What is Microsoft AI-900 Exam
Microsoft AI-900 exam is designed for Microsoft partners who are certified by Microsoft to integrate Microsoft Azure Intelligent Solutions into their own products and solutions. Explore the objectives of the AI-900 exam. Protection of the Azure platform and its components. Reference architecture for Azure solutions. Implementation, configuration, and management of Azure services. Request, manage and monitor Azure services. Troubleshooting of Azure services. Microsoft AI-900 exam dumps exam tests Questions are designed to identify the exam takers level of competence in the functions, features, and services of Azure. Depth and Breadth of Knowledge. Processes and practices for developing and deploying intelligent solutions. Hope you like this article. Associate in IT, Eugene is an expert in the fields of software applications and internet technologies. Paying attention to the latest technology is the reason Eugene uses his knowledge of high-end computer systems, application programming, and data entry.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Explain Computer Vision Workloads Features on Azure (15-20%)
Here, the following skills will be measured:
- Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.
- Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
In case you're eager to get to know more about artificial intelligence facets & machine learning functions that relate to Microsoft Azure, then you're recommended to opt for AI-900 test.
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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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