The correct answer is to import the data into Power BI and design a star schema with separate date, product, and customer dimension tables linked to a sales fact table. This approach minimizes memory usage because Import mode leverages the VertiPaq engine, which applies aggressive columnar compression—star schemas enhance this compression by reducing high-cardinality columns and enabling dictionary encoding, drastically lowering the memory footprint for millions of rows. On the PL-300 exam, this scenario tests your understanding of how data modeling choices directly impact performance; a common trap is choosing DirectQuery for large tables, which avoids memory but sacrifices load-time optimization and compression benefits. Remember that Import mode with a star schema is the gold standard for balancing analytical flexibility with storage efficiency. Memory tip: “Star schemas starve memory waste”—the fewer joins and lower cardinality in dimensions, the tighter the compression.
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 connecting Power BI to a SQL Server database. The database contains a table with millions of sales transactions. You need to design a data model that minimizes load time and memory usage while still allowing analysis of sales by date, product, and customer. Which modeling approach should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Import the data into Power BI, creating a star schema with date, product, and customer dimension tables and the sales fact table.
Option C is correct because importing the data into Power BI and modeling it as a star schema with separate dimension tables (date, product, customer) and a sales fact table minimizes load time and memory usage through columnar compression and optimized query performance. Import mode stores data in the VertiPaq engine, which compresses data efficiently, especially when using a star schema, reducing memory footprint and enabling fast in-memory analysis. This approach balances storage efficiency with analytical flexibility for millions of rows.
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.
✗
Use a composite model with some tables in DirectQuery and others in Import.
Why it's wrong here
Composite models introduce complexity and may not be necessary for this scenario.
✗
Use DirectQuery mode to avoid storing data in Power BI.
Why it's wrong here
DirectQuery may cause slow performance for large queries and is not ideal for high-volume analysis.
✓
Import the data into Power BI, creating a star schema with date, product, and customer dimension tables and the sales fact table.
Why this is correct
Import mode with a star schema optimizes performance and memory usage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a live connection to an existing SQL Server Analysis Services tabular model.
Why it's wrong here
This assumes an existing SSAS model, which may not be available and adds overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose DirectQuery (Option B) thinking it saves memory by not storing data, but they overlook that Import mode with a star schema actually minimizes memory usage through compression and is faster for interactive analysis, while DirectQuery increases load on the source and can degrade performance.
Trap categories for this question
Scenario analysis trap
Composite models introduce complexity and may not be necessary for this scenario.
Detailed technical explanation
How to think about this question
Under the hood, Power BI's Import mode uses the VertiPaq storage engine, which applies columnar compression techniques like value encoding and dictionary encoding to reduce memory usage significantly—often achieving 10x compression ratios for fact tables. A star schema design further optimizes this by separating high-cardinality dimensions (e.g., date, product, customer) from the fact table, allowing Power BI to use relationship-based filtering without duplicating data. In real-world scenarios, importing millions of sales transactions with a star schema can reduce memory consumption by up to 90% compared to a flat table, while DirectQuery would require constant round-trips to SQL Server, increasing latency and load on the source database.
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: Import the data into Power BI, creating a star schema with date, product, and customer dimension tables and the sales fact table. — Option C is correct because importing the data into Power BI and modeling it as a star schema with separate dimension tables (date, product, customer) and a sales fact table minimizes load time and memory usage through columnar compression and optimized query performance. Import mode stores data in the VertiPaq engine, which compresses data efficiently, especially when using a star schema, reducing memory footprint and enabling fast in-memory analysis. This approach balances storage efficiency with analytical flexibility for millions of rows.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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