Exam AI-900: Microsoft Azure AI Fundamentals
微軟MCF認證考試考什么？考試內容？
微軟MCF認證考試是分科目的，現在我們要看得就是微軟給出的MCF考試代碼：AI-900 考試名稱：Microsoft Azure AI Fundamentals的大綱，如果你通過了這科考試之后，將會獲得Microsoft Azure AI Fundamentals的MCF證書
注意：由于云技術在不斷發展，本大綱包含了AI-900微軟MCF認證考試中進行衡量的技能。但是，MCF考試并能不確保包括這些技能的最新發展被包含在內，如想了解最新發展，請參閱相關技能的技術文檔。
注意：每個技能下方列出的內容，說明我們將如何評估該技能。但是由于技術不斷更新，此列表不能確保是確定的或詳盡的。
注意：大多數問題都只涉及已經正式發布的功能。考試可能包含在預覽階段功能的問題（如果這些預覽的功能是常用的）。
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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