Prove that you can describe the following: Artificial Intelligence 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; and features of generative AI workloads on Azure.
This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.
This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:
- Basic cloud concepts
- Client-server applications
You can use Azure AI Fundamentals 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.
Microsoft Azure AI Fundamentals Exam Summary:
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Exam Code | AI-900 |
| Exam Price | $99 (USD) |
| Exam Price | 65 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | AI-900T00: Microsoft Azure AI Fundamentals |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Practice Exam | Microsoft AI-900 Certification Practice Exam |
Microsoft AI-900 Exam Syllabus Topics:
| Topic | Details |
| Describe Artificial Intelligence workloads and considerations (15-20%) | |
| Identify features of common AI 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 |
| 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 |
| Describe fundamental principles of machine learning on Azure (20-25%) | |
| 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 |
| 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 |
| 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 |
| Describe features of computer vision workloads on Azure (15-20%) | |
| 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 |
| 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 |
| Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | |
| 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 |
| 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 |
| Describe features of generative AI workloads on Azure (15-20%) | |
| 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 |
| 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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