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A dimension table is specifically designed to store attributes related to the business context, which can be used for filtering, grouping, and labeling data in analytical processes. In the case of an insurance company aiming to store details about customer attributes, a dimension table is the most suitable choice.

Customer attributes include characteristics such as customer ID, name, address, policy details, and other demographic data that provide context to claims data. By storing these attributes in a dimension table, the company can easily aggregate and analyze claims data against various customer segments, making it possible to generate insights regarding customer behavior, claim patterns, and policy usage.

Staging tables are typically used for temporary data storage during the ETL (Extract, Transform, Load) process and are not intended for long-term storage of business-related information. Fact tables, on the other hand, usually store quantitative data for analysis (e.g., the amount of claims paid, count of claims, etc.) and would reference dimension tables, but they are not the appropriate structure for storing detailed customer attributes that are used for aggregation purposes.