CORRECT ANSWE R: B - No.

According to Microsoft Learn on PySpark DataFrame operations, df.fillna(0, subset=['order_amount']) replaces null values in the order_amount column with the integer 0. This does NOT exclude rows where order_amount is null --- it replaces the null with 0, meaning those rows are still included in the resulting DataFrame with 0 as the order_amount value. The requirement is to 'create a DataFrame that excludes rows where the order_amount is null' --- which means null rows must be removed (dropped), not filled. The correct operation to exclude null rows is df.dropna(subset=['order_amount']) or df.filter(df.order_amount.isNotNull()). fillna is used for data imputation (replacing nulls with a default value), which is a different operation from filtering out null rows.