These test will help you in preparing for the AI 900 exam. All the latest changes are covered. Latest questions are added for the preparation. Go through it multiple times for preparation. AI 900 consists of following topics: Describe AI workloads and considerations (15-20%)Describe fundamental principles of machine learning on Azure (30-35%)Describe features of computer vision workloads on Azure (15-20%)Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)Describe features of conversational AI workloads on Azure (15-20%)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
