Power BI DAX Query View with Copilot: Generate Complex DAX in Seconds

The January 2026 Power BI update introduced a game-changing feature: **DAX Query View with Copilot integration**. This powerful combination allows developers to write, test, and optimize DAX queries using natural language AI assistance—dramatically accelerating semantic model development. This guide covers everything you need to master this new capability. Our Power BI consulting team helps organizations implement advanced DAX optimization strategies.

What is DAX Query View?

The New Development Experience

DAX Query View is a dedicated workspace in Power BI Desktop for writing and executing DAX queries directly against your semantic model. Think of it as "SQL Server Management Studio for DAX."

Key capabilities: - Write DAX queries without creating visuals - Test measure logic before adding to model - Explore table data with ad-hoc queries - Benchmark query performance - Export query results to CSV

Why it matters: Previously, testing DAX required creating temporary visuals or using external tools like DAX Studio. Now it's built into Power BI Desktop.

For DAX fundamentals, review our essential DAX patterns guide.

Copilot Integration: AI-Powered DAX Generation

How Copilot Works in Query View

Simply describe what you want in natural language, and Copilot generates the DAX code:

Example 1: Time Intelligence

Your prompt: "Show me sales for each month in 2025 compared to same month last year"

Copilot generates: ```dax EVALUATE SUMMARIZECOLUMNS( 'Date'[Year Month], "Sales 2025", CALCULATE([Total Sales], 'Date'[Year] = 2025), "Sales 2024", CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date])), "YoY Growth %", DIVIDE([Sales 2025] - [Sales 2024], [Sales 2024]) ) ORDER BY 'Date'[Year Month] ```

Example 2: Customer Segmentation

Your prompt: "Segment customers by total lifetime spend: VIP > $50K, High Value $10K-$50K, Standard < $10K"

Copilot generates: ```dax EVALUATE ADDCOLUMNS( VALUES(Customer[Customer ID]), "Total Spend", [Total Sales], "Segment", SWITCH( TRUE(), [Total Sales] > 50000, "VIP", [Total Sales] >= 10000, "High Value", "Standard" ) ) ORDER BY [Total Spend] DESC ```

Benefits Over Manual DAX Writing

70% faster development: Copilot handles boilerplate syntax Fewer errors: AI generates syntactically correct DAX Learning tool: See how experts write complex queries Consistency: Standardized coding patterns across team

Use Cases for DAX Query View

Use Case 1: Rapid Measure Prototyping

Scenario: Business asks for "rolling 3-month average sales by product category"

Old workflow: 1. Create measure in model 2. Add to visual to test 3. Realize it's wrong 4. Edit measure 5. Refresh visual 6. Repeat 5-10 times

New workflow with Query View: 1. Ask Copilot: "3-month rolling average sales by product category" 2. Test query immediately 3. Iterate until correct 4. Copy working DAX to measure 5. Done in 5 minutes

Use Case 2: Performance Benchmarking

Problem: Report loads slowly—which DAX measure is the bottleneck?

Solution: Test measures individually in Query View

```dax -- Test Measure 1 performance EVALUATE SUMMARIZECOLUMNS( Product[Category], "Complex Calculation", [Measure With Many Filters] )

-- Execution time: 3.2 seconds (slow!) ```

Optimization: Rewrite measure, test again in Query View

```dax EVALUATE SUMMARIZECOLUMNS( Product[Category], "Optimized Calculation", [Rewritten Measure] )

-- Execution time: 0.4 seconds (8x faster!) ```

Use Case 3: Data Quality Auditing

Check for duplicates:

Prompt: "Find duplicate customer IDs in my customer table"

```dax EVALUATE FILTER( ADDCOLUMNS( VALUES(Customer[Customer ID]), "Count", CALCULATE(COUNTROWS(Customer)) ), [Count] > 1 ) ```

Check for missing values:

Prompt: "Show me orders with missing customer information"

```dax EVALUATE FILTER( Orders, ISBLANK(Orders[Customer ID]) ) ```

Use Case 4: Ad-Hoc Analysis Without Visuals

Business question: "Which products were sold in Q4 2025 but not in Q3 2025?"

