Which two elements make up a well modeled Fact Table?

A well-modeled Fact Table is essential in a data warehouse as it acts as the central repository for quantitative data. The primary elements that constitute a well-modeled Fact Table are Dimension Keys and Measures.

Dimension Keys are crucial because they enable the Fact Table to link to various dimension tables. These keys provide context for the measurements contained in the Fact Table, such as date, product, or customer. Each Dimension Key corresponds to a specific attribute in a dimension table, thus allowing data analysts to drill down into the facts through multiple dimensions.

Measures, on the other hand, represent the quantitative data that one wishes to analyze. This could include values such as sales revenue, quantity sold, or profit margin. Measures are the actual numbers that users will query, aggregate, and analyze, providing insight into business performance.

While using REST APIs and allowing nulls might be aspects relevant to data connectivity and data quality, respectively, they are not fundamental components of a well-structured Fact Table. Therefore, the inclusion of Dimension Keys and Measures is what fundamentally constitutes a well-modeled Fact Table.