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

AI-900 Microsoft Azure AI Fundamentals Certificate Exam Study Guide

AI-900 Preparation Details

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.

AI-900 Azure AI Fundamentals Online Course

LinkedIn Learning (Free trial) Exam Tips: Microsoft Azure AI Fundamentals
Udemy Microsoft Azure AI Fundamentals Preparation
Pluralsight Artificial Intelligence: Executive Briefing
Coursera AI Fundamentals Exam Prep Specialization

AI-900 Azure AI Fundamentals Practice Test

Whizlabs Exam Questions Microsoft Azure Fundamental Practice Test
Udemy Practice Tests AI Fundamentals Certificate Exam

AI-900 Azure AI Fundamentals Other Stuff

Udacity (Nanodegree) Artificial Intelligence Nanodegree Program
Amazon e-book (PDF) Microsoft Azure AI Fundamentals

AI-900 Sample Practice Exam Questions

AI-900 Microsoft Azure AI Fundamentals Sample Practice Test

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Describe Artificial Intelligence Workloads and Considerations (15-20%)

Identify Features of Common AI Workloads

Identify prediction/forecasting workloads

Demand Forecasting

Demand forecasting & price optimization

Demand forecasting for shipping and distribution

Personalized offers

Identify features of anomaly detection workloads

Anomaly detector

Identify computer vision workloads

Applying content tags to images

Detect common objects in images

Detect popular brands in images

Categorize images by subject matter

Describe images with a human-readable language

Identify natural language processing or knowledge mining workloads

Choosing a natural language processing technology in Azure

Identify conversational AI workloads

Conversational AI tools: To build, connect & manage intelligent bots

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

Microsoft AI principles

FATE: Fairness, Accountability, Transparency & Ethics in AI

Exam AI-900 Azure AI Fundamentals Study Guide

Amazon link (affiliate)

Describe Fundamental Principles of Machine Learning on Azure (30-35%)

Identify Common Machine Learning Types

Identify regression machine learning scenarios

Linear Regression

Identify classification machine learning scenarios

Classification modules

Identify clustering machine learning scenarios

Clustering modules

Describe Core Machine Learning Concepts

Identify features and labels in a dataset for machine learning

Framing: Key ML Terminology

Describe how training and validation datasets are used in machine learning

About train, validation & test sets in Machine learning

Describe how machine learning algorithms are used for model training

Which machine learning algorithm should I use?

Select and interpret model evaluation metrics for classification and regression

Metrics for Classification models

Metrics for Regression models

Azure certification Frequently Asked Questions

Identify Core Tasks in Creating a Machine Learning Solution

Describe common features of data ingestion and preparation

AI Workflow: Data ingestion

Data Preparation for Machine Learning

Describe feature engineering and selection

Beginner’s guide to feature selection in Python

Representation: Feature Engineering

Describe common features of model training and evaluation

Model Training

Evaluate model

Introduction to Machine learning model evaluation

Describe common features of model deployment and management

Deploy real-time machine learning services with Azure Machine Learning

Machine Learning Operations (MLOps)

Describe Capabilities of No-Code Machine Learning with Azure Machine Learning Studio

Automated ML Wizard UI

What is automated machine learning (AutoML)?

Azure Machine Learning designer

What is an 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

Train image classification models with MNIST data & sci-kit-learn

Identify features of object detection solutions

Detect common objects in images

Identify features of optical character recognition solutions

Optical Character Recognition (OCR)

Identify features of facial detection, recognition, and analysis solutions

Face detection and attributes

Face recognition concepts

How to analyze videos in real-time?

Identify Azure Tools and Services for Computer Vision Tasks

Identify capabilities of the Computer Vision service

What is Computer Vision?

Identify the capabilities of the Custom Vision service

What is Custom Vision?

Identify capabilities of the Face service

What is the Azure Face service?

Identify capabilities of the Form Recognizer service

What is a Form Recognizer?

Describe Features of Natural Language Processing (NLP) Workloads on Azure (15-20%)

Identify Features of Common NLP Workload Scenarios

Identify features and uses for keyphrase extraction

How to extract key phrases using Text Analytics?

Identify features and uses for entity recognition

Entity recognition cognitive skill

Identify features and uses for sentiment analysis

Sentiment analysis with cognitive service & Azure

Identify features and uses for language modeling

MSRLM: A scalable language modeling toolkit

Identify features and uses for speech recognition and synthesis

What is Speech service?

Get started with speech-to-text

Identify features and uses for translation

Translator app features

AI-900 Exam Details and Tips

Identify Azure Tools and Services for NLP Workloads

Identify the capabilities of the Text Analytics service

What is the Text Analytics API?

Identify the capabilities of the Language Understanding Service (LUIS)

What is Language Understanding (LUIS)?

Identify the capabilities of the Speech service

What is Speech service?

Identify capabilities of the Text Translator service

Translator: Value of Translator

Describe Features of Conversational AI Workloads on Azure (15-20%)

Identify Common Use Cases for Conversational AI

Identify features and uses for webchat bots

Web Chat overview

Identify common characteristics of conversational AI solutions

Microsoft Conversational AI tools

Identify Azure Services for Conversational AI

Identify capabilities of the QnA Maker service

What is QnA Maker?

Identify capabilities of the Azure Bot Service

Azure Bot Service overview

This brings us to the end of the AI-900 Azure AI Fundamentals 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!

In case you are preparing for other Azure certification exams, check out the Azure study guide for those exams.

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