In a data warehouse, what is the primary purpose of a dimension table?

• A. To store descriptive attributes used to constrain and group data
• B. To store measurements and foreign keys for facts
• C. To store raw staging data
• D. To cache aggregated results

Answer: (A)

Dimension tables hold descriptive attributes that describe the business entities analyzed in the warehouse. These attributes provide the context you need to constrain, filter, and group data when querying facts. In a typical star schema, the fact table stores measurements (like sales amount) and keys to the dimensions, while the dimension tables supply the labels, hierarchies, and descriptors—such as product name, category, date, customer region, and so on. This setup lets you slice and dice data by those attributes and drill down into finer levels of detail.

Other options miss this contextual role: storing measurements and foreign keys for facts belongs to the fact table, not the dimension table; raw staging data goes in the staging area, not the dimension; caching aggregated results is a performance optimization separate from the structural purpose of a dimension.