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Experimentation Platforms and Infrastructure Questions

Addresses the technical and organizational infrastructure required to run experiments at scale. Topics include randomization and assignment strategies, traffic allocation, instrumentation and metric collection pipelines, experiment configuration and rollout systems, experiment tracking and metadata, data quality and monitoring, guardrails to detect interference or contamination, automated validity checks, self service experimentation tooling, governance and permissions, and approaches to scale experimentation across many teams while preserving statistical validity. Senior conversations include designing experiment platforms, enabling self service and observability, and trade offs when scaling experiment velocity across products.

MediumTechnical
81 practiced
Describe a monitoring strategy for data quality specifically tailored to experimentation: which indicators to track (e.g., event loss rate, duplicate rate, late-arriving events), alerting thresholds, and a triage checklist for analysts encountering bad data.
HardTechnical
61 practiced
Design and describe an approach for supporting sequential testing in the experimentation platform. Compare frequentist alpha-spending methods and Bayesian stopping rules, explain how to integrate stopping decisions into rollout controls, and discuss how to communicate stopping semantics to experiment owners.
HardSystem Design
68 practiced
Explain how you would implement household- or cluster-level randomization (e.g., for shared devices or households) such that interference is minimized while preserving statistical power. Discuss cluster definition, assignment, and changes to analysis (e.g., cluster-robust SEs).
MediumTechnical
71 practiced
Design a governance and permission model for an experimentation platform that supports self-serve but enforces compliance and auditability. Define roles, permissions, approval workflows, and audit logs necessary for heavy regulated environments.
MediumSystem Design
74 practiced
How would you scale a self-service experimentation platform to hundreds of teams while minimizing false discoveries from multiple concurrent tests? Discuss technical controls, statistical corrections (FDR), and organizational policies you would implement.

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