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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Describe features of generative AI workloads on Azure (20-25%)	20-25%	- 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 OpenAI service 3. Describe features and capabilities of Azure AI Foundry - 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
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 speech recognition and synthesis 2. Identify features and uses for sentiment analysis 3. Identify features and uses for key phrase extraction 4. Identify features and uses for entity recognition 5. Identify features and uses for translation 6. Identify features and uses for language modeling
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 accountability in an AI solution 3. Describe considerations for privacy and security in an AI solution 4. Describe considerations for reliability and safety in an AI solution 5. Describe considerations for fairness in an AI solution 6. Describe considerations for inclusiveness in an AI solution - Identify features of common AI workloads 1. Identify features of generative AI workloads 2. Identify features of Natural Language Processing (NLP) workloads 3. Identify features of computer vision workloads 4. Identify features of content moderation and moderation workloads 5. Identify features of document intelligence workloads 6. Identify features of knowledge mining workloads
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 - Describe Azure Machine Learning capabilities 1. Describe data and compute services for data science and machine learning 2. Describe model management and deployment capabilities in Azure Machine Learning 3. Describe capabilities of automated machine learning - Identify common machine learning techniques 1. Identify clustering machine learning scenarios 2. Identify classification machine learning scenarios 3. Identify features of deep learning techniques 4. Identify regression machine learning scenarios
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 semantic segmentation solutions 2. Identify features of face detection and identification solutions 3. Identify features of image classification solutions 4. Identify features of image analysis solutions 5. Identify features of object detection solutions 6. Identify features of optical character recognition (OCR) solutions

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Microsoft Azure AI Fundamentals Sample Questions (Q117-Q122):

NEW QUESTION # 117
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
The correct selection is AIProjectClient. In the Microsoft Foundry SDK, AIProjectClient serves as the primary project-level client for accessing and managing resources associated with a Foundry project.
Microsoft documentation specifically exposes operation groups through this client for deployments, agents, and indexes, which directly matches the requirements stated in the question.
For example, the SDK exposes deployments operations for working with models deployed to the project, agents operations for creating and managing agents, and indexes operations for accessing project search indexes. Microsoft also describes the Azure AI Projects client library as a unified Foundry SDK component that connects applications to project resources through a single project endpoint.
ChatCompletionsClient is focused primarily on model inference and chat-completion operations rather than comprehensive project-resource management. FoundryLocalManager is associated with local Foundry capabilities, while ModelCatalogClient would relate to model discovery/catalog functionality rather than deployments, agents, and indexes collectively.
Therefore, the class required for this scenario is AIProjectClient.

NEW QUESTION # 118
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 119
Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?

A. sentiment analysis
B. key phrase extraction
C. summarization
D. Named Entity Recognition (NER)

Answer: D

Explanation:
The adequate text analysis feature is Named Entity Recognition (NER), which is a core prebuilt capability of the Azure AI Language service.
Prebuilt Categorization: The prebuilt NER feature automatically parses unstructured text (like emails) to identify and group elements into standard classes, including Person, Organization, and Location.
No Training Required: Because it relies on state-of-the-art pretrained transformer models, it can be deployed immediately out-of-the-box without requiring data labeling or custom model training.
High Extensibility: If your emails contain highly specialized terms (such as proprietary product IDs or industry-specific roles), you can easily scale to Custom NER within the same architecture.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview

NEW QUESTION # 120
What are two types of generative Al models? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. natural language processing
B. transformer
C. DALLE
D. computer vision
E. GPT

Answer: C,E

Explanation:
GPT and DALL-E are examples of generative AI model types/families.
GPT models generate natural language text, such as answers, summaries, emails, and conversations.
DALL-E models generate images from text descriptions.
Why the other options are incorrect:
A). transformer is a model architecture, not the best answer here as a generative model type/family.
B). computer vision is an AI workload area.
E). natural language processing is an AI workload area, not a specific generative AI model type.

NEW QUESTION # 121
You are creating an app to help employees write emails and reports based on user prompts.
What should you use?

A. Azure AI Speech
B. Azure Machine Learning studio
C. Azure AI Vision
D. Azure OpenAI Service

Answer: D

Explanation:
Azure OpenAI Service can generate text based on user prompts. It leverages GPT (Generative Pre-trained Transformer) models that are trained to understand and generate human-like text.
Users interact with these models by providing a prompt, and the model attempts to produce the most likely continuation or completion of that text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models

NEW QUESTION # 122
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