This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Transform and load the data
      --> Create and Transform Columns

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

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Columns are the foundation of data analysis in Power BI. The ability to create new columns and transform existing ones is essential for shaping your dataset into a structure that supports meaningful insights and accurate reports.

In the PL-300 exam, Microsoft tests not only whether you can perform transformations but also whether you understand when and why to apply them.

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Why Create and Transform Columns?

Before data can be modeled and visualized:

• It must be clean, consistent, and in the right format
• New columns may be needed to support business logic
• Transformations ensure data is reliable and analysis-ready

For example:

• Converting text dates into true Date types
• Extracting parts of a string (e.g., Year from a date)
• Splitting a full name into first and last names
• Normalizing inconsistent text values

These are not just useful—they are often necessary for accurate DAX measures and reporting.

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Where Column Transformations Happen

Most column creation and transformation tasks happen in Power Query Editor (before the data loads into the model).

Key places include:

• Transform tab
• Add Column tab
• Applied Steps pane
• Advanced Editor (for M code)

Power BI also allows column creation after loading the data through:

• DAX Calculated Columns (in the data model)

The exam may present scenarios where you choose which tool (Power Query vs DAX) to use.

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Common Column Transformations

Here are the main categories of column operations you should be ready to apply:

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1. Basic Transformations

These change existing columns:

• Rename columns
• Change data types
• Trim, clean, or format text
• Replace values
• Remove columns

These are the bread-and-butter tasks that clean and standardize data.

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2. Splitting and Merging Columns

When data is combined within one field:

• Split Column (by delimiter or number of characters)
  • Example: Split Full Name → First Name and Last Name
• Merge Columns
  • Example: Combine City and State into a single location field

This is essential when data needs to be restructured for modeling.

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3. Extracting Components

Examples include:

• Extracting Year, Month, or Day from a Date column
• Taking the first/last characters from a text string
• Extracting text before or after a specific character

These operations prepare granular fields needed for grouping or calculations.

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4. Calculations Using “Add Column”

You can create derived columns based on logic:

• Custom Columns (via M formulas)
• Conditional Columns
  • Example: Flag High Value sales where sales > $1,000
• Index Columns
  • Useful for row ordering

These columns often support business metrics or classifications.

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Text Transformations

Text columns commonly require cleaning and standardization:

• Uppercase / Lowercase
• Trim (removes leading/trailing spaces)
• Clean (removes non-printable characters)
• Replace Values (e.g., “N/A” → null)

The exam often tests whether you know how to fix inconsistent text data.

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Date and Time Transformations

Working with dates is core to analysis:

• Change text to date/time type
• Extract Year, Quarter, Month, Day
• Add custom time intelligence columns
• Use locale conversion for date parsing

This enables time-based grouping and accurate measures like YTD (Year-to-Date).

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Conditional and Custom Columns

Conditional Columns

• Created through UI (Add Column → Conditional Column)
• Define logic visually (e.g., if Sales > 500 then "High" else "Low")

Custom Columns

• Created using Power Query M code
• More advanced logic and functions

Both are useful depending on the complexity of your requirement. Exam questions often compare these approaches.

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Column Transformations vs DAX Calculated Columns

Power Query Column

• Transformation occurs before data loads into model
• Changes physical data shape
• Useful for cleaning and structuring data

DAX Calculated Column

• Created after data loads into the model
• Evaluated per row in the model
• Useful for measures and relationships tied to data model context

Exam insight:
Use Power Query transformations for structural cleanup. Use DAX calculated columns when the logic depends on model relationships or evaluation context.

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Best Practices for the Exam

• Clean data before creating columns.
  Don’t derive new data from dirty input.
• Apply the right transformation tool.
  Power Query for structural cleanup; DAX for model-aware calculations.
• Name columns clearly.
  Report consumers and measures depend on intuitive names.
• Avoid unnecessary columns.
  Only keep what’s needed for reporting to improve model performance.
• Group related transformations logically.
  Use Query folding where possible (especially for large datasets).

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How This Appears on the PL-300 Exam

You might see scenarios like:

You need to split a full address column into street, city, and postal code for better filtering. Which transformation should you use?

This tests:

• Knowledge of Split Column
• When to apply it
• How to maintain data type integrity afterward

Or:

Your date column is text and not aggregating correctly. What do you do?

This tests:

• Understanding of data types
• Ability to convert to proper Date/Time

Most questions are scenario-based, requiring both decision and action reasoning.

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Key Takeaways

✔ Column transformations are a core part of shaping data
✔ Power Query is the primary environment for creating and transforming columns
✔ Use Add Column for new fields and Transform for modifying existing fields
✔ Know the difference between Power Query and DAX calculated columns
✔ Common transformations include text, date, splitting/merging, conditional logic, and custom formulas

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Practice Questions

Go to the Practice Exam Questions for this topic.