Updated Microsoft - Certification AI-901 Exam Infor

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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
- Implement AI solutions
  • 1. Monitor and manage AI solutions
  • 2. Implement single-agent solutions
  • 3. Integrate AI services into applications
  • 4. Use code examples to call AI services
- Work with models in Microsoft Foundry
  • 1. Evaluate model performance and safety
  • 2. Customize models with prompt engineering
  • 3. Discover and deploy foundation models
  • 4. Fine-tune models
Identify AI concepts and responsibilities40-45%- Describe principles of responsible AI
  • 1. Describe inclusiveness considerations
  • 2. Describe transparency considerations
  • 3. Describe privacy and security considerations
  • 4. Describe fairness considerations
  • 5. Describe accountability considerations
  • 6. Describe reliability and safety considerations
- Identify AI concepts and features
  • 1. Describe capabilities of computer vision
  • 2. Describe types of AI workloads
  • 3. Describe capabilities of machine learning
  • 4. Describe capabilities of natural language processing
  • 5. Describe characteristics of generative AI
- 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

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Microsoft Azure AI Fundamentals Sample Questions (Q118-Q123):

NEW QUESTION # 118
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 # 119
You plan to create an AI application that will use Azure OpenAI in Foundry Models. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?

Answer: A

Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md


NEW QUESTION # 120
Drag and Drop Question
You are reviewing best practices for using AI at your company.
Which Microsoft responsible AI principle is each task an example of? To answer, drag the appropriate principles to the correct tasks. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct match is worth one point.

Answer:

Explanation:


NEW QUESTION # 121
For each of the following statements, select Yes if the statement is true. Otherwise, select NOTE Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format.
= Yes
Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.


NEW QUESTION # 122
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.
Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use , which maps directly to the Microsoft responsible AI principle of reliability and safety .
The other options are incorrect:
Fairness focuses on reducing bias and treating people equitably.
Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.
Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.
Therefore, the correct answer is reliability and safety .


NEW QUESTION # 123
......

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