This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Implement and manage semantic models (25-30%) 
    --> Optimize enterprise-scale semantic models 
        --> Implement performance improvements in queries and report visuals

Performance optimization is a critical skill for the Fabric Analytics Engineer. In enterprise-scale semantic models, poor query design, inefficient DAX, or overly complex visuals can significantly degrade report responsiveness and user experience. This exam section focuses on identifying performance bottlenecks and applying best practices to improve query execution, model efficiency, and report rendering.

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1. Understand Where Performance Issues Occur

Performance problems typically fall into three layers:

a. Data & Storage Layer

• Storage mode (Import, DirectQuery, Direct Lake, Composite)
• Data source latency
• Table size and cardinality
• Partitioning and refresh strategies

b. Semantic Model & Query Layer

• DAX calculation complexity
• Relationships and filter propagation
• Aggregation design
• Use of calculation groups and measures

c. Report & Visual Layer

• Number and type of visuals
• Cross-filtering behavior
• Visual-level queries
• Use of slicers and filters

DP-600 questions often test your ability to identify the correct layer where optimization is needed.

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2. Optimize Queries and Semantic Model Performance

a. Choose the Appropriate Storage Mode

• Use Import for small-to-medium datasets requiring fast interactivity
• Use Direct Lake for large OneLake Delta tables with high concurrency
• Use Composite models to balance performance and real-time access
• Avoid unnecessary DirectQuery when Import or Direct Lake is feasible

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b. Reduce Data Volume

• Remove unused columns and tables
• Reduce column cardinality (e.g., avoid high-cardinality text columns)
• Prefer surrogate keys over natural keys
• Disable Auto Date/Time when not needed

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c. Optimize Relationships

• Use single-direction relationships by default
• Avoid unnecessary bidirectional filters
• Ensure relationships follow a star schema
• Avoid many-to-many relationships unless required

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d. Use Aggregations

• Create aggregation tables to pre-summarize large fact tables
• Enable query hits against aggregation tables before scanning detailed data
• Especially valuable in composite models

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3. Improve DAX Query Performance

a. Write Efficient DAX

• Prefer measures over calculated columns
• Use variables (VAR) to avoid repeated calculations
• Minimize row context where possible
• Avoid excessive iterators (SUMX, FILTER) over large tables

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b. Use Filter Context Efficiently

• Prefer CALCULATE with simple filters
• Avoid complex nested FILTER expressions
• Use KEEPFILTERS and REMOVEFILTERS intentionally

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c. Avoid Expensive Patterns

• Avoid EARLIER in favor of variables
• Avoid dynamic table generation inside visuals
• Minimize use of ALL when ALLSELECTED or scoped filters suffice

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4. Optimize Report Visual Performance

a. Reduce Visual Complexity

• Limit the number of visuals per page
• Avoid visuals that generate multiple queries (e.g., complex custom visuals)
• Use summary visuals instead of detailed tables where possible

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b. Control Interactions

• Disable unnecessary visual interactions
• Avoid excessive cross-highlighting
• Use report-level filters instead of visual-level filters when possible

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c. Optimize Slicers

• Avoid slicers on high-cardinality columns
• Use dropdown slicers instead of list slicers
• Limit the number of slicers on a page

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d. Prefer Measures Over Visual Calculations

• Avoid implicit measures created by dragging numeric columns
• Define explicit measures in the semantic model
• Reuse measures across visuals to improve cache efficiency

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5. Use Performance Analysis Tools

a. Performance Analyzer

• Identify slow visuals
• Measure DAX query duration
• Distinguish between query time and visual rendering time

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b. Query Diagnostics (Power BI Desktop)

• Analyze backend query behavior
• Identify expensive DirectQuery or Direct Lake operations

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c. DAX Studio (Advanced)

• Analyze query plans
• Measure storage engine vs formula engine time
• Identify inefficient DAX patterns

(You won’t be tested on tool UI details, but knowing when and why to use them is exam-relevant.)

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6. Common DP-600 Exam Scenarios

You may be asked to:

• Identify why a report is slow and choose the best optimization
• Identify the bottleneck layer (model, query, or visual)
• Select the most appropriate storage mode for performance
• Choose the least disruptive, most effective optimization
• Improve a slow DAX measure
• Reduce visual rendering time without changing the data source
• Optimize performance for enterprise-scale models
• Apply enterprise-scale best practices, not just quick fixes

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Key Exam Takeaways

• Always optimize the model first, visuals second
• Star schema + clean relationships = better performance
• Efficient DAX matters more than clever DAX
• Fewer visuals and interactions = faster reports
• Aggregations and Direct Lake are key enterprise-scale tools

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Practice Questions:

Go to the Practice Exam Questions for this topic.