PL-300 Prepare the data 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 'Replace Errors' to replace error values with null.

  Why this is correct

  Replacing errors with null in Power Query is a non-destructive transformation that explicitly substitutes each invalid date value with a null while keeping the row intact. You apply it to the date column (Home > Replace Errors or context menu) and specify null as the replacement value. This preserves all other column values for that row and produces a clean, nullable date column that the data model handles naturally, e.g., through blanks in visuals or DAX functions like CALCULATE with filters. It also avoids the risk of load failure due to leftover errors.

• ✗

  Remove rows with errors using 'Remove Rows' > 'Remove Errors'.

  Why it's wrong here

  Remove Rows > Remove Errors is a row-level operation that deletes the entire record for any row containing an error at the selected column(s), even if only the date column is invalid. This is unnecessarily destructive because it discards other valid columns (e.g., customer ID, sales amount), causing data loss and potential bias in aggregations. In Power Query, it may also remove rows that have errors in unselected columns if you don't scope it to the date column, compounding the problem. Use Replace Errors when you need to preserve row context.

• ✗

  Change the data type to 'Date' and ignore errors.

  Why it's wrong here

  Power Query's data type change dialog does not offer an 'ignore errors' option—when you coerce a column to Date, any value that cannot be parsed becomes an error value in that column. 'Ignoring' errors is not a real action; the error values remain, and on load, Power Query throws an error that causes the refresh to fail. Even if you attempt to continue, downstream steps will propagate these errors, and visuals will be blocked. You must explicitly handle errors after the type conversion—using Replace Errors or Remove Errors—to ensure a clean load.

• ✗

  Filter the column to exclude rows where the date is invalid after type conversion.

  Why it's wrong here

  Filtering out invalid dates after a type conversion is not feasible because errors are not filterable values in the Power Query filter interface; the column shows '(Error)' and the filter UI does not allow selecting or excluding error values. To do this you would need to add a calculation column using try/otherwise logic to test each date string before conversion, then filter on that flag—a multi-step workaround that is more complex and less efficient than a single Replace Errors step. Additionally, filtering out rows discards whole records, similar to Remove Errors, which undermines the goal of preserving data.

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