The answer is a star schema with Sales as a fact table and Customers and Products as dimension tables. This approach optimizes query performance and storage in Power BI by separating transactional data from descriptive attributes, which reduces data duplication and improves columnar compression. The star schema enables efficient filter propagation through one-to-many relationships, allowing Power BI to perform faster aggregations and avoid the bloated row context of a flat table. On the PL-300 exam, this tests your understanding of dimensional modeling best practices—a common trap is choosing a single flat table for simplicity, which actually degrades performance due to redundant data and larger memory footprint. Remember the memory tip: “Facts are skinny and tall; dimensions are wide and small”—fact tables store numeric measures and foreign keys, while dimension tables hold the descriptive text, keeping the model lean and fast for analytical queries.
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 data model for a sales analysis report. The source data includes a Sales table with columns: OrderID, CustomerID, ProductID, OrderDate, Quantity, and UnitPrice. You also have a Customers table and a Products table. Which approach best optimizes query performance and storage?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a star schema with Sales as a fact table and Customers and Products as dimension tables.
Option A is correct because a star schema optimizes query performance and storage in Power BI by separating transactional data (Sales fact table) from descriptive attributes (Customers and Products dimension tables). This reduces data duplication, improves compression, and enables efficient aggregations and filter propagation via one-to-many relationships, which is the recommended modeling approach for analytical workloads.
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.
✓
Create a star schema with Sales as a fact table and Customers and Products as dimension tables.
Why this is correct
Star schema is recommended for optimal performance and simplicity.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create separate fact tables for each dimension.
Why it's wrong here
This is not a standard star schema; fact tables should contain measures.
✗
Create a single flat table by joining all columns from Sales, Customers, and Products into one table.
Why it's wrong here
This leads to denormalization and larger table size.
✗
Create a snowflake schema by normalizing Customers into multiple related tables.
Why it's wrong here
Snowflaking can degrade query performance in Power BI.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a flat table (Option C) thinking it simplifies the model, but they overlook the severe storage and performance penalties from data duplication, which is a key anti-pattern in Power BI data modeling.
Detailed technical explanation
How to think about this question
In Power BI, star schemas leverage columnstore indexes and VertiPaq compression more effectively by storing dimension tables with high cardinality columns separately from fact tables, reducing memory footprint. The one-to-many relationships allow DAX queries to use bidirectional filtering and automatic aggregations without redundant data, which is critical for large-scale sales analysis. A real-world scenario is a retail dataset with millions of orders; a star schema can reduce storage by 50-70% compared to a flat table while maintaining sub-second query response times.
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: Create a star schema with Sales as a fact table and Customers and Products as dimension tables. — Option A is correct because a star schema optimizes query performance and storage in Power BI by separating transactional data (Sales fact table) from descriptive attributes (Customers and Products dimension tables). This reduces data duplication, improves compression, and enables efficient aggregations and filter propagation via one-to-many relationships, which is the recommended modeling approach for analytical workloads.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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