Exam AI-900: Microsoft Azure AI Fundamentals
微软MCF认证考试考什么？考试内容？
微软MCF认证考试是分科目的，现在我们要看得就是微软给出的MCF考试代码：AI-900 考试名称：Microsoft Azure AI Fundamentals的大纲，如果你通过了这科考试之后，将会获得Microsoft Azure AI Fundamentals的MCF证书
注意：由于云技术在不断发展，本大纲包含了AI-900微软MCF认证考试中进行衡量的技能。但是，MCF考试并能不确保包括这些技能的最新发展被包含在内，如想了解最新发展，请参阅相关技能的技术文档。
注意：每个技能下方列出的内容，说明我们将如何评估该技能。但是由于技术不断更新，此列表不能确保是确定的或详尽的。
注意：大多数问题都只涉及已经正式发布的功能。考试可能包含在预览阶段功能的问题（如果这些预览的功能是常用的）。
Describe Artificial Intelligence workloads and considerations (15-20%)
- Identify features of common AI workloads
- identify prediction/forecasting workloads
- identify features of anomaly detection workloads
- identify computer vision workloads
- identify natural language processing or knowledge mining workloads
- identify conversational 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 (30-35%)
- 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 how machine learning algorithms are used for model training
- select and interpret model evaluation metrics for classification and regression
- Identify core tasks in creating a machine learning solution
- describe common features of data ingestion and preparation
- describe feature engineering and selection
- describe common features of model training and evaluation
- describe common features of model deployment and management
- Describe capabilities of no-code machine learning with Azure Machine Learning studio
- automated ML UI
- 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 (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
- identify capabilities of the Text Analytics service
- identify capabilities of the Language Understanding service (LUIS)
- identify capabilities of the Speech service
- identify capabilities of the Translator Text service
Describe features of conversational AI workloads on Azure (15-20%)
- Identify common use cases for conversational AI
- identify features and uses for webchat bots
- identify common characteristics of conversational AI solutions
- Identify Azure services for conversational AI
- identify capabilities of the QnA Maker service
- identify capabilities of the Azure Bot service
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