PL-300 Prepare the data Practice Question
You are designing a Power BI data model for a sales analytics solution. The source data includes a 'Sales' fact table with millions of rows and dimension tables for 'Customer', 'Product', 'Date', and 'Salesperson'. You need to minimize the model size in Power BI. Which action should you take?
⚠ Common exam trap
The trap is that candidates might choose DirectQuery to avoid importing data, but the question asks about minimizing model size in an imported model context (implied by the need to minimize size). DirectQuery is a different architecture, not a means of optimizing the size of an imported model.
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 any columns from the fact table that are not used in the model.
Removing unused columns from the fact table reduces the amount of data imported into the Power BI model, which directly minimizes model size. Each column consumes memory for compression and storage, so eliminating unnecessary columns reduces the model footprint. DirectQuery (Option B) would also reduce the amount of data stored in the model, but it changes the storage mode to query the source directly, which is not the intended approach for minimizing the size of an imported data model.
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 any columns from the fact table that are not used in the model.
Why this is correct
Removing unused columns from the fact table directly reduces the amount of data imported into the model. Each column consumes memory for storage and compression dictionaries; eliminating unnecessary columns lowers the overall model footprint and speeds up refresh and query times. This is a foundational data modeling best practice for minimizing model size without changing query behavior or architecture.
- ✗
Set the 'Storage mode' of fact table to 'DirectQuery'.
Why it's wrong here
Switching the fact table to DirectQuery prevents data from being cached, so it does reduce the in-memory footprint. However, this is a fundamental architectural change that sends every query to the source system, which can cause performance degradation and requires a stable, high-throughput connection. The goal here is model size minimization; DirectQuery changes the entire connectivity model rather than simply trimming unused data, so it is not an appropriate general-purpose optimization.
- ✗
Enable 'Include relationship columns' in the relationship settings.
Why it's wrong here
Enabling 'Include relationship columns' adds extra columns to the fact table that are not needed for the analysis. These columns often duplicate foreign key values from related dimension tables, increasing data redundancy and memory usage. In a model optimized for size, you want to avoid any unnecessary duplication, so this option runs counter to the goal.
- ✗
Create a calculated column for row number.
Why it's wrong here
Creating a calculated column for a row number computes and stores a value for every row in the table, which increases memory consumption and extends refresh time. A row number is rarely needed in a Power BI model for analytics and would be an entirely artificial addition. Calculated columns are always evaluated during load, so they add permanent overhead to the model.
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