Prompt: "Products sold in Q4 2025 but not Q3 2025"

```dax EVALUATE EXCEPT( VALUES(Sales[Product ID], Sales[Date] >= DATE(2025,10,1) && Sales[Date] <= DATE(2025,12,31)), VALUES(Sales[Product ID], Sales[Date] >= DATE(2025,7,1) && Sales[Date] <= DATE(2025,9,30)) ) ```

Result: Instant answer without creating a report page.

Best Practices for DAX Query View with Copilot

Practice 1: Start with Clear, Specific Prompts

Bad prompt: "sales stuff" Good prompt: "Total sales by region for last 6 months, sorted descending"

Bad prompt: "customer analysis" Good prompt: "Top 10 customers by lifetime value with order count and average order value"

Practice 2: Iterate with Copilot

If first result isn't perfect, refine your prompt:

First prompt: "Monthly sales trend" Copilot result: Shows all months (too much data)

Refined prompt: "Monthly sales trend for last 12 months only" Better result: Focused on recent data

Practice 3: Validate AI-Generated DAX

Copilot is powerful but not perfect. Always: 1. Review generated code for logic errors 2. Test with edge cases (nulls, zeros, missing data) 3. Compare results to known values 4. Check performance on large datasets

Practice 4: Save Useful Queries

Create a query library for common patterns: - Time intelligence templates - Customer segmentation queries - Data quality checks - Performance benchmarks

Export as .dax files or document in your wiki.

Practice 5: Combine with DAX Formatter

After Copilot generates code, format it for readability:

Copilot output (compressed): ```dax EVALUATE SUMMARIZECOLUMNS('Date'[Year],"Sales",[Total Sales],"Profit",[Total Profit]) ```

Formatted (readable): ```dax EVALUATE SUMMARIZECOLUMNS( 'Date'[Year], "Sales", [Total Sales], "Profit", [Total Profit] ) ORDER BY 'Date'[Year] ```

Use DAX Formatter or Power BI's built-in formatter.

Advanced DAX Query View Techniques

Technique 1: Table Relationships Exploration

Discover which tables are related:

```dax EVALUATE INFO.RELATIONSHIPS() ```

Returns all relationships in model with: - From/To tables and columns - Cardinality (1:Many, Many:1) - Cross-filter direction - Active vs inactive

Technique 2: Column Statistics

Analyze column distribution:

Prompt: "Show me distribution of customer segments"

```dax EVALUATE SUMMARIZECOLUMNS( Customer[Segment], "Customer Count", COUNTROWS(Customer), "Percentage", DIVIDE(COUNTROWS(Customer), CALCULATE(COUNTROWS(Customer), ALL(Customer))) ) ```

Technique 3: Measure Dependencies

Find which measures use a specific measure:

```dax EVALUATE INFO.MEASURES() ```

Filter for measures referencing [Total Sales]:

Useful for impact analysis before changing measures.

