New 2022 MS Azure AI Fundamentals AI-900 Practical Exams

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