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You are tasked with analyzing website traffic patterns using Snowflake. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE) and 'PAGE_VIEWS (NUMBER). You need to identify anomalies (unusually high or low traffic days) using the 'STDDEV POP function to calculate the standard deviation and flag days that fall outside a certain number of standard deviations from the mean. Which of the following SQL queries BEST implements this anomaly detection logic, flagging days with page views more than 2 standard deviations above the mean?
Correct Answer: D
Option D correctly uses window functions to calculate the average and standard deviation across the entire dataset without grouping, then filters the rows where 'PAGE_VIEWS' are more than 2 standard deviations above the mean. Window functions (OVER ()) allow aggregate calculations without collapsing the rows, making it suitable for anomaly detection on a row-by-row basis. Option A and C incorrectly tries to use aggregate function without group by condition, and option B without CTE its creating ambiguity. Option E has logical issue.