Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Microsoft Azure AI Fundamentals (AI-900)

 

The Microsoft Azure AI Fundamentals (AI-900) exam helps you understand the basics of machine learning and AI, along with how to use Microsoft Azure services for them. This exam requires familiarity with the learning materials provided for Exam AI-900, whether you're studying at your own pace or with an instructor.

 

Who should take the exam?

This exam is designed for both technical and non-technical backgrounds. Data science and software engineering experience are not required. 

 

What will you learn in the exam?

The exam will help you learn about:

• Basic cloud concepts
• Client-server applications

 

Exam Details of Microsoft AI-900 

• Exam Code: AI-900
• Exam Name: Microsoft Azure AI Fundamentals
• Exam Languages: English, Japanese, Chinese (Simplified), Korean, Spanish, German, French, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian
• Exam Questions: 40-60 Questions
• Passing Score: 700 or greater (On a scale 1 - 1000)

 

AI-900 Exam Course Outline 

The Microsoft Azure AI Fundamentals (AI-900) Exam covers the given topics  - 

Domain 1 - Learn about Artificial Intelligence workloads and considerations (15–20%)

1.1 Identifying features of common AI workloads

• Identifying features of content moderation and personalization workloads

• Identifying computer vision workloads

• Discovering natural language processing workloads

• Identifying knowledge mining workloads

• Identifying document intelligence workloads

• Discovering features of generative AI workloads

 

1.2 Identifying guiding principles for responsible AI

• Explaining considerations for fairness in an AI solution
• Describing considerations for reliability and safety in an AI solution
• Explaining considerations for privacy and security in an AI solution
• Describing considerations for inclusiveness in an AI solution
• Describing considerations for transparency in an AI solution
• Describing considerations for accountability in an AI solution

 

Domain 2 - Understanding fundamental principles of machine learning on Azure (20–25%)

2.1 Identifying common machine learning techniques

• Discovering regression machine learning scenarios
• Identifying classification machine learning scenarios
• Identifying clustering machine learning scenarios
• Identifying features of deep learning techniques

 

2.2 Explaining core machine learning concepts

• Identifying features and labels in a dataset for machine learning
• Explaining how training and validation datasets are used in machine learning

 

2.3 Describing Azure Machine Learning capabilities

• Describing capabilities of Automated machine learning
• Describing data and compute services for data science and machine learning
• Explaining model management and deployment capabilities in Azure Machine Learning

 

Domain 3 - Understand features of computer vision workloads on Azure (15–20%)

3.1 Identifying common types of computer vision solution

• Discovering features of image classification solutions
• Identifying features of object detection solutions
• Discovering features of optical character recognition solutions
• Discovering features of facial detection and facial analysis solutions

 

3.2 Discovering Azure tools and services for computer vision tasks

• Describing capabilities of the Azure AI Vision service
• Explaining capabilities of the Azure AI Face detection service
• Describing capabilities of the Azure AI Video Indexer service

 

Domain 4 - Learn the features of Natural Language Processing (NLP) workloads on Azure (15–20%)

4.1 Discovering features of common NLP Workload Scenarios

• Identifying features and uses for key phrase extraction
• Discovering features and uses for entity recognition
• Identifying features and uses for sentiment analysis
• Identifying features and uses for language modeling
• Identifying features and uses for speech recognition and synthesis
• Discovering features and uses for translation

 

4.2 Identifying Azure tools and services for NLP workloads

• Explaining capabilities of the Azure AI Language service
• Describing capabilities of the Azure AI Speech service
• Describing capabilities of the Azure AI Translator service

 

Domain 5 - Understand features of generative AI workloads on Azure (15–20%)

5.1 Identifying features of generative AI solutions

• Discovering features of generative AI models
• Identifying common scenarios for generative AI
• Identifying responsible AI considerations for generative AI

 

5.2 Identifying capabilities of Azure OpenAI Service

• Explaining natural language generation capabilities of Azure OpenAI Service
• Describing code generation capabilities of Azure OpenAI Service
• Describing image generation capabilities of Azure OpenAI Service