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%) 
    --> Profile and clean the data 
        --> Evaluate data, including data statistics and column properties

Note that there are 10 practice questions (with answers and explanations) at the end of 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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Before cleaning, transforming, or modeling data, a Power BI Data Analyst must first evaluate the quality and structure of the data. The PL-300 exam tests your ability to profile data, interpret data statistics, and understand column properties to identify issues such as missing values, incorrect data types, outliers, and inconsistent formats.

This topic lives under Profile and clean the data because effective data preparation starts with understanding what the data looks like and how it behaves.

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What Does “Evaluate Data” Mean in Power BI?

Evaluating data means using Power BI (specifically Power Query) to:

• Understand data distribution and completeness
• Identify data quality issues
• Verify correct data types and formats
• Decide what cleaning or transformation steps are required

Rather than guessing, Power BI provides built-in profiling tools that summarize data characteristics automatically.

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Data Profiling Tools in Power Query

Power BI includes several profiling features that appear in the Power Query Editor, primarily within the View tab.

Key Data Profiling Options

• Column quality
• Column distribution
• Column profile

These tools help you quickly assess whether a column is usable, trustworthy, and correctly defined.

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Column Quality

Column quality provides a high-level overview of data completeness and validity.

It visually displays:

• Valid values
• Error values
• Empty (null) values

Why Column Quality Matters

• Quickly highlights missing or broken data
• Helps determine whether rows should be filtered, fixed, or removed
• Useful for early detection of refresh or ingestion issues

📌 Exam insight:
Questions often test whether you can identify which tool reveals missing or invalid values—column quality is the answer.

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Column Distribution

Column distribution shows how values are spread across a column.

It provides:

• Frequency of values
• Distinct vs unique counts
• A histogram-style visualization (for numeric fields)

Common Uses

• Spotting unexpected duplicates
• Identifying skewed data
• Detecting outliers
• Validating categorical values

📌 Exam insight:
Column distribution is used to understand value frequency, not just nulls or errors.

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Column Profile

Column profile gives the most detailed statistical view of a column.

Depending on the data type, it may include:

• Minimum and maximum values
• Average
• Standard deviation
• Count and distinct count
• Null count

Typical Use Cases

• Verifying numeric ranges (e.g., negative values where none should exist)
• Checking date ranges
• Understanding overall data shape before modeling

📌 Exam insight:
Column profile helps validate statistical characteristics, not formatting or naming.

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Understanding Column Properties

Beyond statistics, Power BI also evaluates column properties, which affect how data behaves in the model and visuals.

Key Column Properties to Evaluate

Data Type

Examples:

• Whole number
• Decimal number
• Text
• Date / DateTime
• Boolean

Incorrect data types can:

• Break visuals
• Prevent aggregations
• Cause relationship issues

📌 Exam tip:
Always verify data types before applying transformations or creating measures.

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Format

Controls how values appear (e.g., currency, percentage, date format).

• Affects display, not calculation logic
• Often adjusted after validating data type

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Default Summarization

Determines how numeric columns aggregate in visuals:

• Sum
• Average
• Count
• Do not summarize

📌 Exam insight:
Default summarization is evaluated when deciding how columns behave in visuals—not during Power Query transformations.

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Column Name & Description

• Clear names improve usability
• Descriptions help report consumers understand the data

While not deeply technical, the exam may include best-practice questions around data clarity and usability.

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Evaluating Data at the Right Stage

Most evaluation tasks occur in Power Query, before data is loaded into the model.

Why?

• Faster detection of issues
• Prevents poor-quality data from entering the model
• Reduces downstream modeling complexity

📌 Key distinction for the exam:

• Power Query → data evaluation & cleaning
• Model view → relationships & behavior
• Report view → visualization

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Common Exam Scenarios

You may encounter questions like:

Scenario 1

You need to quickly identify columns with missing or invalid values.

Correct concept: Column quality

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Scenario 2

You want to understand how frequently values appear in a categorical column.

Correct concept: Column distribution

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Scenario 3

You need to verify numeric ranges and detect outliers.

Correct concept: Column profile

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Scenario 4

A numeric column is being treated as text and cannot be aggregated.

Correct concept: Incorrect data type (column property)

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Best Practices to Remember

• Enable profiling tools early in data preparation
• Validate data types before transformations
• Use statistics to guide cleaning decisions
• Don’t rely on visuals alone to detect data quality issues

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Key Exam Takeaways

For the PL-300 exam, remember:

• Column quality → valid, error, and null values
• Column distribution → frequency and distinct values
• Column profile → statistical insights
• Column properties affect aggregation, relationships, and visuals
• Data evaluation happens primarily in Power Query

Understanding how to interpret what Power BI is telling you about your data is just as important as knowing how to clean it.

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Practice Questions

Go to the Practice Exam Questions for this topic.