This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Create Single Aggregation Measures

Note that there are 10 practice questions (with answers and explanations) at the end of each topic. Also, there are 2 practice tests with 60 questions each available on the hub below all the exam topics.

Overview

Single aggregation measures are one of the most foundational DAX skills tested on the PL-300 exam. These measures perform basic mathematical aggregations (such as sum, count, average, min, and max) over a column and are evaluated dynamically based on filter context.

Microsoft expects Power BI Data Analysts to understand:

• When to use aggregation measures
• How they differ from calculated columns
• How filter context impacts results
• How to write clean, efficient DAX

What Is a Single Aggregation Measure?

A single aggregation measure:

• Uses one aggregation function
• Operates over one column
• Returns a single scalar value
• Responds dynamically to filters, slicers, and visuals

These measures are typically the building blocks for more advanced calculations.

Common Aggregation Functions You Must Know

SUM

Adds all numeric values in a column.

Total Sales = SUM(Sales[SalesAmount])

📌 Common use case:

• Revenue, cost, quantity, totals

COUNT

Counts non-blank numeric values.

Order Count = COUNT(Sales[OrderID])

📌 Use when:

• Counting numeric IDs that never contain text

COUNTA

Counts non-blank values of any data type.

Customer Count = COUNTA(Customers[CustomerName])

📌 Use when:

• Column contains text or mixed data types

COUNTROWS

Counts rows in a table.

Total Orders = COUNTROWS(Sales)

📌 Very common on the exam
📌 Often preferred over COUNT for fact tables

AVERAGE

Calculates the arithmetic mean.

Average Sales = AVERAGE(Sales[SalesAmount])

📌 Exam tip:

• AVERAGE ≠ AVERAGEX (row context vs table expression)

MIN and MAX

Returns the smallest or largest value.

Min Order Date = MIN(Sales[OrderDate])
Max Order Date = MAX(Sales[OrderDate])

📌 Often used for:

• Date ranges
• KPI boundaries

Why Measures (Not Calculated Columns)?

A frequent PL-300 exam theme is choosing the correct modeling approach.

📌 All aggregation logic should be implemented as measures, not calculated columns.

Filter Context and Single Aggregations

Single aggregation measures automatically respect:

• Visual filters
• Page filters
• Report filters
• Slicers
• Relationships

Example:

Total Sales = SUM(Sales[SalesAmount])

This measure:

• Shows total sales per year in a line chart
• Shows total sales per product in a matrix
• Shows filtered sales when slicers are applied

No additional DAX is required—filter context does the work.

Implicit vs Explicit Measures

Implicit Measures

Created automatically when dragging a numeric column into a visual.

❌ Not recommended for exam scenarios
❌ Limited reuse
❌ Less control

Explicit Measures (Preferred)

Created using DAX in the model.

Total Quantity = SUM(Sales[Quantity])

✅ Reusable
✅ Clear logic
✅ Required for advanced calculations

📌 PL-300 strongly favors explicit measures

Naming and Formatting Best Practices

Microsoft expects clean, readable models.

Naming

• Use business-friendly names
• Avoid technical column names

Total Sales Amount
Average Order Value

Formatting

• Currency
• Decimal places
• Percentage

📌 Formatting is part of model usability, which is tested indirectly.

Common Exam Pitfalls 🚨

• Using calculated columns for totals
• Confusing COUNT vs COUNTROWS
• Expecting measures to work without relationships
• Overusing AVERAGEX when AVERAGE is sufficient
• Forgetting measures respond to filter context automatically

How This Appears on the PL-300 Exam

You may be asked to:

• Identify the correct aggregation function
• Choose between COUNT, COUNTA, and COUNTROWS
• Select the proper DAX expression
• Explain why a measure changes with slicers
• Fix incorrect aggregation logic

Key Takeaways

• Single aggregation measures are core DAX knowledge
• They are dynamic, efficient, and reusable
• Always prefer measures over calculated columns for aggregations
• Understand how filter context impacts results
• Master the basic aggregation functions before moving to CALCULATE or iterators

Practice Questions

Go to the Practice Exam Questions for this topic