The data engineering team maintains the following code: import pyspark . sql . functions as F ( spark . table ( "silver_customer_sales" ) . groupBy ( "customer_id" ) . agg ( F . min ( "sale_date" ). alias ( "first_transaction_date" ), F . max ( "sale_date" ). alias ( "last_transaction_date" ), F . mean ( "sale_total" ). alias ( "average_sales" ), F . countDistinct ( "order_id" ). alias ( "total_orders" ), F . sum ( "sale_total" ). alias ( "lifetime_value" ) ) . write . mode ( "overwrite" ) . table ( "gold_customer_lifetime_sales_summary" ) ) Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?