DAX Queries Made Easy: How to Write and Explain Them with Power BI Copilot

Artificial intelligence is reshaping how teams explore data—and Power BI is leading the charge. With Power BI Copilot, you can generate and explain DAX queries using natural language. That means faster analysis, fewer errors, and more time to focus on insights rather than syntax.

This guide walks you through what Power BI Copilot is, how to enable it, and how to use it to write and explain DAX queries step by step. You’ll also get practical prompt ideas, code examples, troubleshooting tips, and best practices to get more from your data model—without needing to be a DAX expert.

If you’re brand new to Power BI or business intelligence, you may also find these guides helpful:

• What is Microsoft Power BI?
• Mastering Business Intelligence: A Beginner’s Guide
• Real use case inspiration: NPS and Power BI: the perfect combination for your business

What Is Power BI Copilot?

Power BI Copilot is Microsoft’s generative AI assistant embedded in Power BI. It understands natural language and can:

• Generate or refine DAX queries and measures
• Create reports and summaries from prompts
• Explain what a DAX query does in plain English
• Suggest synonyms to improve Q&A experiences
• Accelerate dashboard creation and iteration

In short, Copilot helps you move from “question” to “analysis” faster—especially useful if you’re still learning DAX or working under tight deadlines.

DAX Query vs. DAX Measure: What’s the Difference?

Before diving in, it’s helpful to know the two most common ways you’ll use DAX:

• Measures: Calculations you add to your model (for example, Total Sales, YoY Growth). Measures are reusable across visuals and reports.
• DAX Queries: Ad‑hoc queries you run in DAX Query View (EVALUATE …). These are great for exploring data, validating logic, and prototyping.

Copilot can help with both. You can ask it to write an ad‑hoc query, turn a query into a reusable measure, or explain existing DAX.

Key Benefits of Power BI Copilot for DAX

• Automates repetitive work: Creates measures, calculated columns, and queries from fluent prompts.
• Reduces errors: Natural language to DAX generation minimizes syntax mistakes.
• Explains complex logic: Get step‑by‑step explanations of what a query does.
• Improves data exploration: Build visuals and summaries from a single prompt.
• Boosts accessibility: Non‑experts can perform advanced analysis confidently.

Example prompt:

“Show cumulative sales by product for the last three months, with a column for the previous three months.”

How to Enable Copilot in Power BI (DAX Query View)

To use Copilot for DAX, ensure your environment meets these requirements:

• Your workspace must run in Fabric F64 or Power BI Premium capacity.
• Copilot may require tenant admin enablement and appropriate licensing.
• Use the latest Power BI Desktop to access DAX Query View with Copilot.

Step-by-step (Power BI Desktop):

1) Open Power BI Desktop.

2) File > Options and settings > Options.

3) Preview Features: check “DAX Query View with Copilot.”

4) Restart Power BI Desktop if prompted.

5) Confirm your account has permissions to use Copilot and that your workspace is in the required capacity.

How to open Copilot:

• Press CTRL + I, or
• Click the Copilot icon on the ribbon in DAX Query View.

Tip: Keep your Desktop version up to date to avoid compatibility issues and benefit from the latest Copilot enhancements.

How to Write DAX Queries with Copilot (Step by Step)

Step 1: Activate Copilot

• Use CTRL + I or click the Copilot button in the ribbon.

Step 2: Enter a Natural Language Prompt

Be clear about:

• The fields and tables you want
• The time period (e.g., rolling 3 months, prior year)
• The grain (by product, by region, by month)
• Any filters (only Category = “Bikes”)

Example prompt:

“Create a DAX query that lists products with profit in the last 3 months and the previous 3 months, plus the percentage change, sorted by the latest period descending.”

Step 3: Review and Run

Copilot returns a DAX query. Review for accuracy (field names, filters, time logic) and click Run. You can then Keep it, tweak it, or ask Copilot to optimize or explain it.

Practical DAX Query Examples You Can Generate with Copilot

Below are example prompts and the kinds of DAX queries Copilot can produce. Adjust table/column names to match your model (e.g., 'Sales', 'Date', 'Product').

1) Last 3 Months vs Previous 3 Months (with % change)

Prompt:

“List products with last 3 months profit, previous 3 months profit, and percentage change.”

Example DAX query:

`

EVALUATE

VAR Last3 =

DATESINPERIOD('Date'[Date], LASTDATE('Date'[Date]), -3, MONTH)

VAR Prev3 =

DATESINPERIOD('Date'[Date], DATEADD(LASTDATE('Date'[Date]), -3, MONTH), -3, MONTH)

RETURN

SUMMARIZECOLUMNS(

'Product'[ProductName],

"Last 3 Months Profit", CALCULATE(SUM('Sales'[Profit]), Last3),

"Previous 3 Months Profit", CALCULATE(SUM('Sales'[Profit]), Prev3),

"Pct Change",

VAR L3 = CALCULATE(SUM('Sales'[Profit]), Last3)

VAR P3 = CALCULATE(SUM('Sales'[Profit]), Prev3)

RETURN DIVIDE(L3 - P3, P3)

)

ORDER BY [Last 3 Months Profit] DESC

`

2) Year‑to‑Date vs Prior Year YTD

Prompt:

“Create a DAX query showing YTD sales and prior year YTD sales by month for the selected year.”

