AI-901 Exam Dumps : Microsoft Azure AI Fundamentals (Updated Version)

The correct answer is API endpoint. After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.

An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference. A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.

This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.

Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.

Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.

The other options are incorrect:

Key phrase extraction identifies important phrases or main topics.

Language detection identifies the language of the text.

Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.