Use this quick start guide to collect all the information about Microsoft Azure AI Fundamentals (AI-900) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the AI-900 Microsoft Azure AI Fundamentals exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCF Azure AI certification exam.
The Microsoft Azure AI Fundamentals certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure AI Fundamentals exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCF Azure AI.
Microsoft Azure AI Fundamentals Exam Summary:
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Exam Code | AI-900 |
| Exam Price | $99 (USD) |
| Duration | 65 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | AI-900T00-A: Microsoft Azure AI Fundamentals |
| Schedule Exam | Pearson VUE |
| 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 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 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 |
To ensure success in Microsoft MCF Azure AI certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Microsoft Azure AI Fundamentals (AI-900) exam.