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

AI-900 Microsoft Azure AI Fundamentals Certificate Exam Study Guide

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

AI-900 Azure AI Fundamentals Practice Test

AI-900 Azure AI Fundamentals Other Stuff

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

Identify Features of Common 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

Watch videos on Responsible AI principles from Microsoft like Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency & Accountability

https://www.microsoft.com/en-us/research/theme/fate/

AI-900

Amazon link (affiliate)

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

Identify Common Machine Learning Types

Describe Core Machine Learning Concepts

Identify features and labels in a dataset for machine learning

https://developers.google.com/machine-learning/crash-course/framing/ml-terminology

Describe how training and validation datasets are used in machine learning

https://towardsdatascience.com/train-validation-and-test-sets

Describe how machine learning algorithms are used for model training

https://blogs.sas.com/content/machine-learning-algorithm-use/

Select and interpret model evaluation metrics for classification and regression

Classification: https://docs.microsoft.com/en-us/azure/machine-learning/evaluate-model

Regression: https://docs.microsoft.com/en-us/azure/machine-learning/evaluate-model

Identify Core Tasks in Creating a Machine Learning Solution

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

Describe Features of Computer Vision Workloads on Azure (15-20%)

Identify Common Types of Computer Vision Solution

Identify Azure Tools and Services for Computer Vision Tasks

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

Identify Features of Common NLP Workload Scenarios

Identify Azure Tools and Services for NLP Workloads

Identify capabilities of the Text Analytics service

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

Identify capabilities of the Language Understanding Intelligence Service (LUIS)

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis

Identify capabilities of the Speech service

https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/overview

Identify capabilities of the Text Translator service

https://www.microsoft.com/en-us/translator/business/translator-api/

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

Identify Common Use Cases for Conversational AI

Identify features and uses for webchat bots

https://github.com/microsoft/BotFramework-WebChat

Identify features and uses for telephone voice menus

https://docs.microsoft.com/en-us/microsoftteams/what-are-phone-system-auto-attendants

Identify features and uses for personal digital assistants

https://docs.microsoft.com/en-us/cortana/skills/overview

Identify Azure Services for Conversational AI

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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