What is a primary benefit of denormalizing dimension tables in a semantic model?

• A. Reduces complexity and number of joins, may increase data redundancy
• B. Increases complexity and reduces performance
• C. Has no effect
• D. Causes data to be inconsistent

Answer: (A)

Denormalizing dimension tables in a semantic model mainly makes queries easier and faster by reducing the number of joins analysts have to write and execute. When you bring related attributes into a single dimension or remove the need to look up many small lookup tables, a query against the fact table can be fulfilled with fewer table traversals. That simplification translates into simpler SQL and, in practice, improved performance on large datasets because join operations are often the bottleneck.

The trade-off is data redundancy: storing the same information in more places can lead to more storage use and the need for careful ETL processes to keep duplicates consistent. The other options describe outcomes that aren’t the typical benefit—more complexity, no effect, or data inconsistency as the main advantage.