##### Scenario You are a data scientist at Meta. Fake accounts (bots, spam, scams, impersonation, coordinated inauthentic behavior, and compromised legitimate accounts) are a persistent problem across the platform's social and social-commerce surfaces. ##### Question Design an end-to-end machine learning system to identify fake accounts in the user base, then reason about evaluation, operations, and business impact: 1. **Approach and modeling.** Describe a full approach to identify fake accounts. Outline how you would build and train a model, including the features you would engineer and whether you would use supervised, unsupervised, or semi/weakly-supervised methods. 2. **Evaluation metrics.** Which evaluation metrics would you monitor, and how would you prioritize them given the precision–recall trade-off and the business cost of false positives vs. false negatives? 3. **Drift monitoring.** Once deployed, how would you monitor the model for data drift and concept/performance drift, and how would you decide when to retrain? 4. **Ecosystem impact.** What is the impact of fake users on the overall ecosystem — buyers, sellers, and ads/measurement? 5. **Interpreting a finding.** Suppose an analysis shows that 3% of accounts are fake. What conclusions or next steps does this number suggest? ##### Hints Discuss labeling and class imbalance, precision–recall trade-offs, supervised vs. unsupervised methods, cost-sensitive thresholds tied to enforcement actions, graph/network signals, A/B testing for business impact, and how account-level prevalence can differ from activity-level prevalence.