A travel company has trained hundreds of geographic data models to answer customer questions by...

AWS provides Amazon SageMaker Multi-Model Endpoints to host multiple trained models behind a single inference endpoint. This feature dynamically loads models into memory based on request traffic, significantly reducing endpoint sprawl and operational overhead.

Multi-model endpoints are ideal when an organization has hundreds or thousands of related models, such as geographic or regional models, that are not all invoked simultaneously. AWS documentation highlights this approach as a best practice for cost optimization and simplified deployment management.

Option B (multi-container endpoints) is intended for chaining a small number of models, not hundreds. Option C does not address endpoint consolidation. Option D changes the modeling strategy and is not required.

Therefore, using a single SageMaker multi-model endpoint is the correct solution.