Microsoft Certified: Fabric Analytics Engineer AssociateImplement and manage semantic models (30-35%)Easy
A data engineer is designing a new semantic model in Microsoft Fabric. The model will be used by various departments, each requiring access to different subsets of the data based on their roles. The current plan involves creating multiple versions of the semantic model, each pre-filtered for a specific department. You need to recommend a more efficient and scalable solution to ensure data security and reduce maintenance overhead. Which feature should you implement?
- AObject-Level Security (OLS)
- BDimension-Level Security (DLS)
- CColumn-Level Security (CLS)
- DRow-Level Security (RLS)
Show answer & explanationAnswer & explanation
Correct answer: D. Row-Level Security (RLS)
Row-Level Security (RLS) is the most appropriate feature for this scenario. RLS allows you to filter rows in a table based on user roles and expressions, ensuring that each department only sees the data relevant to them within a single semantic model, rather than maintaining multiple models.
Why the other options are wrong
- A. OLS restricts access to entire tables or columns, not specific rows within tables.
- B. DLS is not a standard security feature in Fabric semantic models; RLS handles filtering based on dimension attributes.
- C. CLS is not a standard security feature in Fabric semantic models; OLS covers column-level restrictions.
Row-Level Security (RLS)
RLS in Microsoft Fabric semantic models allows you to restrict data access at the row level based on user roles and filter expressions, ensuring users only see data relevant to them.
- Filters rows based on user identity or role.
- Implemented using DAX expressions in roles.
- Simplifies data access management for different user groups.
Memory trick: Roles Limit Rows to See What's Right.