Microsoft Azure AI Fundamentals (AI-900) Exam

About Microsoft Azure AI Fundamentals (AI-900) Exam

Microsoft Azure AI Fundamentals (AI-900) Exam gives the opportunity to demonstrate your knowledge of common ML and AI workloads and how to implement them on Azure. The Azure AI Fundamentals exam can also be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Who should take the exam?

Microsoft Azure AI Fundamentals (AI-900) Exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required. However, it is suggested to have some general programming knowledge or experience. This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

Skills Acquired

The Microsoft Azure AI Fundamentals (AI-900) Exam helps to prove your skills and expertise in the following domains -

• AI workloads and considerations
• Fundamental principles of machine learning on Azure
• Features of computer vision workloads on Azure
• Features of Natural Language Processing (NLP) workloads on Azure
• Features of conversational AI workloads on Azure.
  

Course Outline

The Microsoft Azure AI Fundamentals (AI-900) Exam covers latest and updated exam topics -

Module 1 - Describe Artificial Intelligence workloads and considerations (15–20%)

1.1 Identify features of common AI workloads

• Identify features of data monitoring and anomaly detection workloads
• Identify features of content moderation and personalization workloads
• Identify computer vision workloads
• Identify natural language processing workloads
• Identify knowledge mining workloads
• Identify document intelligence workloads
• Identify features of generative AI workloads

1.2 Identify guiding principles for responsible AI

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

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

2.1 Identify common machine learning techniques

• Identify regression machine learning scenarios
• Identify classification machine learning scenarios
• Identify clustering machine learning scenarios
• Identify features of deep learning techniques

2.2 Describe core machine learning concepts

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

2.3 Describe Azure Machine Learning capabilities

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

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

3.1 Identify common types of computer vision solution

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

3.2 Identify Azure tools and services for computer vision tasks

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

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

4.1 Identify features of common NLP Workload Scenarios

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

4.2 Identify Azure tools and services for NLP workloads

• Describe capabilities of the Azure AI Language service
• Describe capabilities of the Azure AI Speech service
• Describe capabilities of the Azure AI Translator service

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

5.1 Identify features of generative AI solutions

• Identify features of generative AI models
• Identify common scenarios for generative AI
• Identify responsible AI considerations for generative AI

5.2 Identify capabilities of Azure OpenAI Service

• Describe natural language generation capabilities of Azure OpenAI Service
• Describe code generation capabilities of Azure OpenAI Service
• Describe image generation capabilities of Azure OpenAI Service

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