PL-300 Visualize and analyze the data Practice Question
You have a Power BI dataset with a fact table and multiple dimension tables. You need to ensure that when a user filters by a dimension, the filter propagates correctly to the fact table. What type of relationship should you use?
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 from dimension to fact.
The correct option is B: One-to-many from dimension to fact. In a star schema, the dimension table holds unique key values (the "one" side) and the fact table has many rows per key (the "many" side), so a one-to-many relationship from dimension to fact lets filter context propagate from the dimension down to the fact table correctly. Option A describes the same relationship from the opposite direction, which is not how Power BI models it, and it would also be a many-to-one from fact to dimension rather than the required propagation direction. Option C (one-to-one) is wrong because a fact table typically has multiple rows per dimension key, and option D (many-to-many) is unnecessary and would introduce ambiguous filter propagation unless a bridge table is involved.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Many-to-one from fact to dimension.
Why it's wrong here
In a star schema, the dimension table is on the one side, and the fact table is on the many side. The relationship is defined from dimension (one) to fact (many) so that filters from dimension propagate down to facts. If you invert it to many-to-one from fact to dimension, you are essentially making the fact table the lookup table, which is semantically incorrect. This would cause filter direction issues and potentially ambiguous behavior, because the 'one' side should be the dimension containing unique values, not the fact table with duplicate keys.
- ✓
One-to-many from dimension to fact.
Why this is correct
In a well-designed star schema, each dimension table contains unique key values (one row per member) and each fact table contains many transactional rows referencing those members. By setting the relationship as one-to-many from dimension to fact, you ensure that filters applied to dimension attributes propagate naturally down to the fact rows. This is the correct cardinality for a classic star schema, enabling fast, intuitive filtering and aggregations.
- ✗
One-to-one between dimension and fact.
Why it's wrong here
A one-to-one relationship would imply that each dimension row has at most one corresponding fact row and vice versa. This contradicts the fundamental nature of fact tables, which typically store many measurements per dimension member over time or at multiple granularities. In practice, one-to-one would force you to merge the tables or suggest that the dimension is not a genuinely separate lookup table. It also limits analytical flexibility such as time series or multiple fact entries per product, making it unsuitable for standard fact/dimension modeling.
- ✗
Many-to-many between dimension and fact.
Why it's wrong here
A many-to-many relationship indicates that a single dimension member can relate to many fact rows and a single fact row can relate to many dimension members, often requiring a bridge table. In a simple star schema, this ambiguity makes filter propagation unclear because a single value in the dimension could match multiple fact rows in ways that are not deterministic. It can also cause double-counting or unexpected aggregation results unless you carefully manage relationship directions and use DAX patterns like CROSSFILTER with a bridge. Therefore, it's wrong because it violates the intended one-to-many star schema design.
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