The answer is to remove any columns from the fact table that are not used in the model. This is correct because each column in a Power BI table is imported into the VertiPaq storage engine, where it consumes memory for compression and dictionary storage; eliminating unused columns directly reduces the data footprint without affecting query performance or refresh logic. On the Microsoft Power BI Data Analyst PL-300 exam, this concept often appears in scenario-based questions where you must choose between removing columns, filtering rows, or changing data types—the common trap is to focus on row reduction instead of column elimination, which has a far greater impact on model size. To minimize Power BI model size by removing columns, remember that every column multiplies memory usage across millions of rows, so if a column isn’t used in measures, relationships, or visuals, it should be deleted before import. Memory tip: “Columns cost, rows don’t—drop the dead weight.”
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Remove any columns from the fact table that are not used in the model.
Removing unused columns directly reduces the data loaded into the model.
B
Set the 'Storage mode' of fact table to 'DirectQuery'.
Why wrong: DirectQuery reduces model size but may impact performance; the question asks to minimize size, but it does not specify query mode. DirectQuery is a valid option but it changes query behavior.
C
Enable 'Include relationship columns' in the relationship settings.
Why wrong: This adds more columns, increasing size.
D
Create a calculated column for row number.
Why wrong: Calculated columns increase model size.
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 VertiPaq engine, which directly minimizes the model size. Each column consumes memory for compression and storage, so eliminating unnecessary columns is the most effective way to reduce the model footprint without changing query performance or data refresh behavior.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 directly reduces the data loaded into the model.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set the 'Storage mode' of fact table to 'DirectQuery'.
Why it's wrong here
DirectQuery reduces model size but may impact performance; the question asks to minimize size, but it does not specify query mode. DirectQuery is a valid option but it changes query behavior.
✗
Enable 'Include relationship columns' in the relationship settings.
Why it's wrong here
This adds more columns, increasing size.
✗
Create a calculated column for row number.
Why it's wrong here
Calculated columns increase model size.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse minimizing model size with optimizing query performance, leading them to choose DirectQuery (Option B) even though the question explicitly asks for minimizing model size in an imported model context.
Detailed technical explanation
How to think about this question
Under the hood, Power BI's VertiPaq engine uses columnar compression (e.g., value encoding, hash encoding, and run-length encoding) to store data efficiently. Removing unused columns eliminates entire column segments from the compression dictionary and storage, which can significantly reduce the model size, especially in fact tables with millions of rows. In real-world scenarios, a fact table might include surrogate keys or audit columns that are not needed for analysis; dropping these can reduce model size by 20–40% without affecting any measures or relationships.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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 VertiPaq engine, which directly minimizes the model size. Each column consumes memory for compression and storage, so eliminating unnecessary columns is the most effective way to reduce the model footprint without changing query performance or data refresh behavior.
What should I do if I get this PL-300 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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