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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q60-Q65):

NEW QUESTION # 60
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: A,C,F

Explanation:
Microsoft's Six Responsible AI Principles
[C] Fairness: AI systems should treat all people equitably and avoid bias or discrimination.
[D] Reliability and Safety: Systems must operate dependably, consistently, and safely without causing harm.
Privacy and Security: AI must protect user data securely and respect personal privacy.
[E] Inclusiveness: Design should empower and engage diverse communities and users of all abilities.
Transparency: AI behavior and decision-making processes should be understandable to users.
Accountability: Creators and deployers of AI are responsible for how their systems operate.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach


NEW QUESTION # 61
You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:
* Accept customer orders
* Retrieve support documents
* Retrieve order status updates.
Which type of NLP should you use?

Answer: D

Explanation:
The chatbot must interpret user intent and route actions such as accepting orders, retrieving support documents, and retrieving order status. This is language understanding , because the system must understand what the user wants to do from natural language input.


NEW QUESTION # 62
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 # 63
Hotspot Question
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to develop an application that uses the Azure OpenAI client library to send prompts to the model.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box: responses
The correct method call uses the OpenAI Responses API, which utilizes
client.responses.create(). This modern endpoint is designed for text and structured generations, moving away from the older chat.completions.create syntax.
Example code:
import os
from openai import OpenAI
# Initialize the client (automatically uses OPENAI_API_KEY environment variable) client = OpenAI()
# Call the Responses API
response = client.responses.create(
model="gpt-4o",
input="Explain quantum computing in one sentence."
)
Reference:
https://developers.openai.com/api/docs/guides/text


NEW QUESTION # 64
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

Answer: B

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.
Why the other options are incorrect:
A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs.
C . Compliance relates to governance and compliance, not model cost comparison.
D . Tools provides Foundry tools, not benchmarked cost comparison across models.


NEW QUESTION # 65
......

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