Non-text-generation Large Language Models (LLMs), such as embedding or classification models, require structured input, typically in a JSON format, rather than a simple text prompt. The Nutanix AI platform's endpoint testing interface provides a "Custom Request" option specifically for this purpose. This feature allows the user to either paste a custom JSON payload or upload a file containing the test data in the required format, enabling a valid inference test against the deployed model.

A. Copying the sample API code is for integrating the endpoint into an external application, not for direct testing within the Nutanix AI user interface.

C. Creating a Sample Request with a default prompt is the standard method for testing text-generation models, which is not the type specified in the question.

D. Opening an API endpoint URL in a web browser is not a valid method for testing, as it cannot properly formulate the required POST request with a data payload.

1. Nutanix Cloud Platform AI Administration and User Guide v1.0:

Section: Testing an Endpoint

Content: The guide details the process of testing a deployed model's endpoint. It explicitly describes two tabs: "Sample Request" and "Custom Request". The "Custom Request" tab is specified for use cases where a custom JSON request body is needed, which is characteristic of non-text-generation models. The documentation states, "In the Custom Request tab, you can either paste a custom request or upload a file to test the endpoint." This directly supports the procedure described in the correct answer.