You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

Correct Answer: E
Option E is the most efficient. It filters the transactions within the last 30 days, uses a window function to calculate the distinct count of product categories per customer, and then filters to identify customers with more than one category. The use of window functions allows the aggregation to happen within Snowflake, minimizing data transfer and leveraging Snowflake's processing power. distinct at the end makes it unique.