Performance Optimization in DAX is about writing efficient formulas that calculate faster, use fewer resources, and scale well with large datasets. By applying best practices like minimizing row context, reducing repeated calculations, and using efficient functions, you can significantly improve Power BI report performance.
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Which DAX function is generally faster for conditional logic?
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Question 2 of 15
2. Question
What is one of the best practices for optimizing DAX calculations?
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Question 3 of 15
3. Question
How can using variables improve DAX performance?
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Question 4 of 15
4. Question
Which mode is generally faster for small datasets?
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Question 5 of 15
5. Question
What is a major performance drawback of DirectQuery mode?
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Question 6 of 15
6. Question
Which DAX function should you avoid for large datasets due to row-by-row processing?
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Question 7 of 15
7. Question
What is a benefit of aggregating data before loading into Power BI?
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Question 8 of 15
8. Question
Which of the following can improve DAX performance?
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Question 9 of 15
9. Question
Why should you avoid using the FORMAT function in heavy calculations?
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Question 10 of 15
10. Question
Which storage engine does DAX use for Import mode?
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Question 11 of 15
11. Question
How can you test DAX performance for optimization?
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Question 12 of 15
12. Question
What is the recommended way to handle complex calculations?
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Question 13 of 15
13. Question
Why should you avoid SELECTEDVALUE inside heavy iteration functions?
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Question 14 of 15
14. Question
Which filtering approach is faster in DAX?
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Question 15 of 15
15. Question
What is one key principle of DAX performance optimization?
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Summary
These Performance Optimization in DAX MCQs cover techniques like using measures over calculated columns, leveraging variables, reducing cardinality, and avoiding expensive functions. Mastering these best practices ensures your Power BI reports run faster and scale efficiently for large datasets.
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