
Explanation:
The correct answer is A, as it accurately creates a CTE named 'active_orders' that includes all orders with an 'order_date' that is on or before the current date, effectively filtering out any future orders. This is achieved by using the 'CURRENT_DATE()' function to compare the 'order_date' column. The query then counts the number of active orders using the COUNT(*) function. Options B, C, and D either exclude orders from today (B), only include orders from today (C), or incorrectly include future orders (D), making them unsuitable for the task.
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In a scenario where you are tasked with analyzing customer orders data stored in a DataFrame 'df' with columns 'customer_id', 'order_id', and 'order_date', your goal is to identify and count only the active orders, defined as those orders that have not been scheduled for a future date. Considering the importance of accurately filtering out future orders to ensure the integrity of your analysis, which of the following Spark SQL queries correctly creates a Common Table Expression (CTE) named 'active_orders' that filters out orders with a future order_date, and then uses the CTE to count the number of active orders? Choose the best option from the four provided below.
A
WITH active_orders AS (SELECT * FROM df WHERE order_date <= CURRENT_DATE()) SELECT COUNT(*) FROM active_orders
B
WITH active_orders AS (SELECT * FROM df WHERE order_date < CURRENT_DATE()) SELECT COUNT(*) FROM active_orders
C
WITH active_orders AS (SELECT * FROM df WHERE order_date = CURRENT_DATE()) SELECT COUNT(*) FROM active_orders
D
WITH active_orders AS (SELECT * FROM df WHERE order_date > CURRENT_DATE()) SELECT COUNT(*) FROM active_orders