Microsoft Azure AI Fundamentals AZ-900 Certification Overview

Microsoft Certified: Azure AI Fundamentals

Candidates for the Azure AI Fundamentals certification should have a foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

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.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Job Role:

  • AI Engineer
  • Developer
  • Data Scientist
  • Student

Required Exams: AI-900

Certification Details

Take one exam: AI-900: Microsoft Azure AI Fundamentals

Earn the certification: Microsoft Certified: Azure AI Fundamentals

Exam AI-900: Microsoft Azure AI Fundamentals

Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish

Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.

Exam AI-900: Microsoft Azure AI Fundamentals

Candidates for this exam should have a foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Part Of The Requirements For: Microsoft Certified: Azure AI Fundamentals

Related Exams: none

Skills Measured:

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 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

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 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 Azure Bot service

Preparing For The Exam

For the updated and accurate study material for the Microsoft AI-900 exam, Certensure.com is a credible source. All the training material available at Certensure.com is backed by a money-back guarantee for sure success. The comprehensive training package for the AI-900 exam by Certensure.com consists of Study Guides and Testing Engine. The professionally compiled braindumps by Certensure.com are known for the accuracy. The affordable training resources at  Certensure.com can lead you to sure success in the exam AI-900 to earn Microsoft Certified: Azure AI Fundamental credential.

 

Conclusion

Cloud computing is one of the fastest-growing technologies of the present era. With more organizations migrating to the cloud platform from conventional on-premises infrastructure, it has seen phenomenal growth in the job market as well. A large number of professionals with cloud platform-related skills are required by the market. Microsoft is among the second-largest cloud platform in the world. Microsoft Azure is the most popular cloud platform in the enterprise market. Microsoft Azure platform certifications are valuable credentials for the job market. Microsoft Certified: Azure AI Fundamentals is a single exam valued certification for beginners that can be earned with a single Exam AI-900: Microsoft Azure AI Fundamentals.

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Welcome to the Night Helper Blog. The Night Helper Blog was created in 2008. Since then we have been blessed to partner with many well-known Brands like Best Buy, Fisher Price, Toys "R" US., Hasbro, Disney, Teleflora, ClearCorrect, Radio Shack, VTech, KIA Motor, MAZDA and many other great brands. We have three awesome children, plus four adorable very active grandkids. From time to time they too are contributors to the Night Helper Blog. We enjoy reading, listening to music, entertaining, travel, movies, and of course blogging.

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