Technique 4: Simulating What-If Parameters

Test scenarios without creating parameters:

```dax EVALUATE VAR PriceIncrease = 1.10 -- 10% price increase RETURN ADDCOLUMNS( VALUES(Product[Product Name]), "Current Revenue", [Total Sales], "Projected Revenue", [Total Sales] * PriceIncrease, "Revenue Increase", [Total Sales] * PriceIncrease - [Total Sales] ) ORDER BY [Revenue Increase] DESC ```

Copilot Prompting Patterns for Common DAX Tasks

Time Intelligence Patterns

Year-over-Year: "Compare [metric] this year vs last year by [dimension]"

Month-over-Month: "Show [metric] month-over-month growth rate for last 12 months"

Year-to-Date: "Calculate YTD [metric] for each year in my data"

Rolling Average: "3-month rolling average of [metric] by [dimension]"

Ranking and Top N

Top Performers: "Top 10 [dimension] by [metric] descending"

Bottom Performers: "Bottom 5 [dimension] by [metric] with percentage of total"

Rank with Ties: "Rank customers by sales, showing rank and handling ties"

Segmentation and Bucketing

Quintiles: "Divide customers into 5 equal groups by lifetime value"

Custom Ranges: "Bucket products by price: Low < $50, Medium $50-$200, High > $200"

Percentile Analysis: "Show products in top 20% by sales volume"

Aggregations and Summaries

Grouped Summaries: "Total sales and average order value by customer segment and region"

Running Totals: "Running total of sales by date ascending"

Weighted Averages: "Weighted average product price by sales quantity"

Troubleshooting Common Issues

Issue 1: Copilot Generates Incorrect DAX

Symptom: Query runs but returns wrong results

Causes: - Ambiguous prompt ("sales" could mean revenue or units sold) - Copilot misunderstands your data model structure - Time intelligence without proper date table

Fix: - Be more specific in prompts - Provide context: "sales revenue in dollars" vs "sales units sold" - Verify date table is marked correctly - Manually adjust generated DAX

Issue 2: Query Takes Too Long

Symptom: DAX Query View hangs or times out

Causes: - Querying too many rows (millions) - Inefficient DAX (nested iterators, lack of filters) - Large calculated columns in model

Fix: - Add filters to limit data: WHERE 'Date'[Year] = 2025 - Use SUMMARIZECOLUMNS instead of ADDCOLUMNS + SUMMARIZE - Test on sample data first - Optimize underlying data model

For performance optimization strategies, see our Power BI performance guide.

Issue 3: Copilot Doesn't Recognize Table Names

Symptom: "Table not found" error in generated DAX

Causes: - Table renamed after Copilot learned model - Table name includes special characters or spaces - Table hidden from report view

Fix: - Use exact table names with single quotes: 'Sales Data' - Refresh Copilot context (close/reopen Query View) - Unhide tables temporarily

Integration with Existing Workflows

Query View + DAX Studio

Use Query View for: Quick prototyping, AI-assisted generation Use DAX Studio for: Advanced performance analysis, VertiPaq Analyzer, query plan inspection

Workflow: 1. Generate initial DAX in Query View with Copilot 2. Export to DAX Studio for performance tuning 3. Optimize with DAX Studio tools 4. Bring optimized DAX back to model

Query View + Tabular Editor

Use Query View for: Functional DAX development Use Tabular Editor for: Bulk measure creation, model documentation, deployment

Workflow: 1. Prototype measures in Query View 2. Export working DAX 3. Use Tabular Editor to create 50 similar measures via scripting 4. Deploy to production with version control

Query View + Power BI Service

Limitation: Query View only available in Power BI Desktop Workaround: Use Copilot in Service for report-level queries

Security Considerations

Row-Level Security (RLS) in Query View

Important: Query View respects RLS roles when "View as Role" is enabled.

Testing RLS: 1. Enable "View as Role" in Modeling tab 2. Select role (e.g., "Sales Manager - West Region") 3. Run query in Query View 4. Results filtered to role permissions

Without "View as Role": Query View shows ALL data (admin view).

Sensitive Data in Queries

Risk: Exporting query results with PII/confidential data

Mitigation: - Apply RLS in development models - Redact sensitive columns in exports - Use data classification labels - Audit Query View usage in Fabric Capacity Metrics

Future Enhancements (Roadmap)

Microsoft plans to expand Query View capabilities:

2026 Roadmap: - Save favorite queries in Query View - Share queries across team (Query Library) - Schedule queries to run on refresh - Export query results to OneLake automatically - Copilot suggests query optimizations

Conclusion

DAX Query View with Copilot transforms Power BI development from manual coding to AI-assisted data exploration. Key benefits:

Speed: 70% faster measure development Accuracy: Fewer syntax errors with AI generation Learning: See expert DAX patterns instantly Flexibility: Test logic without creating visuals Performance: Benchmark queries before deploying

Organizations adopting this workflow report: - 50% reduction in development time - 30% fewer production bugs (caught in Query View testing) - Higher team DAX proficiency (learning from AI)

Start using Query View today—it's available in Power BI Desktop January 2026 release. Requires Fabric F64+ capacity or Power BI Premium P1+ for Copilot features.

Ready to accelerate your DAX development? Contact our Power BI experts for training and implementation support.

**Sources**: - Microsoft Learn: Power BI January 2026 Feature Summary - Power BI Blog: January 2026 Update - Microsoft Fabric Blog: January 2026 Features