Which schema is typical of a Denormalized Model?

A star schema is a design typically associated with denormalized data models. It is characterized by a central fact table that stores quantitative data for analysis, surrounded by dimension tables that contain descriptive attributes related to the facts. This approach reduces the complexity of joins that would be necessary in a fully normalized model, making it easier and faster for querying and reporting.

Denormalizing data within a star schema minimizes the number of tables, which allows for improved performance in read-heavy operations often found in data warehousing scenarios. The consolidation of tables helps streamline data access, enhancing retrieval speeds, which is particularly valuable in analytical applications.

In contrast, hierarchical schemas involve multiple nested levels and are typically used in applications that require a strict parent-child relationship, leading to heightened complexity in data retrieval. Network schemas also exhibit complex relationships which can result in convoluted data structures. Relational schemas are generally normalized into several interrelated tables, which can lead to inefficiencies in certain analytic scenarios.

Thus, the star schema stands out as the correct choice due to its effectiveness in denormalization, promoting simplicity and optimized performance in data processing and analysis.