{"blocks": [{"key": "236bbfeb", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "bf9924b6", "text": "Predicting whether an enterprise customer will renew their Google Meet contract.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "af64afd0", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "082b9f90", "text": "Describe how you would build a model to predict customer retention. Which features would you engineer and why? When would logistic regression be sufficient and when would you prefer more complex models (e.g., GBM, NN)? How would you evaluate and compare model performance?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2f1ae63e", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "f2693665", "text": "Cover sampling window, target definition, feature importance, calibration, AUC, business lift, interpretability vs performance trade-off.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}