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Microsoft AI-901 Exam Syllabus Topics:

Topic	Details
Topic 1	Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2	Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

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Microsoft Azure AI Fundamentals Sample Questions (Q21-Q26):

NEW QUESTION # 21
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Counting the number of animals in an area based on a video feed is an example of computer vision.
Computer vision is the AI workload used to analyze visual content such as images and video. Counting animals in a video feed requires detecting visual objects in video frames and analyzing them, which is a computer vision task.
Forecasting predicts future values or trends from historical data.
Knowledge mining extracts insights from large document or data collections.
Therefore, the correct answer is computer vision.

NEW QUESTION # 22
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 23
You are developing a lightweight application that will call an agent programmatically by using the Microsoft Foundry SDK. You need to identify the agent ID. What should you do?

A. In the model catalog, open the model card and copy the model family name.
B. In the Foundry playground, select Code, and view the .env variables.
C. In the Foundry tool catalog, copy the tool name used by the agent.
D. the Foundry portal, copy the project name.

Answer: B

Explanation:
When calling a Foundry agent programmatically by using the Microsoft Foundry SDK, the application needs the agent's configuration values, including the agent ID . The Foundry playground provides a Code view that shows sample application code and environment variables needed to call the agent.
The other options are incorrect:
A). copy the tool name used by the agent identifies a tool, not the agent.
B). copy the model family name identifies the model family, not the agent instance.
C). copy the project name identifies the project, not the specific agent ID.

NEW QUESTION # 24
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to develop an application that sends a message containing text and an image URL.
The solution must ensure the quickest response time.
Which message structure should you include in the request?

A. a system message that includes only a text item and sends an image item in a separate request
B. a system message that includes both a text item and an image item in the content array
C. a user message that includes only a text item and sends an image item in a separate request
D. a user message that includes both a text item and an image item in the content array

Answer: D

Explanation:
To achieve the fastest possible response time in Microsoft Foundry/Azure AI, you must combine the text prompt and image URL into a single user message, utilizing a system message for instructions, and opting for a low-latency model like gpt-4o-mini.
Sending separate requests or splitting the text and image into different messages increases network overhead and conversation context length, both of which add unnecessary latency.
Note:
1. Required Message Structure
You should include both a system message and a custom user message in your API request payload:
System Message: Use this to define the context, guardrails, and explicit format requirements. It sets up the model behavior before the image is even processed, optimizing processing efficiency.
User Message (Custom Message): This is where you pass your text prompt and the actual image reference.
2. Request Handling: Single vs. Separate Requests
You must send the text item and the image item in the same single request.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/transitioning-from-azure-language-features-to-foundry-models/4524092

NEW QUESTION # 25
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:

Explanation:
Box 1: Yes
Yes, limiting access to AI systems and data directly reduces the risk of unauthorized data exposure by minimizing attack surfaces and preventing accidental leaks.
Key Security Benefits
Fewer Entry Points: Restricting who and what can connect to your AI model lowers the chance of a security breach.
Controlled Data Flow: Limiting data inputs ensures sensitive user or corporate information does not enter training pipelines or public logs.
Better Compliance: Strict access rules help meet privacy laws and internal security policies.
Box 2: No
Sharing personal user data does not automatically improve collaboration or support responsible AI practices; in many cases, it creates significant privacy, security, and ethical risks that run counter to responsible AI principles.
Box 3: Yes
Yes, protecting personal data and complying with regulations are essential parts of responsible AI. They build trust, respect user privacy, and prevent illegal data use.
User Trust: People want to know their private data is safe.
Legal Rules: Laws punish companies that misuse data.
Fairness: Good data habits stop AI from learning bad or biased habits.
Reference:
https://www.linkedin.com/top-content/artificial-intelligence/ai-in-cybersecurity/data-security-issues-in-artificial-intelligence/
https://pmc.ncbi.nlm.nih.gov/articles/PMC10498316/

NEW QUESTION # 26
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