The correct answer is A --- Yes.

df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark's filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.

This works correctly because isNotNull() is explicitly null-aware --- unlike the != None comparison in Q52, it doesn't rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.

Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=['order_amount']) produce identical results. The choice between them is stylistic --- isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.