The Microsoft Azure AI Fundamentals (AI-901) certification is the way to go in the modern Microsoft era. Success in the AI-901 exam of this certification plays an essential role in an individual's future growth. Nowadays, almost every tech aspirant is taking the test to get Microsoft certification and find well-paying jobs or promotions. But the main issue that most of the candidates face is not finding updated Microsoft AI-901 Practice Questions to prepare successfully for the Microsoft AI-901 certification exam in a short time.
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NEW QUESTION # 66
You have a Microsoft Foundry project that contains a vision-enabled chat model deployment.
You are developing a Python application that uses the responses API. The application sends a request that includes a user prompt and a local JPEG image.
You need to include the local image in the request.
Which value should you use for the image input?
Answer: D
Explanation:
For a local JPEG image in a Python application using the Azure OpenAI Responses API, the image should be read, base64 encoded, and sent as a data URL, such as:
{ " type " : " input_image " , " image_url " : " data:image/jpeg;base64, < base64_image_data > " } Microsoft's Responses API documentation shows vision-enabled requests using input_image with image_url set to a base64 data URL in the format data:image/jpeg;base64,{base64_image}. It also confirms that JPEG images are supported.
A local file path such as file:///C:/images/photo.jpg or C:\images\photo.jpg is not a valid API-accessible image input. A public or accessible HTTPS URL can be used, but the question specifically says the application sends a local JPEG image , so the correct choice is the base64 data URL.
NEW QUESTION # 67
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Creating captions for a video recording . Speech recognition, also called speech-to-text , converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice-activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis .
NEW QUESTION # 68
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?
Answer: A
Explanation:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry
NEW QUESTION # 69
You have the following REST API request.
Which Azure OpenAI model should you use to process the request?
Answer: B
Explanation:
A DALL-E REST API example using parameters like prompt, size, style, and n (number of images) can be used to generate images. Here's how:
Example Request Body (JSON):
{
"prompt": "A cat wearing a hat, standing on a skateboard",
"size": "1024x1024", // Supported values: "1024x1024", "1792x1024", "1024x1792"
"style": "natural", // DALL-E 3 supports "natural" and "vivid" styles
"n": 1 // DALL-E 3 only supports n=1
}
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/dall-e
NEW QUESTION # 70
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft's Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data. It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL.
NEW QUESTION # 71
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