Power BI Data Load Performance: Remove Columns, Set Data Types, Use Query Folding
Which THREE of the following are best practices for optimizing data load performance in Power BI?
Quick Answer
Query folding, which pushes Power Query's transformation steps back to the source database so it does the filtering, sorting, and aggregating instead of Power BI, is one of the most effective load-performance practices because it means far less raw data ever has to travel across the network and land in memory in the first place. This pairs naturally with a second practice that attacks the same problem from another angle: removing unnecessary columns and rows during the import process, so the data model only ever has to store and refresh what a report actually uses. Both practices reduce the same underlying cost -- the volume of data that must be transferred, processed, and held in memory -- one by delegating work to a more capable source system, the other by simply not bringing over data that isn't needed in the first place. Best-practice questions about data load performance in Power BI tend to reward this kind of thinking: rather than optimizing after the data has already landed in the model, look for ways to shrink or delegate the work upstream, at the source or early in the query, since anything trimmed or pushed back to the source never has to be moved, stored, or refreshed downstream at all.
⚠ Common exam trap
Many candidates confuse 'splitting tables' (Option B) with star schema design best practices, but splitting a fact table unnecessarily violates dimensional modeling principles and harms performance, whereas proper star schema involves splitting dimensions from facts, not splitting facts themselves.
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
✓
Remove unnecessary columns and rows during the import process.
Removing unnecessary columns and rows during the import process reduces the amount of data loaded into the Power BI data model, which directly decreases memory usage and refresh time. By filtering out irrelevant data early in Power Query, you minimize the data volume that must be processed and stored, leading to faster load performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Remove unnecessary columns and rows during the import process.
Why this is correct
Reducing data volume improves load time.
- ✗
Split a large fact table into multiple smaller fact tables.
Why it's wrong here
This can complicate the model and may not improve performance.
- ✓
Set data types correctly in Power Query to avoid type detection overhead.
Why this is correct
Proper data types improve load efficiency.
- ✗
Use DirectQuery mode instead of Import mode to reduce data load time.
Why it's wrong here
DirectQuery does not load data, but it is not always a performance best practice.
- ✓
Use query folding to push transformations to the source database.
Why this is correct
Query folding reduces data transfer.
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Same concept, more angles
2 more ways this is tested on PL-300
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE of the following are best practices for data preparation in Power BI to improve performance and maintainability? (Select THREE.)
hard- ✓ A.Filter out unnecessary rows as early as possible in the query
- B.Avoid renaming columns in Power Query; use original names
- ✓ C.Use query folding to push transformations back to the source
- ✓ D.Split complex queries into multiple steps for clarity
- E.Keep all columns from the source to avoid missing data
Why A: 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.
Variation 2. Which THREE of the following are best practices when preparing data in Power BI for optimal performance?
medium- A.Merge all tables into a single table for simplicity.
- B.Create calculated columns instead of measures when possible.
- ✓ C.Set correct data types for all columns.
- ✓ D.Remove unnecessary columns and rows during import.
- ✓ E.Use query folding to push transformations to the data source.
Why C: Setting correct data types for all columns (Option C) is a best practice because it ensures that Power BI uses the most efficient storage and processing methods. Incorrect data types can lead to increased memory usage, slower query performance, and inaccurate calculations, as Power BI must perform implicit conversions at query time.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.