PL-300 Prepare the data Practice Question
You are importing a large CSV file (200 MB) into Power BI Desktop. The import is very slow and sometimes fails. What should you do to improve performance?
⚠ Common exam trap
Test-takers frequently assume upgrading to Premium or using the Service will magically fix performance issues, but the PL-300 exam emphasizes that data reduction during import (via Power Query filtering) is the primary technique to optimize large file ingestion in Power BI Desktop.
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 rows and columns during import using Power Query to reduce data size.
Filtering rows and columns via Power Query is the correct approach to reduce the data loaded into the Power BI Desktop model. However, query folding does not apply to CSV files — it is a technique for database sources. For CSV, Power Query reads the entire file but still reduces memory and processing overhead by discarding unnecessary columns and rows before loading into the model. This is a standard best practice for large flat files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Power BI Service to import the file instead.
Why it's wrong here
The Power BI Service is a cloud collaboration and sharing platform, not a file import engine. It cannot directly import local CSV files; you must first load the file into Power BI Desktop, then publish the dataset or use a gateway for scheduled refreshes. Thus, using the service does not circumvent the Desktop import process and will not improve the initial 200MB CSV loading performance.
- ✗
Remove all relationships before import.
Why it's wrong here
Relationships in Power BI are modeling metadata defined after the data has already been loaded into the data model. They are not part of the CSV parsing or transformation pipeline, so removing them has zero effect on the number of rows read from the source file or the memory consumed during import. The performance bottleneck for a 200MB CSV is during the read and transformation stage, which occurs well before any relationship definitions are evaluated.
- ✓
Filter rows and columns during import using Power Query to reduce data size.
Why this is correct
Power Query serves as the extraction and transformation engine that feeds the Power BI data model. By applying row filters and removing unneeded columns in Power Query, you prevent the full 200MB CSV from ever being stored in the in-memory columnar engine. This reduces the data volume that must be processed, loaded, and compressed, directly accelerating the import step and reducing the final model size and memory footprint.
- ✗
Upgrade to Power BI Premium.
Why it's wrong here
Power BI Premium provides dedicated cloud capacity for hosting and refreshing published datasets, but the initial import of a local CSV file occurs entirely within Power BI Desktop on your local machine. Premium capacity does not alter how Desktop parses a 200MB file or how much local memory it consumes during the load, because that processing happens before any data is uploaded to the service. Therefore, upgrading to Premium does not address the Desktop-side performance bottleneck for this import scenario.
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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.