Which two features are recommended to provide extensibility to the semantic model?

Which two features are recommended to provide extensibility to the semantic model?

• A. Semantic Model Extensions
• B. External Applications
• C. Data Augmentation
• D. Semantic Model Extensions and External Applications

Answer: (D)

Extensibility of a semantic model comes from pairing ways to evolve the model itself with ways to use and deploy it across systems. Semantic Model Extensions let you augment the model's vocabulary and meaning—adding new types, relationships, attributes, and rules tailored to your domain—without touching the core definitions. This keeps the model adaptable as requirements grow or shift. External Applications provide the practical channels to consume, apply, and extend the model in real-world workflows. They can implement domain-specific processes, dashboards, integrations, and logic that rely on the extended semantic definitions, making the model usable across different platforms and teams. Using both together offers true extensibility: you enhance what the model can represent (extensions) and you amplify how it is applied and integrated (external applications). Data augmentation, while useful for improving data quality or training, does not add new semantic structures or deployment paths, so it doesn’t achieve extensibility in the same way.

Extensibility of a semantic model comes from pairing ways to evolve the model itself with ways to use and deploy it across systems. Semantic Model Extensions let you augment the model's vocabulary and meaning—adding new types, relationships, attributes, and rules tailored to your domain—without touching the core definitions. This keeps the model adaptable as requirements grow or shift.

External Applications provide the practical channels to consume, apply, and extend the model in real-world workflows. They can implement domain-specific processes, dashboards, integrations, and logic that rely on the extended semantic definitions, making the model usable across different platforms and teams.

Using both together offers true extensibility: you enhance what the model can represent (extensions) and you amplify how it is applied and integrated (external applications). Data augmentation, while useful for improving data quality or training, does not add new semantic structures or deployment paths, so it doesn’t achieve extensibility in the same way.