Example DAX query:

`

EVALUATE

VAR YTDRange = DATESYTD('Date'[Date])

VAR PYRange = DATEADD(YTDRange, -1, YEAR)

RETURN

SUMMARIZECOLUMNS(

'Date'[Year],

'Date'[MonthName],

'Date'[MonthNumber],

"Sales YTD", CALCULATE(SUM('Sales'[SalesAmount]), YTDRange),

"Sales PY YTD", CALCULATE(SUM('Sales'[SalesAmount]), PYRange),

"YTD YoY %", DIVIDE(

CALCULATE(SUM('Sales'[SalesAmount]), YTDRange) - CALCULATE(SUM('Sales'[SalesAmount]), PYRange),

CALCULATE(SUM('Sales'[SalesAmount]), PYRange)

)

)

ORDER BY 'Date'[Year], 'Date'[MonthNumber]

`

Note: Ensure your Date table is marked as a date table and fully related to your fact tables.

3) Top N Contributors with a “Rest of” Group

Prompt:

“Show top 5 products by sales and group everything else as ‘Other’, including each product’s share of total.”

Example DAX query:

`

EVALUATE

VAR TotalSales = CALCULATE(SUM('Sales'[SalesAmount]), ALL('Product'))

VAR TopNTable =

TOPN(

5,

SUMMARIZECOLUMNS('Product'[ProductName], "Sales", SUM('Sales'[SalesAmount])),

[Sales], DESC

)

VAR OtherSales = TotalSales - SUMX(TopNTable, [Sales])

RETURN

UNION(

TopNTable,

ROW("ProductName", "Other", "Sales", OtherSales)

)

ORDER BY [Sales] DESC

`

Turn a Query into a Reusable Measure

Copilot can convert your ad‑hoc query logic into a single measure you can use across visuals. Try:

• “Convert this DAX query into a measure called ‘Last 3 Months Profit’.”
• “Create a measure for YoY % based on the logic in this query.”

Use Copilot to Explain Any DAX Query

One of the most powerful features is understanding complex DAX written by you or someone else.

How to get an explanation:

1) Select the DAX query (or measure).

2) Activate Copilot (CTRL + I).

3) Prompt: “Explain what this query does line by line and describe the output table.”

Example to explain:

`

EVALUATE

SUMMARIZECOLUMNS(

'Product'[ProductName],

"Total Sales", SUM('Sales'[SalesAmount])

)

`

Typical Copilot explanation:

• EVALUATE: Executes a DAX query that returns a table.
• SUMMARIZECOLUMNS: Groups results by ProductName.
• SUM: Computes total SalesAmount for each product.
• Output: A two‑column table with ProductName and Total Sales.

You can also ask:

• “Why does this return blanks in some rows?”
• “Rewrite this to use variables and improve readability.”
• “Optimize for performance with large datasets.”

Prompt Patterns That Get Great Results

Try these templates and fill in your specifics:

• “Create a DAX query that returns [metric] by [dimension] for [time period], sorted by [metric] descending.”
• “Write a measure for [metric] with filters [condition], respecting slicers.”
• “Add a running total column for [metric] ordered by [Date] using ALLSELECTED.”
• “Explain why this [measure/query] returns the same value for all rows.”
• “Rewrite this using VARs and CALCULATE for clarity.”
• “Add a column for contribution to total (% of grand total).”
• “Return top N [dimension] by [metric], plus an ‘Other’ row.”

Best Practices for Accurate Copilot Results

• Use a star schema: Separate fact and dimension tables with clear relationships.
• Mark your Date table: Required for time intelligence functions.
• Name consistently: Use clear, human‑readable table and column names.
• Add synonyms: Improves Q&A and natural language understanding.
• Provide context in prompts: Include table names, time windows, and filters.
• Validate output: Always review field names and logic before running.

Model hygiene dramatically improves Copilot’s accuracy and speed.

Common Pitfalls—and How Copilot Helps You Fix Them

• Blank or identical values across rows
• Likely a context issue. Ask: “Why is this measure not changing by row? Fix filter context so it evaluates per [dimension].”
• Wrong time comparisons
• Confirm date granularity and marked date table. Prompt: “Rewrite using SAMEPERIODLASTYEAR or DATEADD for correct YoY.”
• Performance slowdowns
• Ask Copilot to: “Optimize this for performance—replace row context with iterators only where needed, use variables, remove unnecessary ALL.”

Governance, Capacity, and Privacy Considerations

• Capacity: Ensure your workspace is in Fabric F64 or Premium (P‑SKU) capacity.
• Admin: Some features require tenant admin enablement.
• Privacy: Follow your organization’s data governance and sensitivity labels. Copilot respects the model security context (e.g., RLS), but verify how AI features are configured in your tenant.

Power Tips for Daily Workflows

• Prototype in DAX Query View: Validate logic quickly before creating measures.
• Ask for alternatives: “Provide two different DAX approaches and explain the trade‑offs.”
• Generate documentation: “Explain this measure and produce a short description I can paste into the model.”
• Create visuals faster: “Build a bar chart showing top 10 products by [metric] this quarter.” Then refine.

FAQ

• Do I need to be a DAX expert to use Copilot?
• No. Copilot helps you generate and understand DAX. You’ll learn faster by reviewing its explanations.

• Does Copilot replace best‑practice modeling?
• No. A clean star schema, proper relationships, and a marked Date table still matter. Copilot amplifies good models.

• Can Copilot create full reports?
• Yes, Copilot can draft visuals and summaries from prompts. You can iterate and refine like you would manually—just faster.

Next Steps

• New to Power BI? Start here: What is Microsoft Power BI?
• Need a broader BI foundation? Explore: Mastering Business Intelligence: A Beginner’s Guide
• Looking for a practical use case? See: NPS and Power BI: the perfect combination for your business

With Power BI Copilot, writing and explaining DAX queries becomes straightforward and scalable. Use clear prompts, maintain a tidy model, validate Copilot’s output, and you’ll ship insights—fast.