{"blocks": [{"key": "b59a1514", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "4014dbf6", "text": "Facebook wants to understand and curb fake-account activity over the past month and at sign-up time.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "688b29b1", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c188fbb5", "text": "List product or engagement metrics that could serve as proxies for month-over-month changes in fake-account activity. Which user-behavior signals would help distinguish fake from real accounts post-registration? Can we flag a fake account during the registration flow? Propose features and quick checks. Given a prior fake-account rate of 2% and a binomial observation of 18 flags in 600 new accounts, compute the posterior probability the next account is fake (clearly show Bayesian steps). To evaluate a fake-account classifier, would you optimize precision or recall? Discuss pros, cons, and the wider impact on Facebook.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "ce4099ed", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "f021863e", "text": "Frame the Bayesian update, assume Beta-Binomial conjugacy, and relate precision/recall trade-offs to user experience and integrity.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}