You have a Fabric workspace that contains a complex semantic model for a Microsoft Power BI report. You need to optimize the semantic model for analytical queries and use denormalization to reduce…

Denormalizing dimension tables already at the same level of granularity might not significantly reduce model complexity or joins, as they are likely already part of a star schema.

Fact tables are typically already highly denormalized and contain measures; further denormalizing them is generally not the primary strategy for reducing join complexity in a dimensional model.

Role-playing dimension tables are a modeling technique to use the same dimension multiple times with different roles (e.g., Order Date, Ship Date); denormalizing them directly is not the common approach to reduce model complexity from joins.

Snowflaked dimension tables involve multiple linked dimension tables (e.g., product -> product category), which require multiple joins to retrieve full attribute information. Denormalizing these by flattening them into a single, wider dimension table directly reduces the number of joins needed for queries, thereby simplifying the model and improving query performance.