pocock or O'Brien-Fleming). Inputs: current z-score, total_number_of_looks, current_look_index, overall_alpha. Return a boolean 'stop' and the critical threshold for this look. Document limitations and assumptions in comments.
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