Free PDF Quiz 2026 Microsoft AI-901: Reliable Microsoft Azure AI Fundamentals Pass Rate

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q37-Q42):

NEW QUESTION # 37
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: Generating a response to a user prompt occurs during the inference stage. = Yes When a deployed generative AI model receives a user prompt and generates an output, that process is called inference. The model is being used to produce a response, not being trained.
Statement 2: A generative AI model generates responses by copying stored documents directly from the model's training data. = No Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.
Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model's training data. = Yes This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.


NEW QUESTION # 38
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 # 39
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 # 40
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value 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 selection is worth one point.

Answer:

Explanation:

Explanation:

input=[
{
" role " : " user " ,
" content " : [
{ " type " : " input_text " , " text " : " What is in this image? Provide 3 bullet points. " },
{ " type " : " input_image " , " image_url " : image_url},
],
}
]
print(response.output_text)
For a multimodal request that sends both text and an image to an Azure OpenAI model, the input content array uses separate content item types. Text content is sent with type: " input_text " , and image content is sent with type: " input_image " . Microsoft's Azure OpenAI Responses API documentation shows the Responses API supports multimodal inputs and uses structured input content items in the request.
The options output_text and output_image are not used inside the user input content array. In the shown code, response.output_text is used after the response is returned to print the model's text answer.


NEW QUESTION # 41
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type 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 selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 42
......

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