## Scenario You are given a dataset with one input feature **x** and a target **y**. The interviewer asks: “How would you model this?” Later, you are shown a plot with **two distributions** (e.g., distribution of a feature for two groups/classes, or train vs. production) and asked to interpret what it implies. Finally, you are asked several **cold-start** questions. ## Tasks 1. Explain how you decide whether this is **regression vs classification**, what baseline models you try first, and what evaluation metrics you use. 2. Given a plot with two distributions, explain how you would: - Describe what you see (separation/overlap, shift, variance, multimodality) - Diagnose potential issues (label leakage, covariate shift, class imbalance, thresholding) - Decide next steps (feature engineering, calibration, sampling, monitoring) 3. Describe practical **cold start** strategies for: - New users - New items (videos) - New regions/languages Assume you care about both predictive quality and production robustness.