PL-300 Prepare the data Practice Question
You are importing data from a CSV file that contains a column with mixed data types (numbers and text). Power BI automatically assigns the data type as Text. You need to perform numerical aggregations on this column. What should you do?
⚠ Common exam trap
Watch out — candidates often think a DAX calculated column (Option C) is the correct approach for data type conversion, but the PL-300 exam emphasizes performing data transformations in Power Query Editor (the 'Prepare the data' domain) rather than in DAX, as Power Query is the proper tool for cleaning and shaping data before loading it into the model.
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
✓
In Power Query Editor, change the data type of the column to Whole Number or Decimal Number.
Changing the column's data type to Whole Number or Decimal Number in Power Query Editor will force Power BI to interpret the numeric values as numbers, enabling aggregations like SUM or AVERAGE. Power Query Editor provides a robust transformation environment where data type changes are applied during the load process, ensuring the column is treated as numeric for all downstream calculations. This approach is more efficient and reliable than using DAX conversions, as it avoids the overhead of calculated columns and leverages Power Query's native type detection and error handling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Split the column using a delimiter to separate numbers from text.
Why it's wrong here
Splitting the column using a delimiter creates separate columns but does not recast any of them as numeric; the resulting segments are still text unless you explicitly change each one's data type. Furthermore, this approach alters your table structure unnecessarily and still requires a follow-up type-conversion step, so it fails to address the core requirement of enabling numerical aggregation on the original values.
- ✗
Create a relationship with a numeric table to enable aggregation.
Why it's wrong here
Creating a relationship with a numeric table does not modify the data type of the existing text column; relationships merely define join semantics and require compatible types, so you would still need to convert the text column to numeric before relating. Even if you map to a numeric lookup, the text column itself remains text, and any aggregation attempted on it will still be disallowed. It's a modeling workaround that misses the fundamental type-conversion need.
- ✗
Create a calculated column in DAX using VALUE() to convert the text to numbers.
Why it's wrong here
A calculated column using DAX's VALUE() does convert text to a numeric value and would allow aggregation, but it tries to solve a data import issue at the presentation layer. Because the calculated column is re-evaluated whenever the model refreshes, it consumes memory and adds model overhead, whereas fixing the type in Power Query is performed only during query load and keeps the model lean. Best practice is to correct the data type at the source query step, not defer it to DAX.
- ✓
In Power Query Editor, change the data type of the column to Whole Number or Decimal Number.
Why this is correct
Changing the data type in Power Query Editor to Whole Number or Decimal Number is the proper and idiomatic remedy because it defines the column as numeric from the moment it enters the data model, immediately making it available for aggregations like SUM and AVERAGE. This transform is part of your ETL workflow, gets applied at each refresh, and is self-documenting in the query steps, ensuring downstream reports treat the column precisely as intended.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.