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

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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

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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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