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A data engineering team is tasked with automating a Spark SQL query that compiles monthly sales data. The query is designed to access a table named in the format monthly_sales_YYYYMM, where YYYYMM denotes the year and month of the sales data. The automation needs to ensure that each execution of the query targets the table for the month preceding the current one. The standard query format is: SELECT product_category , SUM ( sales ) FROM monthly_sales_YYYYMM GROUP BY product_category ; Considering the requirement for the query to adapt monthly to access data from the previous month, which strategy should the data engineering team employ? For instance, if the current month is March 2023, the query should automatically target the table named monthly_sales_202302.