Does Removing the Salesperson Table Meet Model1's Size Goal?
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You are creating a Power BI semantic model named Model1 that contains the following tables.
The tables have the following relationships.
You have 100 salespeople in 10,000 cities. Each salesperson sells in multiple cities.
You import one million rows into the Sales table.
You need to reduce the size of Model1. The solution must ensure that you can analyze the total number of sales by salesperson per city.
Solution: You remove the Salesperson table.
Does this meet the goal?
Community Insight
The question tests whether a dimension table is required when its key already exists in the fact table; the trap is assuming every SalespersonID needs a separate table.
In this PL-300 scenario, Model1's Sales fact table already stores both SalespersonID and CityID, so removing the redundant one-column Salesperson dimension still allows total sales to be analyzed by salesperson per city while reducing model size.
The most common wrong answer is No, because candidates assume removing the Salesperson table loses the ability to analyze salesperson performance, even though Sales[SalespersonID] remains available for grouping and filtering.
Community Discussion (4 comments)
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Expert Analysis
Why the Answer Is Correct
Removing the Salesperson table meets the goal because the Sales table already contains SalespersonID and CityID, which are the keys needed to analyze total sales by salesperson per city. The Salesperson table, as described in the scenario, adds only SalespersonID and no descriptive salesperson attributes. A matrix visual can group Sales[SalespersonID] against City[CityID] or City[City] without the redundant dimension. Therefore, the solution reduces Model1 by eliminating an unnecessary table and relationship while preserving the required analysis.
Why the Other Options Are Wrong
Choosing No incorrectly assumes that the Salesperson dimension supplies data needed for the required analysis. Because Sales still has SalespersonID, users can still filter and group fact rows by salesperson. Losing a textbook star-schema dimension is not fatal for this specific requirement, especially when the dimension is a single key column. The only case where No would be right is if the Salesperson table contained additional attributes such as salesperson name or region that the report required, but those attributes are not part of this scenario.
Community Comment Notes
As nikisychan noted, the Salesperson table "only contains a single column: SalespersonID" and is redundant because that key already exists in Sales. e68b25f made the same key point: the Sales table "has both column SalespersonID and CityID", so the required grouping is possible from the fact table. Idaresit worried about losing salesperson names and star-schema structure, but names are not part of the described Salesperson table, and the exam question asks whether the solution meets the goal rather than whether it is the most elegant model design.
Official Reference
Exam Strategy
Separate the need to reduce model size from the need to preserve the exact report fields. If the fact table already contains the keys used for grouping and filtering, removing a one-column dimension is usually valid even if the size saving is small.
Frequently Asked Questions
Why is removing the Salesperson table acceptable when analyzing sales by salesperson?
Because the Sales fact table already contains SalespersonID, a matrix can group total sales by Sales[SalespersonID] and City[CityID] without the one-column dimension.
Does dropping a 100-row dimension really reduce Model1's size?
Only slightly, but it removes a redundant table and relationship; the solution still meets the stated goal because the required salesperson-by-city analysis remains possible.
Related Analysis
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