PL-300 Prepare the data Practice Question
Exhibit
Refer to the exhibit.
```
Table.TransformColumnTypes(Source,{{"SalesAmount", type number},
{"OrderDate", type datetime},
{"CustomerID", type text}})
```You are reviewing a Power Query M expression that transforms column types. The 'SalesAmount' column contains values like '1,234.56' (with a comma as thousands separator). After applying this transformation, what is the likely result?
⚠ Common exam trap
Many exam-takers assume Power Query will automatically handle locale-specific formatting (like commas as thousands separators) during type conversion, but in reality, it fails with errors unless the data is preprocessed or the correct culture is specified.
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
✓
The transformation will result in errors for rows containing commas.
Power Query's default type conversion for numeric columns expects a period as the decimal separator and no thousands separator. When the 'SalesAmount' column contains values like '1,234.56' with a comma as a thousands separator, attempting to convert the column directly to a number type (e.g., using 'Change Type' or 'Table.TransformColumnTypes') will cause errors for rows containing commas, as Power Query cannot parse the comma as part of a valid number. The comma is not a recognized numeric character in the default locale, so the conversion fails.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The transformation will result in errors for rows containing commas.
Why this is correct
The M expression, likely using Number.From or a table column type change, requires text to match the current locale's numeric format. Since a comma is not the decimal separator in the default en-US locale, each row containing a comma causes a conversion failure that produces an Error value in the cell. Rather than being corrected or ignored, the transformation faithfully reports the parse failure as an error, which is the expected result.
- ✗
The column will be converted to text automatically.
Why it's wrong here
Power Query's conversion functions never silently promote a column to text when a numeric parse fails. Instead, the failed cells become Error values, and the column's type either remains the target numeric type with errors or becomes 'any', but never a clean text representation. The claim of automatic text conversion contradicts M's strict type system and error-handling behavior.
- ✗
The column will be successfully converted to numbers.
Why it's wrong here
A successful numeric conversion is only possible when the entire text matches the selected culture's formatting rules. Because the comma is not a valid decimal separator in the default locale, Number.From('12,34') produces an error rather than a number. The transformation cannot 'successfully convert' rows that contain such a comma, as every row is parsed individually and a single invalid character invalidates the whole value.
- ✗
The transformation will ignore the comma and convert the number correctly.
Why it's wrong here
Power Query's M language performs strict, literal parsing of numeric strings and does not skip or reinterpret invalid characters. Unlike some spreadsheet tools, it never ignores a comma and then treats the rest of the string as a number; the comma's presence forces a format validation failure. Consequently, the idea that the transformation will simply drop the comma and proceed is fundamentally at odds with how M's conversion functions operate.
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