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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Implement AI solutions by using Microsoft Foundry55-60%- Get started with Microsoft Foundry
  • 1. Create and configure a Foundry project
  • 2. Describe core features and use cases of Microsoft Foundry
  • 3. Navigate the Microsoft Foundry portal
- Work with models in Microsoft Foundry
  • 1. Customize models with prompt engineering
  • 2. Fine-tune models
  • 3. Evaluate model performance and safety
  • 4. Discover and deploy foundation models
- Implement AI solutions
  • 1. Implement single-agent solutions
  • 2. Integrate AI services into applications
  • 3. Use code examples to call AI services
  • 4. Monitor and manage AI solutions
Identify AI concepts and responsibilities40-45%- Identify AI concepts and features
  • 1. Describe types of AI workloads
  • 2. Describe capabilities of computer vision
  • 3. Describe capabilities of natural language processing
  • 4. Describe characteristics of generative AI
  • 5. Describe capabilities of machine learning
- Identify AI model components and configurations
  • 1. Describe data preparation for AI models
  • 2. Describe model training and evaluation concepts
  • 3. Describe model deployment and consumption concepts
- Describe principles of responsible AI
  • 1. Describe privacy and security considerations
  • 2. Describe transparency considerations
  • 3. Describe inclusiveness considerations
  • 4. Describe reliability and safety considerations
  • 5. Describe fairness considerations
  • 6. Describe accountability considerations

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Microsoft Azure AI Fundamentals Sample Questions (Q19-Q24):

NEW QUESTION # 19
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: C

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 # 20
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right. Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Speech recognition is used to recognize spoken language and convert it into written text. Therefore, the correct match is:
Speech recognition = Transcribes spoken input
Speech synthesis, also known as text to speech, is used to convert text into spoken audio. Therefore, the correct match is:
Speech synthesis = Generates spoken output
The other options are incorrect for these two tools:
Extracts entities from the output is a text analytics / language processing task.
Translates spoken input is speech translation.
Translates written output is text translation.


NEW QUESTION # 21
You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?

Answer: B

Explanation:
To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.
The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.


NEW QUESTION # 22
You have an Azure subscription that uses Azure OpenAI.
You need to create an original image of a rural scene to use on a website.
What should you do?

Answer: B

Explanation:
Azure OpenAI in Azure AI Foundry Models
Image generation models
The image generation models generate images from text prompts that the user provides. GPT- image-1 is in limited access public preview. DALL-E 3 is generally available for use with the REST APIs. DALL-E 2 and DALL-E 3 with client SDKs are in preview.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models


NEW QUESTION # 23
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 # 24
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