Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Azure AI Fundamentals
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The technologies that are tested on Microsoft AI-900 exam are necessary to develop intelligent operations based on cloud computing. Audience for this certification exam is individuals who work as computer programmers, developers, system administrators and project managers. Built for high-volume, high-speed web applications that use Azure, this Examines the skills required to develop intelligent cloud solutions. Smarter IT environments are the goal of this exam. Single sign-on for Azure, is an exam objective. Microsoft AI-900 exam dumps helps to make it easier for students to get this certification. Outline the business value of the exam. The importance of having this certification. Identify the technologies covered on Microsoft AI-900 exam. Gain a better understanding of the skills you'll need to develop intelligent cloud solutions.
Incredible opportunities are associated with this certification. Background on Azure and its products, services, networks, and the scalability of its computing infrastructure. Background of cloud computing in general. Automatically configure Azure virtual machines. Implement and configure SQL Server Integration Services (SSIS), Visual Studio Team Services (VSTS) and other Microsoft technologies. Predict the effects of each of the possible outcomes of the solution design. Led you to the final test. Ability to perform automated deployment of Azure services.
Workers who take this exam should have experience in the IT industry and should understand the architecture and business requirements of Azure. Leave no stone unturned. Personalized study plan for self-paced learning. Tests your knowledge in the whole Cloud Computing path from Azure to Office 365.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Describe Artificial Intelligence workloads and considerations (20-25%) | |
| Identify features of common AI workloads | - identify features of anomaly detection workloads - identify computer vision workloads - identify natural language processing workloads - identify knowledge mining 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 (25-30%) | |
| Identify common machine learning types | - identify regression machine learning scenarios - identify classification machine learning scenarios - identify clustering machine learning scenarios |
| 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 capabilities of visual tools in Azure Machine Learning studio | - automated machine learning - azure Machine Learning designer |
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, facial recognition, and facial analysis solutions |
| Identify Azure tools and services for computer vision tasks | - identify capabilities of the Computer Vision service - identify capabilities of the Custom Vision service - identify capabilities of the Face service - identify capabilities of the Form Recognizer service |
Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%) | |
| 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 | - identify capabilities of the Language service - identify capabilities of the Speech service - identify capabilities of the Translator service |
| Identify considerations for conversational AI solutions on Azure | - identify features and uses for bots - identify capabilities of the Azure Bot service |
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