Quick Answer
The answer is a one-to-many relationship from Products to Sales. This is correct because in a star schema, the dimension table (Products) contains unique values on the "one" side, while the fact table (Sales) holds multiple matching rows on the "many" side, enabling users to filter sales data by any dimension attribute like Category or Subcategory. On the PL-300 exam, this tests your understanding of dimensional modeling fundamentals—a common trap is reversing the direction or choosing a many-to-many relationship, which would cause ambiguous filtering. Remember that the dimension table always sits on the "one" side, and the fact table on the "many" side. A useful memory tip: think of a single product appearing on many sales receipts—that’s your one-to-many.
This PL-300 practice question tests your understanding of model 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.
A data modeler is creating a Power BI semantic model for a retail company. The model includes a 'Products' dimension table with columns ProductID, ProductName, Category, and Subcategory. The 'Sales' fact table has columns ProductID, Date, Quantity, and Revenue. The modeler wants to ensure that users can filter by Category and Subcategory. Which relationship type should be created between Products and Sales?
One-to-many (Products to Sales)
Each product can have many sales, so Products is the 'one' side.
Many-to-one (Sales to Products)
Why wrong: This is the reverse direction; Products should filter Sales.
Many-to-many
Why wrong: Many-to-many would require a bridging table.
One-to-one
Why wrong: One-to-one would imply each product has only one sale, which is unrealistic.
Answer choices
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 (Products to Sales)
In a Power BI semantic model, a one-to-many relationship from Products (the dimension table) to Sales (the fact table) is the standard star schema design. This allows users to filter Sales data by any attribute in Products, such as Category and Subcategory, because each ProductID in Products is unique (the 'one' side) and can be associated with many rows in Sales (the 'many' side). This relationship type ensures proper cross-filtering and aggregation behavior.
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.
One-to-many (Products to Sales)
Why this is correct
Each product can have many sales, so Products is the 'one' side.
Related concept
Read the scenario before looking for a memorised answer.
Many-to-one (Sales to Products)
Why it's wrong here
This is the reverse direction; Products should filter Sales.
Many-to-many
Why it's wrong here
Many-to-many would require a bridging table.
One-to-one
Why it's wrong here
One-to-one would imply each product has only one sale, which is unrealistic.
Common exam traps
The trap here is that candidates might confuse the cardinality direction (one-to-many vs. many-to-one) as being technically different, but Power BI treats them identically; the key is recognizing that the dimension table (Products) should be on the 'one' side to maintain a proper star schema and avoid unnecessary complexity.
Detailed technical explanation
Under the hood, Power BI uses a storage engine (VertiPaq) that optimizes one-to-many relationships by creating a hash index on the 'one' side column, enabling fast filtering and aggregation. In a real-world scenario, if the modeler mistakenly used a many-to-many relationship, Power BI would require a bridge table or a more complex configuration, leading to ambiguous filter propagation and potential performance degradation. The one-to-many relationship also supports bidirectional cross-filtering when needed, but the default single direction is sufficient for this use case.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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FAQ
Model the data — This question tests Model the data — Read the scenario before looking for a memorised answer..
The correct answer is: One-to-many (Products to Sales) — In a Power BI semantic model, a one-to-many relationship from Products (the dimension table) to Sales (the fact table) is the standard star schema design. This allows users to filter Sales data by any attribute in Products, such as Category and Subcategory, because each ProductID in Products is unique (the 'one' side) and can be associated with many rows in Sales (the 'many' side). This relationship type ensures proper cross-filtering and aggregation behavior.
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
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