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Privacy Advocacy and Business Tradeoffs Questions

Covers the ability to champion user privacy within an organization while understanding and partnering with business priorities. Candidates should demonstrate how they explain privacy risks in business terms such as financial exposure, reputational harm, and regulatory compliance, and how they build the business case for privacy through risk mitigation, customer trust, and long term brand value. This topic includes designing privacy aware solutions that are legally and technically feasible, proposing phased or alternative implementations and mitigations that balance privacy and product goals, and prioritizing privacy work against other investments using risk based frameworks. Candidates should show how they quantify tradeoffs and opportunity costs, build coalitions across product, engineering, legal, and leadership, influence and negotiate with stakeholders, escalate when appropriate, and persist with evidence based arguments. They should avoid false dichotomies by finding pragmatic compromises, propose concrete privacy preserving controls such as data minimization, pseudonymization, selective retention, and encryption, and support organizational decisions once the appropriate authority has decided.

MediumTechnical
0 practiced
Write a SQL query (PostgreSQL) to compute monthly retention cohorts (users active in month N retained in month N+1) using a pseudonymized user identifier. Schema:
user_events(user_hash text, occurred_at timestamp)
Ensure the query avoids exposing raw user identifiers and explain tradeoffs (deterministic hashing vs salted hashing).
HardTechnical
0 practiced
You're auditing a third-party analytics SDK suspected of collecting unexpected PII. As the data-science lead, design a test harness and instrumentation strategy: synthetic user profiles, network and telemetry capture, triggered events, and heuristics to detect unexpected fields. Describe escalation steps if leaks are found.
MediumTechnical
0 practiced
Case study: The product team wants to use social-graph data (friendship edges) for recommendations. Legal raises concerns about sensitive relationship inference. Propose a phased rollout plan (pilot → scale) including privacy controls, DPIA checkpoints, measurement plan, and governance stops for go/no-go decisions.
HardTechnical
0 practiced
You must evaluate whether GAN-based synthetic data is a safe replacement for sharing raw data with a third-party analytics vendor. Design a test suite that measures (a) statistical fidelity for downstream model tasks, (b) privacy leakage risks (membership inference, nearest-neighbor reproduction), and (c) reproducibility/provenance. Describe acceptance thresholds and remediation if tests fail.
EasyTechnical
0 practiced
List and briefly explain the GDPR principles most relevant to a data scientist building predictive models (e.g., purpose limitation, data minimization, storage limitation, accuracy). For each principle write one concrete action you'd take during model development to comply.

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