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What Should Be Stored in a Dimension Table in Power BI Star Schema?

You are building a star schema in Power BI. The fact table contains sales transactions. Which of the following should be stored in a dimension table?

Quick Answer

The correct answer is product category, because it is a descriptive attribute that belongs in a dimension table within a star schema. In a properly designed star schema, dimension tables store the “who, what, where, when” context—such as product names, categories, or customer regions—while fact tables store quantitative measures like sales amount. For the Microsoft Power BI Data Analyst PL-300 exam, this question tests your understanding of star schema fundamentals and the separation of descriptive attributes from numeric measures. A common trap is confusing foreign keys, like customer ID, which reside in the fact table as links, with the descriptive attributes that populate dimension tables. Remember the memory tip: “Facts are numbers, dimensions are descriptors”—if it describes a business entity, it belongs in a dimension.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Product category

Product category is a descriptive attribute that belongs in a product dimension table. Option B is wrong because sales amount is a numeric measure that should be stored in the fact table. Option C is wrong because customer ID is a foreign key that links to a customer dimension but is stored in the fact table as a key, not as a descriptive attribute. Option D is wrong because transaction date can be part of a date dimension, but the question asks for what should be stored in a dimension table, and product category is the clearest dimension attribute among the options.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Product category

    Why this is correct

    Product category is a descriptive attribute that should reside in a product dimension table.

  • Sales amount

    Why it's wrong here

    Sales amount is a numeric measure and belongs in the fact table.

  • Customer ID

    Why it's wrong here

    Customer ID is a foreign key in the fact table, not a descriptive attribute stored in a dimension.

  • Transaction date

    Why it's wrong here

    Transaction date can be part of a date dimension, but the question asks for a typical dimension attribute; product category is a better example.

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Same concept, more angles

1 more way this is tested on PL-300

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. You are building a Power BI model that includes a table 'Orders' with columns: OrderID, CustomerID, OrderDate, and TotalAmount. You also have a table 'Customers' with columns: CustomerID, CustomerName, and Segment. You need to create a relationship between Orders and Customers on CustomerID. Which relationship configuration should you choose to ensure that filtering Customers by Segment correctly filters Orders?

hard
  • A.One-to-many relationship from Customers to Orders
  • B.Many-to-one relationship from Orders to Customers
  • C.Many-to-many relationship with a bridge table
  • D.One-to-one relationship

Why A: A one-to-many relationship from Customers (one side) to Orders (many side) with single-direction cross-filtering ensures that filtering Customers by Segment correctly filters Orders. Option B (many-to-one) would reverse the cardinality and, by default, would not allow filtering from Customers to Orders. Option C is unnecessary and can cause ambiguity. Option D is inappropriate as one customer can have many orders.

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.