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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-901 |
| Real Exam Qty: | 30-40 |
| Exam Format: | Case studies, Multiple choice, Multiple select |
| Available Languages: | Chinese (Simplified), Chinese (Traditional), German, Spanish, Italian, Indonesian (Indonesia), Korean, English, French, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Japanese |
| Exam Price: | $99 USD |
| Certificate Validity Period: | Lifetime |
| Passing Score: | 700 |
| Exam Duration: | 60 minutes |
| Recommended Training: | Microsoft Learn: Get started with Microsoft Foundry Microsoft Learn: AI concepts for developers and technology professionals |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/ |
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NEW QUESTION # 92
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?
Answer: A
Explanation:
When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item. The correct content item type is:
{"type": "input_image", "image_url": image_url}
Microsoft's Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.
Why the other options are incorrect:
A . image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image.
B . image_generation = Incorrect. This is related to generating images, not providing an image for analysis.
C . output_image = Incorrect. This would refer to generated output, not image input.
D . input_image = Correct.
NEW QUESTION # 93
Your company wants to build a recycling machine for bottles. The recycling machine must use cameras to automatically identify bottles of the correct shape and reject all other items.
Which type of Al workload should the company use?
Answer: B
Explanation:
A recycling machine that uses cameras to identify bottles of the correct shape and reject other items requires analysis of visual input. This is a computer vision workload.
Computer vision is used for image and video analysis tasks such as object detection, classification, shape recognition, and visual inspection.
The other options are incorrect:
A). knowledge mining extracts insights from large collections of documents or data.
B). natural language processing analyzes or generates language.
C). generative AI creates new content, but the requirement is to inspect camera images and identify objects.
NEW QUESTION # 94
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 95
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model.
To call a deployed model from an application, the application must send requests to the deployed model's endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.
The other options are incorrect:
evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.
Foundry playground is an interactive testing environment, not the production target for application requests.
training dataset of the model is used for training or fine-tuning, not for sending inference requests.
Therefore, the correct answer is endpoint of the model.
NEW QUESTION # 96
You plan to create an AI application by using Azure AI Foundry. The solution will be deployed to dedicated virtual machines. Which deployment option should you use?
Answer: A
Explanation:
Deployment options
Azure AI Foundry provides several deployment options depending on the type of models and resources you need to provision. The following deployment options are available:
* Standard deployment in Azure AI Foundry resources
* Deployment to serverless API endpoints
* Deployment to managed computes
Serverless API endpoint
This deployment option is available only in Azure AI hub resources. It allows you to create dedicated endpoints to host the model, accessible through an API. Azure AI Foundry Models support serverless API endpoints with pay-as-you-go billing, and you can create only regional deployments for serverless API endpoints.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/deployments-overview
NEW QUESTION # 97
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