AI-901 Exam, AI-901 Exam Tips

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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
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 (Q20-Q25):

NEW QUESTION # 20
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: A

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 # 21
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, in enterprise environments like Microsoft Foundry, you can select a supported base model from the model catalog and deploy it directly to create a functional inference endpoint.
Box 2: No
Model deployments are created in the management portal or API first, and the playground is just a tool to test and use an already deployed model.
Box 3: Yes
Yes, after deploying an OpenAI model in an Azure or Microsoft Foundry environment, you can immediately use the built-in interactive playground within the portal to send prompts and review responses.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/how-to/deploy-foundry-models


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

Answer:

Explanation:

Explanation:

Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness.
The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.
The other options are incorrect:
accountability means people and organizations remain responsible for AI systems and their effects.
privacy and security focuses on protecting data and controlling access.
transparency means users should understand when and how AI is being used, including system capabilities and limitations.


NEW QUESTION # 23
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text 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 # 24
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
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:

Explanation:

The completed code is:
poller = client.begin_analyze(
analyzer_id= " invoice " ,
input_url=url
)
result = poller.result()
Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result().
Therefore, to retrieve the analysis results after processing completes, the correct option is:
result
The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.


NEW QUESTION # 25
......

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