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You are analyzing customer order data in Snowflake. The 'orders' table has columns: and 'order_totar. Your task is to identify the top 5 customers who have consistently placed high-value orders over time. You need to rank customers based on their average order total, but only consider customers who have placed at least 10 orders. Furthermore, you want to account for the recency of orders by applying a weighted average where more recent orders contribute more to the average. Which of the following approaches will efficiently achieve this goal in Snowflake?
Correct Answer: B,E
Both options B and E correctly address the problem. B calculates a weighted average, filters based on the minimum order count, and then ranks customers based on the weighted average. E achieves the same result in using QUALIFY which is an important technique to filter. Option A doesn't account for weighting. C is inefficient and does not leverage Snowflake's processing power. D is unnecessarily complex with join, date series and subquery for simple operation that can be achieved using window functions.