Improve Data Refresh Performance in Power BI
Which TWO actions can improve data refresh performance in Power BI?
Quick Answer
The answer is filtering rows at the source to reduce data volume and disabling load for intermediate queries used only as reference steps. Filtering at the source, such as using SQL WHERE clauses or Power Query’s native query folding, minimizes the amount of data imported into the data model, directly cutting refresh time and memory overhead. Disabling load for intermediate queries prevents Power BI from materializing tables that serve only as transformation steps, so the engine skips loading unnecessary data into the model. On the PL-300 exam, this tests your understanding of Power Query’s query dependencies and the difference between reference queries and loaded tables; a common trap is assuming all queries must be loaded to the model. To improve data refresh performance in Power BI, always push filtering as far upstream as possible and treat intermediate reference queries as disposable steps. Memory tip: “Filter first, disable the rest” — reduce volume at the source, then turn off load for helper queries.
⚠ Common exam trap
It's easy for candidates to confuse 'disable load' with 'disable refresh' or think that merging queries (Option A) is always beneficial, when in fact it can reduce parallelism and hurt performance.
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 load for intermediate queries used only for reference.
Disabling load for intermediate queries used only as reference steps prevents Power BI from materializing those tables in the data model. This reduces memory consumption and refresh time, as the engine skips loading data that isn't needed for reports or further transformations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Merge all queries into a single query.
Why it's wrong here
Merging queries can increase complexity and refresh time.
- ✗
Add calculated columns in Power Query instead of DAX.
Why it's wrong here
Calculated columns increase model size and refresh time.
- ✓
Disable load for intermediate queries used only for reference.
Why this is correct
Prevents unnecessary data loading.
- ✓
Filter rows at the source to reduce data volume.
Why this is correct
Fewer rows means faster refresh.
- ✗
Keep all columns from the source data to avoid re-importing.
Why it's wrong here
Unnecessary columns increase data volume.
Go deeper
Related to this question
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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 TWO actions can help reduce the size of a Power BI dataset when preparing data?
medium- A.Include all historical data
- ✓ B.Aggregate transaction data to daily level
- C.Add calculated columns
- ✓ D.Remove columns that are not used in reports
- E.Use DirectQuery mode
Why B: Aggregating transaction data to a daily level reduces the number of rows in the dataset, which directly decreases the storage footprint and improves refresh performance. Power BI compresses data more efficiently when cardinality is lower, and fewer rows mean smaller column dictionaries and reduced page compression overhead.
Variation 2. Which TWO actions should you take to reduce the size of a Power BI dataset? (Choose two.)
medium- ✓ A.Filter out rows that are not needed.
- ✓ B.Remove unnecessary columns during import.
- C.Disable query folding to improve performance.
- D.Add calculated columns to precompute values.
- E.Use DirectQuery instead of Import.
Why A: Filtering out unnecessary rows at the source reduces the number of rows loaded into the Power BI dataset, directly decreasing the data volume and storage size. This is a fundamental data reduction technique that minimizes memory consumption and improves refresh performance.
Variation 3. Which THREE actions in Power Query Editor can improve the performance of data refresh? (Select three.)
hard- A.Sort data in ascending order to improve compression.
- B.Merge queries before filtering.
- ✓ C.Remove columns that are not used in the report.
- ✓ D.Disable the 'Enable load' option for intermediate tables that are not needed in the model.
- ✓ E.Filter rows as early as possible in the query.
Why C: Removing unused columns reduces the amount of data loaded into the Power Query engine and the data model, directly decreasing memory usage and refresh time. Power Query processes all columns in a query step, so eliminating unnecessary columns early minimizes the data footprint for subsequent transformations and the final load.
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.