Azure AI-900 Exam Study Guide

Microsoft Certified: Azure AI Fundamentals - Credly

Last week I passed Microsoft’s Azure AI-900 Certification exam and thought it would be helpful to share my experience. This exam is designed to test foundational knowledge of Artificial Intelligence and Machine Learning workloads and how to implement them in Azure. There are no prerequisites for this exam, but some Azure and general programming experience is helpful.

Let’s get started by taking a look at the exam objectives.

Exam Objectives

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 feature engineering and selection
  • 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 studio

  • Automated ML UI
  • 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 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 service (LUIS)
  • Identify capabilities of the Speech service
  • Identify capabilities of the Translator Text 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 common characteristics of conversational AI solutions

Identify Azure services for conversational AI

  • Identify capabilities of the QnA Maker service
  • Identify capabilities of the Azure Bot service

The Exam

The exam consists of 40-60 questions (I had 60) with a 60 minute time limit. There were several different question types including:

  • True/False
  • Multi-select
  • Drag/Drop
  • Fill in the blank

You’ll need to score at least 700 to pass the exam. The exam costs $99, but Microsoft does offer a free exam voucher if you attend one of their Virtual Training days. You can learn more about these training opportunities here.

Study Resources

I used a combination of the Microsoft Learn resources and the Cloudskills AI-900 course to study for this exam. I also took advantage of Whizlabs practice exams. I felt they contained everything I needed to successfully pass. You can check out links to these and various learning resources for the AI-900 for this exam

Summary

AI and Machine Learning is one of the fastest-growing technologies and is enabling us to build amazing software that can significantly improve our lives. This exam will provide you an opportunity to learn more about AI and Machine Learning and validate your foundational AI skills on Azure.

Feel free to reach out with any questions. if you are planning on taking the exam, Good Luck!!

Thank you for reading!