PL-300 Model the data Practice Question
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?
⚠ Common exam trap
PL-300 often tests the direction confusion between 'one-to-many from Customers to Orders' and 'many-to-one from Orders to Customers' — candidates pick the many-to-one wording even though it describes the same relationship from the wrong side.
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
✓
One-to-many relationship from Customers to Orders
In a star schema, the dimension table (Customers) has a unique CustomerID and the fact table (Orders) has many rows per customer, so the relationship is one-to-many from Customers to Orders. Power BI's default filter direction is single, meaning Customers filters Orders, which is exactly what is needed for filtering Orders by Segment. This is the standard dimensional modeling pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
One-to-many relationship from Customers to Orders
Why this is correct
This is the canonical star-schema relationship. A single customer can appear in many orders, so Customers is the 'one' side and Orders is the 'many' side. By placing the relationship from Customers to Orders, customer attributes (region, segment, etc.) automatically filter and slice all related order rows, enabling correct aggregations in measures. This relationship uses the dimension table as the lookup table and the fact table as the data table, which is the preferred pattern for performance and intuitive filtering.
- ✗
Many-to-one relationship from Orders to Customers
Why it's wrong here
This relationship declaration is wrong because it describes the source as the Orders table, implying filters would flow from the many-side fact table to the one-side dimension table. While a many-to-one relationship in Power BI has the same cardinality as a one-to-many, the requested star-schema design must start at Customers (the dimension) and end at Orders (the fact). Starting at the fact side obscures filter direction and breaks the convention of having the lookup table on the one side drive all downstream measures.
- ✗
Many-to-many relationship with a bridge table
Why it's wrong here
A many-to-many relationship requires a bridge table to resolve the cardinality when both tables lack unique keys. In this scenario, each order has exactly one customer and each customer can have many orders, so there is no many-to-many association between Customers and Orders. Introducing a bridge table would needlessly fragment the model, add extra joins, and risk double-counting customer metrics unless special handling such as CALCULATE with USERELATIONSHIP is added.
- ✗
One-to-one relationship
Why it's wrong here
A one-to-one relationship would force every customer to have at most one order and every order to belong to a distinct customer. This is impossible in an order management system because a customer can place multiple orders over time, and multiple order rows share the same customer key. Enforcing one-to-one cardinality would require denormalizing order details into the customer row, which violates the normalized design of a fact table containing multiple transactions per dimension member.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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