What characterizes a Normalized Model in data architecture?

A normalized model in data architecture is characterized by the organization of data into multiple tables that minimize redundancy and ensure data integrity. The approach typically involves having one or more fact tables that hold quantitative data for analysis, along with several dimension tables that provide context to that data, such as attributes related to the facts.

In this model, dimension tables are designed to be connected to the fact tables through keys, allowing for efficient querying and reporting. This structure supports a clear distinction between different types of data, helping users to maintain a clean and manageable database design. It is well-suited for transactional databases where updates, insertions, and deletions occur frequently, as it helps to maintain data accuracy and reduces the risk of anomalies.

The other options describe different types of data models. A single fact table with few dimension tables indicates a denormalized model or a simple structure rather than a comprehensive normalized one. A flat structure with all data combined typically refers to a denormalized approach, where data from multiple tables is merged into one large table, which can make data management less efficient. An unstructured format does not apply to normalized models, as they rely on a structured approach to data organization.