The best approach is to change the data type of the Response column to Whole Number in Power Query, then filter out or replace null values. This is correct because Power Query is the designated environment for data transformation and cleansing during the load phase, where converting text to number and handling null values should occur before data enters the model. Changing the data type automatically converts valid text numbers like "1" or "5" into integers, while any conversion errors or missing entries can be addressed by filtering rows or replacing nulls with a default value, such as 0 or the column average. On the PL-300 exam, this scenario tests your understanding of the M-language transformation layer versus DAX calculations—a common trap is attempting to handle type conversion or nulls inside a measure, which adds unnecessary overhead and violates best practices. Remember the memory tip: "Clean in Query, calculate in DAX"—always perform data type fixes and null handling in Power Query to ensure a clean, efficient data model.
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
More complex than needed.
✗
In Power Query, split the Response column by delimiter and then use the first part.
Why it's wrong here
Splitting is unnecessary and may lose data.
✗
Use a DAX calculated column to convert text to number.
Why it's wrong here
Better to do in Power Query for performance and clarity.
✓
Change the data type of Response to whole number in Power Query, then filter out or replace null values.
Why this is correct
Directly resolves the type issue and handles nulls.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
When you change a column's data type in Power Query, the engine applies a type conversion using the underlying M language's Table.TransformColumnTypes function, which attempts to parse text values into the target type and marks any failures as errors. Filtering out nulls or replacing them with a default value (e.g., 0 or an average) can be done with the 'Replace Values' or 'Remove Rows' steps, ensuring that only clean, typed data enters the data model, which improves performance and avoids implicit type coercion issues in DAX measures.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the data type of Response to whole number in Power Query, then filter out or replace null values. — Option D is correct because 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.
What should I do if I get this PL-300 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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