PL-300 Fill Down/Up Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Use the 'Fill Down' or 'Fill Up' option to propagate non-null values into null cells.

  Why this is correct

  Fill Down and Fill Up are correct null-handling techniques in Power Query. Fill Down copies the last non-null value above into subsequent null cells until another non-null value is encountered; Fill Up works in the reverse direction. This is ideal for sparse columns where nulls represent the previous known value, such as period-end totals or grouping labels, though leading or trailing nulls may remain when there is no non-null value to propagate.

• ✗

  Remove rows that contain null values using the 'Remove Rows' > 'Remove Blank Rows' option.

  Why it's wrong here

  Remove Blank Rows is an incorrect method for handling rows with null values because it only removes rows where all columns are null or empty. If a row has a null in one column but contains data in others, it is considered a partial row and is retained. To remove rows with any null values, you would need to filter each relevant column individually or use a custom row-level condition, not simply Remove Blank Rows.

• ✓

  Replace null values with a default value using the 'Replace Values' transform.

  Why this is correct

  Replace Values is a valid and direct method for handling nulls. In Power Query, you can select the column, choose Replace Values, enter 'null' as the value to find, and specify a default replacement such as 0, 'Unknown', or a date. This preserves row count while eliminating missing values, and it can be applied to selected columns or multiple columns depending on the transformation context.

• ✗

  Merge the table with another table that has no nulls.

  Why it's wrong here

  Merging the table with another table that has no nulls does not handle existing null values. A merge operation combines columns based on keys, but the original nulls in the primary table remain unchanged unless the new columns are explicitly used to replace or coalesce them. Without adding a conditional column or expanding and applying logic, merging alone does not clean nulls, and it can introduce additional duplicates or nulls if key matches are incomplete.

• ✗

  Change the data type of the column to a non-nullable type.

  Why it's wrong here

  Changing a column's data type to a non-nullable type is not a valid null-handling method because Power Query data types do not enforce nullability. Nulls remain valid values for any data type, and forcing a non-nullable type in the data model would cause import errors rather than transforming the data. This approach fails to address existing nulls and simply shifts the problem to load-time validation.

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