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Answer: Create separate dimension tables for each attribute (age, gender, treatment type) and establish many-to-one relationships with the fact table, ensuring each dimension can be independently related to the fact table.
Creating separate dimension tables for each attribute and establishing many-to-one relationships with the fact table is the best approach. This method provides flexibility and accuracy in data analysis, allowing each dimension to be independently related to the fact table. It also supports scalability and compliance with data privacy regulations by maintaining clear and manageable relationships. Option A may oversimplify the model, Option C could unnecessarily complicate it for the given requirements, and Option D would fail to leverage the relational capabilities of the semantic model.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with designing a semantic model for a healthcare organization. The organization requires a solution to analyze patient data across multiple dimensions, including age, gender, and treatment type, with the ability to scale as data grows. The solution must also comply with healthcare data privacy regulations. Considering these requirements, which of the following approaches should you take to implement relationships between these dimensions? (Choose one correct option)
A
Create a single dimension table combining all attributes (age, gender, treatment type) and establish a many-to-one relationship with the fact table, to simplify the model.
B
Create separate dimension tables for each attribute (age, gender, treatment type) and establish many-to-one relationships with the fact table, ensuring each dimension can be independently related to the fact table.
C
Implement a bridge table to establish a many-to-many relationship between the fact table and each dimension table, to accommodate complex queries.
D
Ignore the relationships and treat each dimension as a separate entity, to reduce complexity in the data model.