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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
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 AI Foundry
  • 3. Describe features and capabilities of Azure OpenAI service
- Identify features of generative AI solutions
  • 1. Identify features of generative AI models
  • 2. Identify responsible AI considerations for generative AI
  • 3. Identify common scenarios for generative AI
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 language modeling
  • 2. Identify features and uses for key phrase extraction
  • 3. Identify features and uses for speech recognition and synthesis
  • 4. Identify features and uses for translation
  • 5. Identify features and uses for sentiment analysis
  • 6. Identify features and uses for entity recognition
- 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
Describe fundamental principles of machine learning on Azure (15-20%)15-20%- Identify common machine learning techniques
  • 1. Identify features of deep learning techniques
  • 2. Identify classification machine learning scenarios
  • 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 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
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 Vision service
  • 2. Describe capabilities of the Azure AI Face detection service
- Identify common types of computer vision solution
  • 1. Identify features of object detection solutions
  • 2. Identify features of image classification solutions
  • 3. Identify features of semantic segmentation solutions
  • 4. Identify features of image analysis solutions
  • 5. Identify features of optical character recognition (OCR) solutions
  • 6. Identify features of face detection and identification solutions
Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify guiding principles for responsible AI
  • 1. Describe considerations for accountability in an AI solution
  • 2. Describe considerations for fairness in an AI solution
  • 3. Describe considerations for reliability and safety in an AI solution
  • 4. Describe considerations for privacy and security in an AI solution
  • 5. Describe considerations for inclusiveness in an AI solution
  • 6. Describe considerations for transparency in an AI solution
- Identify features of common AI workloads
  • 1. Identify features of computer vision workloads
  • 2. Identify features of knowledge mining workloads
  • 3. Identify features of Natural Language Processing (NLP) workloads
  • 4. Identify features of content moderation and moderation workloads
  • 5. Identify features of generative AI workloads
  • 6. Identify features of document intelligence workloads

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2026 Microsoft Valid AI-901: Online Microsoft Azure AI Fundamentals Test

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

NEW QUESTION # 120
You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?

Answer: B

Explanation:
To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.
The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.


NEW QUESTION # 121
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: No
No, batch transcription in Azure Speech does not use a microphone. It processes large volumes of prerecorded audio files stored in locations like Azure Blob Storage.
Box 2: No
No, Azure Speech in Foundry Tools cannot analyze video frames to detect or label objects. It is strictly an audio-focused tool designed for speech-to-text transcription, text-to-speech synthesis, speech translation, and voice analytics Box 3: No No, speech translation in Azure Speech in Foundry Tools outputs translated text as well as optional spoken audio.
Supported Output Formats
Text output: It can output the recognized speech as translated text in one or multiple target languages without generating any spoken audio.
Speech-to-Speech: It can optionally synthesize that translated text into natural-sounding audible speech (audio output) using text-to-speech capabilities.
Source transcription: It simultaneously provides the original source language transcription in text format alongside the translation results.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/batch-transcription
https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/speech-service/speech-to-text/transparency-note


NEW QUESTION # 122
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 # 123
You are deploying a generative Al model to a Microsoft Foundry project. You assign a higher tokens per minute (TPM) allocation to the model. What is the result of this change?

Answer: B

Explanation:
Tokens per minute, or TPM , is a throughput allocation for a model deployment. Assigning a higher TPM allocation increases the amount of token processing capacity available to the deployment, which affects the speed and scale at which the deployment can process requests.
It does not directly make responses longer, change region availability, or automatically add citations.


NEW QUESTION # 124
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box: natural language processing
The interactive answering of questions entered by a user as part of an application is a direct example of natural language processing (NLP).
How NLP Powers Interactive Q&A
Text interpretation: The system uses linguistic rules and machine learning models to read and interpret the user's typed input.
Intent recognition: It figures out what the user wants or needs by analyzing the meaning behind the words.
Response generation: It formulates and delivers a human-like reply back through the application interface.
Reference:
https://www.gauthmath.com/solution/1800845210023942/The-interactive-answering-of-questions-entered-by-a-user-as-part-of-an-applicati


NEW QUESTION # 125
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