PL-300 Prepare the data Practice Question
You are working on a Power BI project for a marketing department. You have a CSV file with customer survey responses. The file contains columns: CustomerID, SurveyDate, Response (text with ratings from 1 to 5), Comments (free text). The file is 10 MB. You need to load the data into Power BI and create a measure that calculates the average rating. However, when you load the file, you notice that the Response column is imported as text instead of whole number. Also, there are some rows with missing values in the Response column. You need to ensure the data is correctly typed and handle missing values appropriately. What is the best approach?
⚠ Common exam trap
Many exam-takers think data type conversion can be done in DAX (Option C) because it seems simpler, but the PL-300 exam emphasizes that Power Query is the correct place for data preparation tasks like type changes and null handling, not the data model layer.
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
✓
Change the data type of Response to whole number in Power Query, then filter out or replace null values.
Power Query is the designated tool for data type transformations and null handling during the load phase. Changing the Response column's data type to Whole Number in Power Query automatically converts valid text numbers and flags errors, while filtering out or replacing null values ensures clean data before the data model is built. This approach follows the best practice of performing data cleansing in Power Query rather than in DAX, which would add unnecessary overhead and complexity.
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 the 'Column from Examples' feature to create a new column with numeric values.
Why it's wrong here
Using 'Column from Examples' creates a *new* column, leaving the original `Response` column as text, which does not directly address the requirement to correctly type the *existing* column and handle its missing values in place. This feature is tempting because it can intelligently extract or transform data patterns into a new column with a specified data type. It would be the correct choice for deriving a new column from complex text patterns, such as extracting a number from a longer string like "Rating: 3 stars", rather than directly converting an existing column's type.
- ✗
In Power Query, split the Response column by delimiter and then use the first part.
Why it's wrong here
Splitting the Response column by a delimiter is an unnecessary, roundabout approach that assumes a consistent separator exists inside each value. If any cell contains a null value or lacks the delimiter, the split operation can produce blanks, errors, or an incorrect segment, potentially discarding meaningful data. Moreover, splitting creates new columns rather than converting the existing “Response” column's type, so you still need extra steps to extract, convert, and rejoin the numeric value. The correct, minimal action is to directly change the column's data type to whole number in the same Power Query step.
- ✗
Use a DAX calculated column to convert text to number.
Why it's wrong here
A DAX calculated column is the wrong place for type conversion because it runs after data is loaded into the model, increasing the in-memory column store and refresh overhead for a task better suited to the data loading phase. Using a formula like VALUE(Response) would create a brand-new column, leaving the original text column untouched and requiring you to handle blanks explicitly in DAX, which is less readable and less efficient. Power Query transformations occur upstream, support query folding, and produce a single source of truth for the column's type before the model is built, keeping the data preparation logic centralized and performant.
- ✓
Change the data type of Response to whole number in Power Query, then filter out or replace null values.
Why this is correct
Changing the data type of the Response column to whole number in Power Query is the correct approach because it directly addresses the underlying issue: the column is text but requires a numeric type for analysis. In Power Query, this transformation attempts to convert every value, and null values or invalid entries can be handled in the same step by filtering out invalid rows or replacing nulls with a default (e.g., 0) before loading. This is efficient, happens before data enters the model, and avoids the extra overhead of DAX calculated columns while preserving the column's identity and data lineage.
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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.