Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.
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