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

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Microsoft Azure AI Fundamentals Sample Questions (Q88-Q93):

NEW QUESTION # 88
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:


NEW QUESTION # 89
You are developing an application that will coordinate a travel-booking workflow by gathering user preferences, searching for options, requesting approval, and booking services. The solution will maintain context across the steps.
Which type of Al workload is this an example of?

Answer: C

Explanation:
The described solution coordinates a multi-step workflow : gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI
.
Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.
Why the other options are incorrect:
A). information extraction extracts structured data from content.
C). computer vision analyzes images or video.
D). generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI .


NEW QUESTION # 90
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:

Statement 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No Fine-tuning is optional. Microsoft's Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.
Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project , and Foundry playgrounds are used for prototyping and validation before production.
Therefore, the statement using "only after you deploy" is too restrictive.
Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy .


NEW QUESTION # 91
Hotspot Question
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


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: B

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
......

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