Learning Path for AI-900 Microsoft Azure AI Fundamentals Certification

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Index

Prerequisites

This certification is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial. Candidates should have fundamental knowledge of Machine Learning(ML) and Artificial Intelligence(AI) concepts and related Microsoft Azure services.

Why to do it?

This certificate provides you an opportunity to demonstrate the knowledge of common ML and AI workloads and how to implement them on Azure cloud. Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate. It will add value to your skill set and expertise if you into job roles like AI Engineer, Data Scientist, Developer, Solutions Architect etc.

Exam, Languages & Price

To get this certificate you need to pass ‘Exam AI-900’, which is available in English, Japanese, Chinese(Simplified), Korean, German, French and Spanish. Price is based on the country in which the exam is proctored. In the USA it’s for $99 USD and in India it’s for ₹3696 INR.

In exam, you will get 60 minutes to answer around 53 multiple choice questions. To pass the exam, you will need to score 700 points out of 1000. I got 890 points, Link to my certification badge

Skills Measured

Skill Weightage
Describe AI workloads and considerations 15-20%
Describe fundamental principles of machine learning on Azure 30-35%
Describe features of computer vision workloads on Azure 15-20%
Describe features of Natural Language Processing (NLP) workloads on Azure 15-20%
Describe features of conversational AI workloads on Azure 15-20%

Learning Path

There are two ways to prepare for this exam. You can either self-teach using free online resources or can go for instructor led path. In this article I will list all the required resources from Microsoft Learn to clear this exam. Remember objective should be to achieve the necessary knowledge instead of just clearing certifications. If you google it, you will find tons of material with question and answers for this exam. But that won’t help you to gain necessary knowledge!! In machine learning terminology, use all the learning material as ‘training data’ and use online question dumps as your ‘test data’. Remember if you use ‘test data’ during training then it may result in good score but will definitely fail in real life scenarios!

Below are the learning resources for each of the section mentioned in skill measured table. At the end of each learning resource there is knowledge check section, to test your understanding of a particular module.

For doing the labs you can use free Azure subscription. Since some options wasnt available for Indian region I used USA regions for my labs. And please be patient its painfully slow!!

Describe AI workloads and considerations

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This section has around 15 to 20 % weightage in exam. It tests candidates ability to identify features of common AI workloads like below,

  • 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

Apart from identifying the common AI workload this section also expect candidates to 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

Suggested online resource is as below

Describe fundamental principles of machine learning on Azure

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This section has around 30 to 35 % weightage in exam. It tests candidates ability to identify common machine learning types, describe core machine learning concepts, identify core tasks in creating a machine learning solution and to describe capabilities of no-code machine learning with Azure Machine Learning.

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 UI
  • Azure Machine Learning designer

Suggested online resource is as below

Describe features of computer vision workloads on Azure

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This section has around 15 to 20 % weightage in exam. It tests candidates ability to identify common types of computer vision solutions and using Azure tools and services to solve them.

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, 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

Suggested online resource is as below

Describe features of Natural Language Processing (NLP) workloads on Azure

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This section has around 15 to 20 % weightage in exam. It tests candidates ability to identify features of common NLP Workload Scenarios and required Azure tools to solve them.

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 Translator Text service

Suggested online resource is as below

Describe features of conversational AI workloads on Azure

explore-conversational-ai.svg

This section has around 15 to 20 % weightage in exam. It tests candidates ability to identify common use cases for conversational AI and available Azure services for conversational AI.

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 common characteristics of conversational AI solutions

Identify Azure services for conversational AI

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

Suggested online resource is as below

Other Links

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