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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe fundamental principles of machine learning on Azure (15-20%)15-20%- Describe core machine learning concepts
  • 1. Identify features and labels for training data in machine learning
  • 2. Describe how training and validation datasets are used in machine learning
- Identify common machine learning techniques
  • 1. Identify regression machine learning scenarios
  • 2. Identify classification machine learning scenarios
  • 3. Identify features of deep learning techniques
  • 4. Identify clustering machine learning scenarios
- 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
Topic 2: 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 image classification solutions
  • 2. Identify features of object detection solutions
  • 3. Identify features of optical character recognition (OCR) solutions
  • 4. Identify features of semantic segmentation solutions
  • 5. Identify features of image analysis solutions
  • 6. Identify features of face detection and identification solutions
Topic 3: 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 model catalog
  • 3. Describe features and capabilities of Azure AI Foundry
Topic 4: 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 speech recognition and synthesis
  • 5. Identify features and uses for sentiment analysis
  • 6. Identify features and uses for entity recognition
Topic 5: Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify features of common AI workloads
  • 1. Identify features of computer vision workloads
  • 2. Identify features of Natural Language Processing (NLP) workloads
  • 3. Identify features of generative AI workloads
  • 4. Identify features of content moderation and moderation workloads
  • 5. Identify features of knowledge mining workloads
  • 6. Identify features of document intelligence workloads
- Identify guiding principles for responsible AI
  • 1. Describe considerations for reliability and safety in an AI solution
  • 2. Describe considerations for privacy and security in an AI solution
  • 3. Describe considerations for transparency in an AI solution
  • 4. Describe considerations for inclusiveness in an AI solution
  • 5. Describe considerations for fairness in an AI solution
  • 6. Describe considerations for accountability in an AI solution

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Microsoft Azure AI Fundamentals Sample Questions (Q21-Q26):

NEW QUESTION # 21
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 22
Drag and Drop Question
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:


NEW QUESTION # 23
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.
Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use , which maps directly to the Microsoft responsible AI principle of reliability and safety .
The other options are incorrect:
Fairness focuses on reducing bias and treating people equitably.
Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.
Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.
Therefore, the correct answer is reliability and safety .


NEW QUESTION # 24
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?

Answer: C

Explanation:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry


NEW QUESTION # 25
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 # 26
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