{"blocks": [{"key": "e27c23b2", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "84685f19", "text": "Meta DSPA analytics exercise – evaluating engagement via comment activity.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2c709535", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "514cb917", "text": "You are given post, comment, and user tables. How would you analyze the user-comment distribution to understand engagement? Which core metrics would you define and what statistical or experimental steps would you take if a new comment feature is launched?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "4fab4e2f", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3e302e26", "text": "Think about comments per DAU, long-tail distribution, Gini, percentiles; pre/post comparison or A/B test to isolate causal impact.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}