Quick Answer
The correct answer is the Customer, Date, and Product dimension tables. In a star schema, the central fact table stores quantitative metrics like Quantity and UnitPrice from the Orders table, while dimension tables hold descriptive attributes that provide context for those metrics. The Customer dimension stores customer attributes, the Date dimension enables time-based analysis on OrderDate, and the Product dimension provides product details—together they normalize the data and allow efficient slicing and dicing in Power BI. On the PL-300 exam, this tests your understanding of dimensional modeling fundamentals, often appearing as a scenario where you must identify which tables become dimensions versus facts. A common trap is mistaking the Orders table itself as a dimension or including calculated columns like TotalPrice as a dimension table. Remember the memory tip: “Facts are numbers, dimensions are nouns”—if a column describes who, what, or when, it belongs in a dimension.
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 creating a Power BI report from a SQL Server database that contains a table Orders with columns: OrderDate, CustomerID, ProductID, Quantity, UnitPrice. You need to build a star schema. Which THREE tables should you create? (Choose three.)
OrderDetails table with line items.
Why wrong: This is already part of the fact table.
Date dimension table with date attributes.
Essential for time intelligence.
Product dimension table with product attributes.
For product analysis.
Customer dimension table with customer attributes.
For customer analysis.
Sales fact table with measures.
Why wrong: This is the fact table, not a dimension.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Date dimension table with date attributes.
In a star schema, dimension tables contain descriptive attributes (e.g., dates, products, customers) and are connected to a central fact table. For the Orders table, a Date dimension (B) is essential for time-based analysis, a Product dimension (C) provides product details, and a Customer dimension (D) stores customer attributes. These three dimensions normalize the data and enable efficient slicing and dicing in Power BI.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
OrderDetails table with line items.
Why it's wrong here
This is already part of the fact table.
Date dimension table with date attributes.
Why this is correct
Essential for time intelligence.
Related concept
Read the scenario before looking for a memorised answer.
Product dimension table with product attributes.
Why this is correct
For product analysis.
Related concept
Read the scenario before looking for a memorised answer.
Customer dimension table with customer attributes.
Why this is correct
For customer analysis.
Related concept
Read the scenario before looking for a memorised answer.
Sales fact table with measures.
Why it's wrong here
This is the fact table, not a dimension.
Common exam traps
The trap here is that candidates often confuse dimension tables with fact tables or think that line-item details (Option A) should be a separate dimension, when in fact they belong in the fact table to maintain a star schema's simplicity and performance.
Detailed technical explanation
In Power BI, star schema design optimizes query performance by reducing the number of joins and leveraging in-memory columnar storage (VertiPaq). The Date dimension should include a continuous date range and attributes like Year, Quarter, Month, and Day to support time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR). The Product and Customer dimensions should have surrogate keys (e.g., ProductID, CustomerID) that link to the fact table, ensuring referential integrity and avoiding data duplication.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
The correct answer is: Date dimension table with date attributes. — In a star schema, dimension tables contain descriptive attributes (e.g., dates, products, customers) and are connected to a central fact table. For the Orders table, a Date dimension (B) is essential for time-based analysis, a Product dimension (C) provides product details, and a Customer dimension (D) stores customer attributes. These three dimensions normalize the data and enable efficient slicing and dicing in Power BI.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.
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