Microsoft Azure AI Fundamentals

 

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, awareness of cloud basics and client-server applications 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.
 

Skills measured Describe Artificial Intelligence workloads and considerations (20— 25%) Identify features of common AI workloads

• identify features of anomaly detection workloads
• identify computer vision workloads
• identify natural language processing workloads
• identify knowledge mining 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 (25— 30%) 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 capabilities of visual tools in Azure Machine Learning Studio

• automated machine learning
• 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

• Azure (25—30%)

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 Language service
• identify capabilities of the Speech service
• identify capabilities of the Translator service

Identify considerations for conversational AI solutions on Azure

• identify features and uses for bots
• identify capabilities of the Azure Bot service