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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q119-Q124):

NEW QUESTION # 119
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 120
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?

A. Temperature
B. Model version settings
C. Max Completion Tokens
D. Top P

Answer: C

NEW QUESTION # 121
You are developing an AI-powered customer support application. Which task is an example of the Microsoft responsible AI principle of inclusiveness?

A. Encrypt stored customer data and restrict access by using role-based controls.
B. Evaluate model outputs across demographic groups to reduce bias.
C. Design the interface to support multiple languages and screen readers.
D. Provide explanations about how predictions are generated.

Answer: C

Explanation:
The inclusiveness principle states that AI systems should empower everyone and engage people.
Designing for multiple languages and screen readers directly reflects this goal.
Screen reader support ensures accessibility for people with visual impairments.
Multi-language interfaces break down barriers for non-native speakers.
Inclusive design proactively addresses physical, cognitive, and linguistic differences.
Reference:
https://www.netguru.com/blog/fintech-app-accessibility

NEW QUESTION # 122
You need to build an AI solution that responds to user queries with detailed written explanations.
Which type of AI model should you use?

A. a text generation model
B. a speech recognition model
C. an image generation model
D. an embedding model

Answer: A

NEW QUESTION # 123
Your company provides users with clear information about an Al system ' s purpose, functionality, and limitations. Which Microsoft responsible Al principle is this an example of?

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

Answer: C

Explanation:
Providing users with clear information about an AI system's purpose, functionality, and limitations is an example of the Microsoft responsible AI principle of transparency . Transparency means users should be able to understand when and how AI is being used, what the system can do, and what its limitations are.

NEW QUESTION # 124
......

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