PL-300 Prepare the data Practice Question
Which THREE of the following are best practices for data preparation in Power BI to improve performance and maintainability? (Select THREE.)
⚠ Common exam trap
Many exam-takers confuse 'maintainability' with 'avoiding changes to source names,' but Power BI encourages renaming for clarity, and the real performance pitfall is keeping unnecessary columns, not renaming them.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Filter out unnecessary rows as early as possible in the query
Filtering out unnecessary rows early in Power Query reduces the amount of data loaded into memory and processed in subsequent transformation steps. This practice, known as early filtering, minimizes the data footprint and improves both refresh performance and report responsiveness. By applying filters as the first transformation, you leverage query folding to push the filter logic to the source database, further enhancing efficiency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Filter out unnecessary rows as early as possible in the query
Why this is correct
Early filtering reduces data volume and improves performance.
- ✗
Avoid renaming columns in Power Query; use original names
Why it's wrong here
Renaming can improve readability and maintainability.
- ✓
Use query folding to push transformations back to the source
Why this is correct
Query folding improves performance by leveraging source database capabilities.
- ✓
Split complex queries into multiple steps for clarity
Why this is correct
Clear steps improve maintainability.
- ✗
Keep all columns from the source to avoid missing data
Why it's wrong here
Keeping unnecessary columns increases model size and reduces performance.
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