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NEW QUESTION # 32
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 33
You have a Microsoft Foundry project that contains a vision-enabled GPT model deployment You need to develop an application that sends an image and a text question to the model in a single request.
What should you use?
Answer: A
Explanation:
For a vision-enabled GPT model deployment, use the Azure OpenAI Responses API to send text and image inputs in a single request. Microsoft's Foundry SDK documentation states that the endpoint exposes the Responses API and provides access to Azure OpenAI models, including chat completions and multimodal- capable model interactions.
NEW QUESTION # 34
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Defining an AI system for a typical user profile is sufficient to support inclusiveness.
No
A high number of active users means an AI system provides an inclusive experience.
No
Providing accessible interfaces and language options helps support inclusiveness in an AI system.
Yes
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is No, No, Yes.
Microsoft defines inclusiveness as designing AI systems so they work effectively for the full range of people they are intended to serve. The current AI-901 study guide explicitly includes the ability to describe considerations for inclusiveness in an AI solution as part of the Responsible AI objective domain.
The first statement is No because designing only for a hypothetical or "typical" user can exclude people with different abilities, languages, interaction styles, cultural backgrounds, or accessibility requirements. Inclusive design deliberately considers diverse and potentially underserved users.
The second statement is No because the number of active users is an adoption or usage metric. A system may have millions of users while still creating accessibility barriers or excluding particular groups. Usage volume therefore does not demonstrate inclusiveness.
The third statement is Yes. Accessible interfaces, assistive-technology compatibility, and suitable language options reduce barriers and broaden the range of people who can effectively interact with an AI system.
Microsoft specifically emphasizes accessibility and supporting varied user needs as core elements of inclusive AI design.
NEW QUESTION # 35
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as a base64-encoded image data.
Azure OpenAI / Microsoft Foundry vision requests support image input as an input_image item. The image can be provided by URL or as base64-encoded image data in the request. Microsoft documentation for Azure OpenAI batch examples also lists Base64 encoded image and Image URL as supported input patterns for Responses API requests.
The other options are incorrect:
a CSV file attachment is not an image input format for vision analysis.
an MP4 video stream is video content, not an image format.
a shared access signature (SAS) token is only an access token for a protected resource; it is not the image data itself.
NEW QUESTION # 36
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
No, a summarization model is not used to transcribe spoken audio into written text; instead, an automatic speech recognition (ASR) or speech-to-text model performs the transcription, while a summarization model is used afterward to shorten the resulting text.
Box 2: No
No, a standard chat completion model cannot directly generate vector embeddings for semantic tasks. While both types of models process natural language, they are distinct architectures optimized for entirely different outputs:
Chat Completion Models (e.g., gpt-4o, Claude 3.5 Sonnet) are generative. They take a text prompt and predict the next sequence of words to produce a conversational text response.
Embedding Models (e.g., text-embedding-3-small) are extractive. They compress text into a fixed- length numerical array (a vector) that mathematically represents the underlying meaning, or semantic context, of that text.
Box 3: Yes
Yes, an embedding model can generate vector representations of the provided input. It turns text, words, or images into a list of numbers. These numbers capture the meaning of the data.
How Embeddings Work
An embedding model maps complex data into a simple numeric space.
Inputs: Words, sentences, whole documents, or images.
Outputs: A dense vector of floating-point numbers.
Meaning: Similar inputs get vectors that sit close to each other in that space.
Reference:
https://aimultiple.com/speech-recognition
https://www.debutinfotech.com/blog/understanding-the-role-of-embedding-in-models-like-chat-gpt
NEW QUESTION # 37
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