PL-300 Prepare the data Practice Question
You are using Power Query to transform a column 'FullName' containing values like 'Smith, John'. You need to split this into 'LastName' and 'FirstName' columns. Which THREE steps are required?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use 'Split Column' by delimiter
Option B is correct because the 'FullName' values like 'Smith, John' must first be separated at the comma, which is exactly what Power Query's 'Split Column' by delimiter (comma) does, producing two new columns. Option C is correct because the delimiter is followed by a space, so the resulting 'John' value will have a leading space that must be removed with Trim to get clean FirstName values. Option E is correct because the split produces generically named columns (e.g., 'FullName.1' and 'FullName.2'), so they must be renamed to 'LastName' and 'FirstName' to match the required output. Option A does not belong because Unpivot transforms columns into rows and is unrelated to splitting a single text column. Option D does not belong because merging the columns back would undo the split and recreate a single combined value, contradicting the goal of separate LastName and FirstName columns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unpivot columns
Why it's wrong here
Unpivot columns is a table-shaped transformation that rotates selected columns into attribute-value pairs, increasing row count and collapsing the data structure. It does not parse the text inside a single column; instead, it would treat the fullname column as a variable to be spread across rows, which is the opposite of splitting one field into two. This would destroy the desired separate FirstName and LastName fields rather than create them.
- ✓
Use 'Split Column' by delimiter
Why this is correct
Using 'Split Column' by delimiter is the core transformation that parses the fullname column into two columns based on the comma separator. In Power Query, this command (found on the Transform tab) scans each cell for the specified delimiter and divides the text at that point, producing two new columns containing the fragments. This is the only option here that directly creates the separate LastName and FirstName values from the original string, so it is the correct primary action.
- ✓
Trim leading/trailing spaces from new columns
Why this is correct
After the delimiter splits the fullname column, the fragments often retain leading or trailing spaces—for example, 'Doe, John' produces ' Doe' and ' John' if the delimiters were spaced. The Trim transformation in Power Query removes those whitespace characters from both new columns, ensuring the lastName and firstName values are clean and consistent for joins, sorting, or DAX measures. Although this step does not split or restructure data, it is indispensable for data quality and should be applied immediately after splitting.
- ✗
Merge the split columns back
Why it's wrong here
Merging the two split columns would concatenate their values back into a single text string, effectively reversing the delimiter split and recreating a combined fullname field. Since the objective is to have distinct LastName and FirstName columns, merging would undo the transformation and introduce redundant or duplicate data, making the previous steps pointless. If a combined name is needed later, it can be added as a custom column instead of merging away the separated columns.
- ✓
Rename the new columns to LastName and FirstName
Why this is correct
When Power Query splits a column by delimiter, it automatically names the resulting columns sequentially, such as 'fullname.1' and 'fullname.2', which are not descriptive. Renaming these columns to LastName and FirstName (order depending on the source format) makes the data model self-documenting and ensures that measures, relationships, and report visuals reference meaningful fields. This step is essential for maintainability and avoids confusion for other analysts or during schema evolution, even though it does not modify the underlying values.
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