You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a...

The correct answer is B — No.

df.fillna(0, subset=['order_amount']) does not remove null rows. It replaces null values in order_amount with the integer 0, leaving those rows in the DataFrame with 0 as their order_amount value. The row count is unchanged — nulls are imputed, not dropped.

The requirement is to exclude rows where order_amount is null, meaning those rows should not appear in the result at all. fillna is a data imputation function — it fills gaps with default values, which is a different operation entirely from filtering. After fillna, a downstream process would see order_amount = 0 and might treat it as a valid zero-value order rather than recognising it was originally null.

The correct approach is df.dropna(subset=['order_amount']) or df.filter(df.order_amount.isNotNull()), both of which physically remove null rows from the resulting DataFrame.