AI-900 Exam Study Guide (Microsoft Azure AI Fundamentals)

AI-900 Microsoft Azure AI Fundamentals Exam Study Guide 2020

Preparing for the AI-900 Microsoft Azure AI Fundamentals Certificate exam? Don’t know where to start? This post is the AI-900 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the AI-900 exam. I hope this article will help you to prepare for the AI-900 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

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Links for exam objectives to be updated soon

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 common features of feature selection and engineering
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

Automated Machine Learning tool
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 semantic segmentation solutions
Identify features of optical character recognition solutions
Identify features of facial detection, recognition, and 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 Intelligence Service (LUIS)
Identify capabilities of the Speech service
Identify capabilities of the Text Translator 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 features and uses for telephone voice menus
Identify features and uses for personal digital assistants

Identify Azure services for conversational AI

Identify capabilities of the QnA Maker service
Identify capabilities of the Bot Framework

This brings us to the end of AI-900 Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

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