Which components should be included in a data dictionary for a Clarity model?

Which components should be included in a data dictionary for a Clarity model?

• A. Only table names.
• B. Field definitions, data types, allowed values, and relationships.
• C. Hardware requirements.
• D. User permissions.

Answer: (B)

A data dictionary for a Clarity model focuses on metadata that defines what the data elements mean and how they relate to each other. The key parts are field definitions, data types, allowed values, and the relationships between tables. Field definitions describe what a column represents, its units or format, any applicable constraints, and signposts that help users interpret the data correctly. Data types specify whether a value is a number, text, date, boolean, etc., which guides storage, comparisons, and validation rules. Allowed values enumerate the legitimate options a field can take, including enumerations or reference domains, to prevent invalid data and to support consistent data entry and query behavior. Relationships outline how tables are connected through keys—indicating which fields link to other tables and how those connections enforce data integrity and enable navigation across the model. This combination—definitions, types, allowed values, and relationships—provides a complete, usable map of what the data means, how it should be stored, and how different pieces of data interact. Information like hardware requirements or user permissions sits outside the data’s metadata and belongs to environment or security planning, not the data dictionary itself. A minimal glossary of merely table names would fail to convey the structure and constraints necessary to use the data accurately and safely.

A data dictionary for a Clarity model focuses on metadata that defines what the data elements mean and how they relate to each other. The key parts are field definitions, data types, allowed values, and the relationships between tables. Field definitions describe what a column represents, its units or format, any applicable constraints, and signposts that help users interpret the data correctly. Data types specify whether a value is a number, text, date, boolean, etc., which guides storage, comparisons, and validation rules. Allowed values enumerate the legitimate options a field can take, including enumerations or reference domains, to prevent invalid data and to support consistent data entry and query behavior. Relationships outline how tables are connected through keys—indicating which fields link to other tables and how those connections enforce data integrity and enable navigation across the model.

This combination—definitions, types, allowed values, and relationships—provides a complete, usable map of what the data means, how it should be stored, and how different pieces of data interact. Information like hardware requirements or user permissions sits outside the data’s metadata and belongs to environment or security planning, not the data dictionary itself. A minimal glossary of merely table names would fail to convey the structure and constraints necessary to use the data accurately and safely.