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")
)

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 this code produces logically correct results and the source table data has been de-duplicated and validated, which statement describes what will occur when this code is executed?_