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
    --> Select Appropriate Column Data Types

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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Selecting the correct column data types is a foundational step in preparing data for analysis in Power BI. In the PL-300 exam, Microsoft evaluates your ability to choose, validate, and apply appropriate data types to ensure accurate calculations, efficient models, and reliable visuals.

Although Power BI often detects data types automatically, exam scenarios frequently test when automatic detection is wrong—and how to fix it.

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Why Column Data Types Matter

Correct data types directly impact:

• Aggregation behavior (sum, average, count)
• Filtering and sorting
• Relationship creation
• Model performance and storage
• DAX calculations and measures
• Visual behavior in reports

An incorrect data type can silently produce wrong results without throwing an obvious error—something the exam loves to test.

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Where Data Types Are Set

Column data types are primarily managed in Power Query Editor, not in the report or model view.

You can set or change data types by:

• Using the Data Type dropdown in the column header
• Using Transform → Data Type
• Using Transform → Using Locale
• Editing the Changed Type step in Applied Steps

Exam Tip: Power BI automatically inserts a Changed Type step—know when to keep it, modify it, or remove it.

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Common Column Data Types in Power BI

Numeric Types

• Whole Number – IDs, counts, quantities
• Decimal Number – Currency, percentages, measurements
• Fixed Decimal Number – Financial data requiring precision

Text

• Used for names, descriptions, categories, codes
• Avoid using text for numeric or date data whenever possible

Date and Time Types

• Date
• Time
• Date/Time
• Date/Time/Timezone

Correct date types enable:

• Time intelligence in DAX
• Date hierarchies
• Accurate filtering and grouping

Boolean

• True/False values
• Useful for flags and status indicators

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Choosing the Correct Data Type (Exam Focus)

Numbers vs Text

A column containing numeric-looking values (e.g., "1001", "1002") might actually represent:

• An ID → should remain Text, especially if they may contain leading zeros in the future.
• A measure → should be Whole or Decimal Number

Exam Insight: If your IDs are numeric, ensure that they do not have leading zeros or decimals, and also ensure that they are not inadvertently summarized or used in calculations. Numeric IDs are better for performance but setting or leaving them Text is an option if the scenario requires it.

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Dates Stored as Text

Dates often import as text due to:

• Regional formats
• Inconsistent source systems

Correct approach:

• Clean the text first
• Convert using Change Type

Avoid using text dates in the model—this breaks time intelligence.

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Decimal vs Fixed Decimal

• Decimal Number: More flexible, faster
• Fixed Decimal Number: Better for financial accuracy

Exam Insight: Use Fixed Decimal when precision matters (e.g., currency).

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Avoiding Common Data Type Mistakes

❌ Converting Too Early

Changing data types before cleaning:

• Can cause errors
• Makes transformations fail

✔ Best practice: Clean first, then convert.

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❌ Using Text Instead of Numeric

Numeric data stored as text:

• Cannot be aggregated
• Breaks calculations
• Causes visuals to behave unexpectedly

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❌ Incorrect Date Types

Using Date/Time when only Date is needed:

• Increases model size
• Causes grouping issues

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Data Types and Relationships

For relationships to work:

• Data types must match
• Text ↔ Text
• Number ↔ Number
• Date ↔ Date

If data types don’t match:

• Relationships cannot be created
• Merge queries may fail
• Filter propagation breaks

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Using “Change Type with Locale”

This is especially important for:

• International datasets
• CSV files
• Date formats like DD/MM/YYYY

Why it matters for the exam:
Microsoft frequently includes scenarios where date conversion fails due to regional formatting.

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Verifying Data Types Before Load

Before loading data into the model:

• Review all Changed Type steps
• Confirm numeric columns aggregate correctly
• Confirm date columns create hierarchies
• Ensure IDs are not numeric by mistake

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Best Practices for PL-300 Candidates

• Always review auto-detected data types
• Treat IDs and codes as text
• Use Fixed Decimal for financial data
• Convert data types after cleaning
• Match data types before relationships or merges
• Understand when to use locale-based conversion

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How This Appears on the PL-300 Exam

Expect questions that ask you to:

• Choose the correct data type for a scenario
• Identify problems caused by incorrect data types
• Fix failed merges or relationships
• Resolve aggregation or date intelligence issues

These questions are often subtle—the data loads successfully, but behaves incorrectly.

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Key Takeaways

• Selecting appropriate data types is essential for correct analysis
• Automatic detection is helpful but not foolproof
• Power Query is the correct place to manage data types
• Understanding why a type is wrong is more important than memorizing steps

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Practice Questions

Go to the Practice Exam Questions for this topic.