PL-300 Prepare the data Practice Question
You are transforming a table that contains a 'Date' column in text format (e.g., '2026-01-15'). You need to create separate columns for Year, Month, and Day. Which THREE Power Query transformations can you use? (Choose three.)
⚠ Common exam trap
Microsoft often tests the distinction between splitting a column by delimiter versus using date functions, where candidates may incorrectly choose Unpivot (Option A) thinking it 'unpacks' data, but it actually normalizes columns into rows.
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
✓
Split Column by Delimiter using '-' as the delimiter.
Option B is correct because the text dates use a consistent '-' delimiter, so Split Column by Delimiter with '-' produces three separate columns that can be renamed Year, Month, and Day. Option C is correct because a custom column can call Date.Year, Date.Month, and Date.Day (after converting the text to a date type with Date.FromText or a type change) to derive each component reliably. Option E is correct because the Extract feature can pull fixed character ranges from the text, e.g., the first 4 characters for Year and subsequent characters for Month and Day, which works for the ISO-style 'yyyy-MM-dd' format. Option A is not appropriate because Unpivot rotates columns into attribute-value rows rather than creating separate Year, Month, and Day columns. Option D is not appropriate because merging a column with itself is a join operation that does not split or extract date parts.
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 the Date column.
Why it's wrong here
Unpivot is a table-shaping operation that converts selected columns into attribute-value rows, commonly used to normalize cross-tabulated data. It does not interpret or decompose the contents of a single date string; you would still need to split or parse the Date values afterward. Therefore, unpivoting the Date column alone cannot produce Year, Month, and Day columns, making it an incorrect choice for this transformation.
- ✓
Split Column by Delimiter using '-' as the delimiter.
Why this is correct
Using Split Column by Delimiter with '-' assumes the Date column contains text values such as '2025-04-09'. In the Power Query editor, select the column, choose Split Column > By Delimiter, specify '-' as the custom delimiter, and split 'Each occurrence of the delimiter' to create three separate columns. After splitting, you should rename those columns to Year, Month, and Day and change their data types to Whole Number or Text as appropriate. This is a direct and effective method when the format is consistently ISO-like.
- ✓
Use the Date.Year, Date.Month, Date.Day functions in a custom column.
Why this is correct
In a custom column, you can invoke the M functions Date.Year, Date.Month, and Date.Day to pull discrete components from a date value. First ensure the column is typed as Date or use Date.FromText to convert a text value, then add three custom columns with formulas such as = Date.Year([Date]). These functions respect the data type and return integers, avoiding manual substring math. This approach is robust for date-typed columns and is a preferred method when the source has genuine date types.
- ✗
Merge the Date column with itself.
Why it's wrong here
Merging the Date column with itself would create a self-join, matching each row to rows where the join keys align, which is fundamentally about combining tables, not about parsing a single value. A self-merge does not transform the values inside the column; it duplicates rows or appends columns based on a matching condition. It provides no way to isolate the year, month, or day segments, and would likely produce an inflated or incorrect result set. Thus, Merge is irrelevant for extracting date parts.
- ✓
Use the Extract feature to extract first 4 characters for Year, then subsequent characters.
Why this is correct
The Extract feature in Power Query, found under the Transform tab, lets you pull substrings by position or by starting characters. You can extract the first 4 characters to get the year, then use Extract Text Range starting at index 5 with length 2 to get the month, and similarly for the day. This works reliably when every date uses a fixed-width format like 'YYYY-MM-DD', but it becomes fragile with variable-length strings or different separators. After each extraction, rename the columns and set their data types.
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