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Privacy-Preserving Analytics and Experimentation Questions

Doing measurement and data science without over-collecting or exposing individuals: privacy-preserving experiment design, aggregate and on-device measurement, and privacy-respecting attribution. Covers techniques for analytics and A/B testing that limit personal-data use and honor consent. Includes reconciling measurement quality with privacy constraints.

MediumTechnical
149 practiced
Privacy regulations require excluding certain users from aggregated reports. Describe how you would implement privacy-aware reporting for product metrics across Meta's apps. Address consent flags, differential privacy or aggregation thresholds, and how to communicate limitations to stakeholders.
HardTechnical
78 practiced
A compliance request requires that any aggregated result shown to end users must not expose groups smaller than k=5 individuals. As a BI analyst, propose SQL-level strategies to produce aggregates that comply: suppression of small groups, coarsening/binning to ensure minimum group size, adding noise or differential privacy approaches, and describe performance trade-offs and dashboard UX impacts.
MediumTechnical
75 practiced
Medium: A stakeholder requests a bespoke metric that requires joining customer support tickets, billing events, and product events. Outline the privacy, PII, and compliance considerations you would review before building this metric and how you would design access controls around it.
HardTechnical
89 practiced
Design an analytics architecture that allows BI reporting while complying with GDPR/CCPA when using pseudonymized user identifiers. Address: separation of identity and analytics layers, data minimization, access controls, auditability for deletion or subject access requests, and how to handle historical aggregates needing deletion.
MediumTechnical
99 practiced
Describe how Airbnb's BI and data engineering teams should balance personalization (which relies on user-level signals) with privacy and regulatory compliance. Propose technical controls, governance checks, and reporting practices that preserve analytic value while protecting user data.

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