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As a Microsoft Fabric Analytics Engineer Associate, you are designing a data warehouse schema for a multinational retail company. The company operates in multiple regions, has a vast inventory of products, and serves millions of customers. The schema must support complex analytical queries across these dimensions efficiently, while also considering scalability and performance for future growth. Additionally, the solution must minimize data redundancy without compromising query performance. Given these requirements, which of the following schema designs would you recommend? (Choose one)
Explanation:
The star schema (Option A) is the most appropriate choice for this scenario. It balances the need for efficient query performance with the simplicity of the data model, making it ideal for read-heavy analytical workloads typical in retail analytics. The central fact table allows for quick aggregation of sales data, while the denormalized dimension tables provide easy access to related information without complex joins. This design supports scalability and performance, meeting the company's current and future needs.