The correct approach is to keep Customers and Products as separate dimension tables, and Orders as the fact table. This creates a classic star schema where the fact table holds quantitative measures like Quantity and UnitPrice along with foreign keys (CustomerID, ProductID) that connect to dimension tables containing descriptive attributes such as CustomerName and ProductName. This design minimizes table joins, speeds up aggregation, and aligns with best practices for Power BI data modeling—exactly what the PL-300 exam tests under the "Design a Data Model" objective. A common trap is merging all data into a single flat table, which bloats the model and degrades performance; instead, remember that dimensions describe "who" and "what," while the fact table records "how many" and "when." For a quick memory tip: think of the star schema as a pizza—the fact table is the center slice (measures and keys), and the dimension tables are the toppings (descriptive attributes) radiating outward.
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 analysis. The source data has a table 'Orders' with columns: OrderID, CustomerID, ProductID, OrderDate, Quantity, UnitPrice. You also have a table 'Customers' with CustomerID, CustomerName, and 'Products' with ProductID, ProductName. You need to create a star schema. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Keep Customers and Products as separate dimension tables, and Orders as the fact table
Option C is correct because in a star schema, a single fact table (Orders) stores quantitative measures (Quantity, UnitPrice) and foreign keys (CustomerID, ProductID) that link to dimension tables (Customers, Products) containing descriptive attributes. This design optimizes query performance by reducing joins and enabling efficient aggregation, which is a best practice for Power BI data modeling.
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.
✗
Split Orders into multiple fact tables by year
Why it's wrong here
This partitions fact table unnecessarily, breaking star schema.
✗
Create a snowflake schema by normalizing Customers and Products further
Why it's wrong here
Snowflake is more normalized than star, not required.
✓
Keep Customers and Products as separate dimension tables, and Orders as the fact table
Why this is correct
Star schema has dimensions and one fact table connected by keys.
Related concept
Read the scenario before looking for a memorised answer.
✗
Merge Customers and Products into a single dimension table
Why it's wrong here
This would create a denormalized dimension, not star schema.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that splitting fact tables by time (e.g., year) is beneficial, but the correct approach is to keep a single fact table with a date dimension for time-based analysis.
Detailed technical explanation
How to think about this question
In Power BI, star schemas leverage in-memory columnar storage (VertiPaq) to compress dimension tables efficiently, while fact tables store foreign keys as integers for optimal join performance. A real-world scenario where this matters is when analyzing sales by customer region and product category simultaneously—keeping dimensions separate allows slicers and filters to work independently without cross-filtering conflicts. The trap here is that candidates may think splitting fact tables by year improves performance, but it actually breaks the star schema and complicates time intelligence calculations.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Keep Customers and Products as separate dimension tables, and Orders as the fact table — Option C is correct because in a star schema, a single fact table (Orders) stores quantitative measures (Quantity, UnitPrice) and foreign keys (CustomerID, ProductID) that link to dimension tables (Customers, Products) containing descriptive attributes. This design optimizes query performance by reducing joins and enabling efficient aggregation, which is a best practice for Power BI data modeling.
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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