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A company stores web analytics data in a Snowflake table named 'WEB EVENTS. This table includes a 'USER ID column, a 'TIMESTAMP' column indicating when the event occurred, and a 'EVENT TYPE column that captures the type of event (e.g., 'page_view', 'add_to_cart', 'purchase'). The data analysts want to enrich this data to identify the first and last event times for each user. Which Snowflake features or functions would be MOST appropriate and efficient for achieving this enrichment?
Correct Answer: B
Window functions are the most efficient approach for calculating aggregate values (like minimum and maximum) within partitions (in this case, per user) without requiring self-joins or subqueries. Correlated subqueries can be inefficient for large datasets. Stored procedures with iteration are generally slower than set-based operations. Lateral views are more suitable for exploding array structures, not for finding min/max values. A simple GROUP BY would provide the overall minimum and maximum, not per user.