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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Describe features of computer vision workloads on Azure (15-20%)	15-20%	- Identify Azure tools and services for computer vision tasks 1. Describe capabilities of the Azure AI Face detection service 2. Describe capabilities of the Azure AI Vision service - Identify common types of computer vision solution 1. Identify features of image classification solutions 2. Identify features of optical character recognition (OCR) solutions 3. Identify features of face detection and identification solutions 4. Identify features of object detection solutions 5. Identify features of semantic segmentation solutions 6. Identify features of image analysis solutions
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)	15-20%	- Identify features of common NLP workload scenarios 1. Identify features and uses for entity recognition 2. Identify features and uses for sentiment analysis 3. Identify features and uses for language modeling 4. Identify features and uses for translation 5. Identify features and uses for speech recognition and synthesis 6. Identify features and uses for key phrase extraction - Identify Azure tools and services for NLP workloads 1. Describe capabilities of the Azure AI Speech service 2. Describe capabilities of the Azure AI Language service
Describe features of generative AI workloads on Azure (20-25%)	20-25%	- Identify features of generative AI solutions 1. Identify responsible AI considerations for generative AI 2. Identify common scenarios for generative AI 3. Identify features of generative AI models - Identify generative AI services and capabilities in Microsoft Azure 1. Describe features and capabilities of Azure AI Foundry model catalog 2. Describe features and capabilities of Azure AI Foundry 3. Describe features and capabilities of Azure OpenAI service
Describe fundamental principles of machine learning on Azure (15-20%)	15-20%	- Describe Azure Machine Learning capabilities 1. Describe capabilities of automated machine learning 2. Describe model management and deployment capabilities in Azure Machine Learning 3. Describe data and compute services for data science and machine learning - Identify common machine learning techniques 1. Identify classification machine learning scenarios 2. Identify features of deep learning techniques 3. Identify regression machine learning scenarios 4. Identify clustering machine learning scenarios - Describe core machine learning concepts 1. Describe how training and validation datasets are used in machine learning 2. Identify features and labels for training data in machine learning
Describe Artificial Intelligence workloads and considerations (15-20%)	15-20%	- Identify guiding principles for responsible AI 1. Describe considerations for privacy and security in an AI solution 2. Describe considerations for inclusiveness in an AI solution 3. Describe considerations for fairness in an AI solution 4. Describe considerations for transparency in an AI solution 5. Describe considerations for reliability and safety in an AI solution 6. Describe considerations for accountability in an AI solution - Identify features of common AI workloads 1. Identify features of content moderation and moderation workloads 2. Identify features of computer vision workloads 3. Identify features of knowledge mining workloads 4. Identify features of generative AI workloads 5. Identify features of document intelligence workloads 6. Identify features of Natural Language Processing (NLP) workloads

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Microsoft Azure AI Fundamentals Sample Questions (Q120-Q125):

NEW QUESTION # 120
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 121
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 # 122
Your company provides users with clear information about an AI system's purpose, functionality, and limitations. Which Microsoft responsible AI principle is this an example of?

A. fairness
B. accountability
C. reliability and safety
D. transparency

Answer: D

Explanation:
Transparency is the Microsoft responsible AI principle that provides clear information about an AI system's purpose, how it works, and its limitations.
System Purpose: Explains what the AI is designed to do and how it helps users.
Functionality: Shows how the model makes decisions or generates content.
Limitations: Warns users about potential errors, blind spots, or times when the AI might be wrong.
User Awareness: Ensures people know they are interacting with an AI system.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach

NEW QUESTION # 123
Hotspot Question
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:

NEW QUESTION # 124
You have an Azure subscription.
You need to use Azure Content Understanding in Foundry Tools to extract structured data from invoices.
What should you provision?

A. A Microsoft Foundry project
B. a Microsoft Foundry resource
C. an Azure AI Search service
D. an Azure OpenAI resource

Answer: B

Explanation:
To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.
The invoice scenario is also directly aligned with Content Understanding's intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.
Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.

NEW QUESTION # 125
......

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