Top Power Query Performance Best Practices for Power BI
Which TWO are best practices for optimizing Power Query performance? (Choose two.)
Quick Answer
The answer is to filter data as early as possible in the query to reduce row counts. This is a core Power Query performance best practice because it minimizes the amount of data loaded into memory and processed during subsequent transformation steps, directly reducing query execution time and resource consumption. On the Microsoft Power BI Data Analyst PL-300 exam, this concept tests your understanding of query folding and data reduction strategies; a common trap is assuming that all filtering should happen in the report layer rather than at the source or early in the M code. Remember that every transformation step in Power Query is evaluated sequentially, so pushing filters upstream prevents unnecessary data from being carried through later steps. A simple memory tip is "filter first, transform later" — treat your data like a funnel, narrowing it down before applying complex logic.
⚠ Common exam trap
Test-takers frequently confuse data transformation best practices (like merging or splitting columns) with performance optimization techniques, leading them to select options B or E instead of focusing on reducing data volume and controlling query execution.
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
✓
Disable the 'Enable background refresh' option in the query settings.
Disabling 'Enable background refresh' prevents Power Query from running queries in the background while you continue working in Power BI Desktop. This ensures that query execution is synchronous, which can improve performance by avoiding resource contention and allowing you to monitor progress directly. Background refresh can cause delays and unexpected behavior when multiple queries run simultaneously, especially with large data sources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Disable the 'Enable background refresh' option in the query settings.
Why this is correct
Disabling background refresh can improve performance by preventing simultaneous refreshes.
- ✗
Merge tables as early as possible in the query to combine data.
Why it's wrong here
Merging early may cause large data volumes to be processed; delay merging if possible.
- ✗
Keep all columns in the table to avoid missing data.
Why it's wrong here
Removing unnecessary columns reduces memory and processing time.
- ✓
Filter data as early as possible in the query to reduce row counts.
Why this is correct
Early filtering reduces the amount of data carried through subsequent steps.
- ✗
Split columns by delimiter to normalize data.
Why it's wrong here
Splitting columns increases the number of columns and processing steps, potentially reducing performance.
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Same concept, more angles
3 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 optimizing data load performance in Power BI?
hard- ✓ A.Remove unnecessary columns and rows during the import process.
- B.Split a large fact table into multiple smaller fact tables.
- ✓ C.Set data types correctly in Power Query to avoid type detection overhead.
- D.Use DirectQuery mode instead of Import mode to reduce data load time.
- ✓ E.Use query folding to push transformations to the source database.
Why A: 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.
Variation 2. 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 3. 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.