## Scenario You are designing an ML-driven **video recommendation** product (home feed + “up next”) for a consumer app. The interviewer focuses heavily on **infrastructure** and **logging/observability**. ## Requirements - Serve personalized recommendations with low latency. - Handle both: - **Home feed** (rank a set of candidates) - **Next video** (contextual/session-based) - Support rapid iteration (new models/features) and safe experimentation. ## What to cover 1. **High-level architecture**: offline training pipeline, online serving, candidate generation + ranking. 2. **Data & logging design**: - What events to log (impressions, clicks, watch time, skips, likes, shares, follows, dwell, scroll depth, etc.) - How to uniquely identify an “impression” and join it to outcomes - How to avoid common logging pitfalls (position bias, missing-not-at-random, duplicate events) 3. **Feature pipelines**: - Batch vs streaming features - Feature store (online/offline consistency) 4. **Model training & evaluation**: - Labels and objectives (CTR, watch time, completion, satisfaction) - Offline metrics and online A/B testing 5. **Serving infra & reliability**: - Latency budget, caching, fallback behavior, graceful degradation - Monitoring, alerting, model/data drift detection 6. **Privacy & compliance** considerations in logging and retention.