IA Machine and Deep Learning Azure Services Fundamentals  Path Exam AI-900

Describe Artificial Intelligence workloads and considerations

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

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