{"blocks": [{"key": "a40eb0e7", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "64e946e0", "text": "Designing a machine-learning-powered recommendation system from data collection to real-time serving.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "07cd3bc3", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "24c58a06", "text": "Design an end-to-end recommendation system. Discuss data collection, feature pipelines, training workflow, model refresh cadence, online/offline architecture, and meeting real-time latency requirements.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "905ab11f", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c5fff01f", "text": "Address feedback loops, A/B testing, and fallback logic.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}