Start by clarifying requirements and scale, then walk through the end-to-end system architecture covering experiment definition, randomization, exposure logging, and metric computation. Emphasize the importance of unit consistency, deterministic bucketing, and statistical rigor, and discuss how to handle incremental rollout and guardrails.
Pro tip: Highlight the trade-offs between different bucketing strategies (e.g., hash-based vs. deterministic) and explain how you would validate the randomization and detect biases early. Mention the need for a centralized experiment configuration service and real-time monitoring for guardrails.
Ask about expected scale (users, experiments, metrics), latency requirements, and whether the platform is for internal or external use. This shapes design decisions.
Define how experiments are configured (e.g., via a UI or API) and how users are assigned to variants using deterministic hashing (e.g., MurmurHash) with a consistent unit (user ID, cookie, etc.).
Describe how to log exposures (when a user sees a variant) and compute metrics in near real-time or batch, ensuring data quality and handling late-arriving data.
Explain how to gradually ramp up experiments, monitor guardrail metrics (e.g., latency, error rates), and automatically halt if thresholds are breached.
Discuss statistical tests (e.g., t-test, sequential testing), power analysis, and diagnostics like sample ratio mismatch (SRM) and novelty effects.
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