The data engineering team maintains the following code:

accountDF = spark.table("accounts")
orderDF = spark.table("orders")
itemDF = spark.table("items")
orderWithItemDF = (orderDF.join(
    itemDF,
    orderDF.itemID == itemDF.itemID)
    .select(
        orderDF.accountID, 
        orderDF.itemID,
        itemDF.itemName))
finalDF = (accountDF.join(
    orderWithItemDF,
    accountDF.accountID == orderWithItemDF.accountID)
    .select(
        orderWithItemDF["*"],
        accountDF.city))
(finalDF.write
    .mode("overwrite")
    .table("enriched_itemized_orders_by_account"))

accountDF = spark.table("accounts")
orderDF = spark.table("orders")
itemDF = spark.table("items")
orderWithItemDF = (orderDF.join(
    itemDF,
    orderDF.itemID == itemDF.itemID)
    .select(
        orderDF.accountID, 
        orderDF.itemID,
        itemDF.itemName))
finalDF = (accountDF.join(
    orderWithItemDF,
    accountDF.accountID == orderWithItemDF.accountID)
    .select(
        orderWithItemDF["*"],
        accountDF.city))
(finalDF.write
    .mode("overwrite")
    .table("enriched_itemized_orders_by_account"))

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?*