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Exam AI-900: Microsoft Azure AI Fundamentals Crash Course

Topic: Data
Tim Warner

Artificial Intelligence (AI) is the capability of a computer to imitate human behavior. Microsoft Azure includes a suite of tools that enable AI engineers to ingest data, build machine learning (ML) models, and deploy those models as REST API web services. Azure also includes a number of platform-as-a-service cognitive services that give Azure developers easy access to pre-trained ML models.

Because AI/ML factor into most customer-facing software nowadays, coupled with the Azure AI/ML tools' steep learning curve, this training presents an excellent opportunity for IT professionals to learn the technology in a clear, low-stress manner.

Specifically, this Crash Course covers the objectives for Microsoft Exam AI-900: Microsoft Azure AI Fundamentals. This exam fulfills the requirements of the Microsoft Certified AI Fundamentals certification.

Note that Microsoft considers their 900-series exams to be beginner-to-intermediate level skill sets. The certification is intended for both technical and non-technical audiences, and covers the Azure AI skills from a mostly theoretical viewpoint.

What you'll learn-and how you can apply it

At course conclusion, you will be able to:

  • Perform additional review and then pass Exam AI-900: Microsoft Azure AI Fundamentals
  • Describe artificial intelligence workloads and considerations
  • Describe fundamental machine learning concepts on Azure
  • Describe computer vision workloads on Azure
  • Describe conversational workloads on Azure

This training course is for you because...

  • Azure AI certification candidates
  • Future, present, or future data scientists or AI/ML engineers
  • Azure professionals who will support Azure AI/ML projects
  • Non-technical professionals who need to understand a bit of how Azure AI works

Prerequisites

The AI-900 is supposed to be a beginner/fundamentals skill set. Thus, the core prerequisite is that the learner has intermediate knowledge of Azure from any job role: administrator, developer, architect, business analyst, etc.

Ideally the learner is already a data scientist with Python, Scala, or R programming knowledge, but I'll teach to any level.

Course Set-up

To follow along with the demonstrations and practice on his or her own, the learner should have the following environment available:

  • Windows or macOS computer
  • Web browser and Internet connection
  • Microsoft account to create an Azure trial subscription (free)
  • Microsoft Azure 30-day trial (free)

If the learner wants to dive in and actually practice the skills, he or she should also have:

  • A paid Azure subscription if they already used their trial
  • Visual Studio 2019 (the free Community edition is fine)
  • Visual Studio Code (free)
  • Azure Command-Line Interface (CLI) (free)
  • Docker Desktop software (free)

The course files will be available at Tim's GitHub repository:

https://github.com/timothywarner/ai900

Recommended Preparation

Recommended Follow-up

About your instructor

  • Tim Warner is a Microsoft Most Valuable Professional (MVP) in Cloud and Datacenter Management based in Nashville, TN. He is a Pluralsight staff instructor, a Microsoft Press author, and a part-time Microsoft Azure solutions consultant. Tim’s professional specialties include Microsoft Azure, cross-platform PowerShell, and all things Windows Server-related. You can reach Tim via Twitter (@TechTrainerTim), LinkedIn or his personal website, techtrainertim.com

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

DAY 1

Introduction (5 minutes)

  • Set expectations
  • Perform high-level course overview
  • Provide strategies to maximize benefit from this course

Module 1: Identify Features of Common AI Workloads (40 minutes)

  • Identify prediction/forecasting workloads
  • Identify computer vision workloads
  • Identity conversational and natural language processing (NLP) workloads

Module 2: Identify Guiding Principles for Responsible AI (40 minutes)

  • Describe considerations for fairness and reliability in an AI solution
  • Describe considerations for privacy and security in an AI solution
  • Describe considerations for inclusiveness and transparency in an AI solution

Module 3: Identify Common Machine Learning Types (40 minutes)

  • Identify regression ML scenarios
  • Identify classification ML scenarios
  • Identify clustering ML scenarios

(Break: 5 minutes)

Module 4: Describe Core Machine Learning Concepts (40 minutes)

  • Identify features and labels in a dataset for ML
  • Describe how training and validations datasets work
  • Select and interpret model evaluation metrics

Module 5: Identify Core Tasks in Creating a Machine Learning Solution (40 minutes)

  • Describe data ingestion and preparation features
  • Describe feature selection and engineering features
  • Describe model deployment and management features

Module 6: Describe No-Code Machine Learning in Azure Machine Learning (40 minutes)

  • Describe the Automated Machine Learning tool
  • Describe the Azure Machine Learning designer

Review, wrap-up: 10 minutes

DAY 2

Introduction (5 minutes)

  • Briefly review Day 1 material
  • Perform Day 2 content overview
  • Answer any outstanding questions from Day 1

Module 7: Identify Common Types of Computer Vision Solutions (40 minutes)

  • Identify image classification solutions
  • Identify object detection solutions
  • Identify facial detection solutions

Module 8: Identify Azure Tools and Services for Computer Vision Tasks (40 minutes)

  • Identify Computer Vision service capabilities
  • Identity Face service capabilities
  • Identify Form Recognizer service capabilities

Module 9: Describe Features of NLP Workloads in Azure (40 minutes)

  • Identify common NLP workload scenarios
  • Identify entity recognition features and uses
  • Identify language modeling features and uses

(Break: 5 minutes)

Module 10: Identify Common Uses Cases for Conversational AI (40 minutes)

  • Identify webchat bot features and uses
  • Identify telephone voice menu features and uses
  • Identify personal digital assistant features and uses

Module 11: Identify Azure Services for Conversational AI (40 minutes)

  • Identify QnA Maker service capabilities
  • Identify Bot Framework service capabilities

Module 12: Exam AI-900 Strategy (40 minutes)

  • Microsoft Online testing process
  • Exam item tips and tricks

Review, wrap-up: 10 minutes