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

The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users .

Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.

The other options map to different responsible AI principles:

protect sensitive user data = Privacy and security

reduce bias in decisions = Fairness

require human oversight = Accountability

The correct answer is endpoint of the model . After a vision-enabled GPT model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint that applications can call programmatically. The application sends requests containing text and, when required, image content to the deployed model through the appropriate chat-completions API endpoint. Microsoft’s documentation explicitly states that to use vision-enabled models, you call the Chat Completion API against a supported model that has been deployed.

The Foundry playground is primarily an interactive environment for testing prompts, model behavior, and multimodal inputs during development. It is not the production interface that an external application uses to submit inference requests. An evaluation pipeline is used to assess model or application quality against defined metrics and datasets, while a training dataset is associated with model training or fine-tuning rather than serving inference traffic.

Microsoft Foundry provides the endpoint and required authentication information after deployment. Applications then invoke that endpoint using an SDK or REST API, passing text and image inputs as part of the request payload.

Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.

Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.

The other options are incorrect:

diarization identifies or separates speakers, such as “Speaker 1” and “Speaker 2.”

key phrase extraction identifies important phrases from text after text exists.

synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.

Creating new illustrations based on the text of an article is a content generation task. Because the AI solution must create new visual content from a text prompt or written input, the correct workload is generative AI .

A schema defines which fields to extract when analyzing content.

In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.

The other options are incorrect:

A keyword list does not define the complete structured output fields.

OCR-only processing extracts text, but it does not define structured fields.

A synchronous API call describes a request pattern, not the extraction schema.

The requirement is to produce new product images based on written descriptions . This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.

Why the other options are incorrect:

B. image analysis is used to examine and interpret existing images.

C. object detection is used to identify and locate objects within an existing image.

D. optical character recognition (OCR) is used to extract text from images or scanned documents.

Since the solution must generate new visual content from user-provided descriptions, the correct answer is:

A. image generation

GPT and DALL-E are examples of generative AI model types/families.

GPT models generate natural language text, such as answers, summaries, emails, and conversations.

DALL-E models generate images from text descriptions.

Why the other options are incorrect:

A. transformer is a model architecture, not the best answer here as a generative model type/family.

B. computer vision is an AI workload area.

E. natural language processing is an AI workload area, not a specific generative AI model type.