Audience Profile Candidates for Microsoft Azure AI Fundamentals

Exam AI-900: Microsoft Azure AI Fundamentals – Skills Measured  

Audience Profile 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.

Skills Measured NOTE:  

The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is not definitive or exhaustive. 

NOTE: In most cases, exams do NOT cover preview features, and some features will only be added to an exam when they are GA (General Availability).

https://canvas.msstate.edu/eportfolios/1710/Download_Now/Verified_AI900_Exam_2020

https://pearson.instructure.com/eportfolios/74035/Download_Now/Latest_AI900_Exam_2020

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 
  • 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

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