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

Practice Questions:

Here are 10 questions to test and help solidify your learning and knowledge. As you review these and other questions in your preparation, make sure to …

• Identifying and understand why an option is correct (or incorrect) — not just which one
• Look for and understand the usage scenario of keywords in exam questions to guide you
• Expect scenario-based questions rather than direct definitions

1. A Power BI report built on a large semantic model is slow to respond. Performance Analyzer shows long DAX query times but minimal visual rendering time. Where should you focus first?

A. Reducing the number of visuals
B. Optimizing DAX measures and model design
C. Changing visual types
D. Disabling report interactions

✅ Correct Answer: B

Explanation:
If DAX query time is the bottleneck, the issue lies in measure logic, relationships, or model design, not visuals.

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2. Which storage mode typically provides the best interactive performance for large Delta tables stored in OneLake?

A. Import
B. DirectQuery
C. Direct Lake
D. Live connection

✅ Correct Answer: C

Explanation:
Direct Lake queries Delta tables directly in OneLake, offering better performance than DirectQuery while avoiding full data import.

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3. Which modeling change most directly improves query performance in enterprise-scale semantic models?

A. Using many-to-many relationships
B. Converting snowflake schemas to star schemas
C. Increasing column cardinality
D. Enabling bidirectional filtering

✅ Correct Answer: B

Explanation:
A star schema simplifies joins and filter propagation, improving both storage engine efficiency and DAX performance.

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4. A measure uses multiple nested SUMX and FILTER functions over a large fact table. Which change is most likely to improve performance?

A. Replace the measure with a calculated column
B. Introduce DAX variables to reuse intermediate results
C. Add more visuals to cache results
D. Convert the table to DirectQuery

✅ Correct Answer: B

Explanation:
Using DAX variables (VAR) prevents repeated evaluation of expressions, significantly improving formula engine performance.

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5. Which practice helps reduce memory usage and improve performance in Import mode models?

A. Keeping all columns for future use
B. Increasing the number of calculated columns
C. Removing unused columns and tables
D. Enabling Auto Date/Time for all tables

✅ Correct Answer: C

Explanation:
Removing unused columns reduces model size, memory consumption, and scan time, improving overall performance.

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6. What is the primary benefit of using aggregation tables in composite models?

A. They eliminate the need for relationships
B. They allow queries to be answered without scanning detailed fact tables
C. They automatically optimize visuals
D. They replace Direct Lake storage

✅ Correct Answer: B

Explanation:
Aggregation tables allow Power BI to satisfy queries using pre-summarized Import data, avoiding expensive scans of large fact tables.

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7. Which visual design choice is most likely to degrade report performance?

A. Using explicit measures
B. Limiting visuals per page
C. Using high-cardinality fields in slicers
D. Using report-level filters

✅ Correct Answer: C

Explanation:
Slicers on high-cardinality columns generate expensive queries and increase interaction overhead.

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8. When optimizing report interactions, which action can improve performance without changing the data model?

A. Enabling all cross-highlighting
B. Disabling unnecessary visual interactions
C. Adding calculated tables
D. Switching to DirectQuery

✅ Correct Answer: B

Explanation:
Disabling unnecessary visual interactions reduces the number of queries triggered by user actions.

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9. Which DAX practice is recommended for improving performance in enterprise semantic models?

A. Use implicit measures whenever possible
B. Prefer calculated columns over measures
C. Minimize row context and iterators on large tables
D. Use ALL() in every calculation

✅ Correct Answer: C

Explanation:
Iterators and row context are expensive on large tables. Minimizing their use improves formula engine efficiency.

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10. Performance Analyzer shows fast query execution but slow visual rendering. What is the most likely cause?

A. Inefficient DAX measures
B. Poor relationship design
C. Too many or overly complex visuals
D. Incorrect storage mode

✅ Correct Answer: C

Explanation:
When rendering time is high but queries are fast, the issue is usually visual complexity, not the model or DAX.

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