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Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Microsoft Azure AI Fundamentals (AI-900) Exam


About Microsoft Azure AI Fundamentals (AI-900) Exam

Microsoft Azure AI Fundamentals (AI-900) Exam gives the opportunity to demonstrate your knowledge of common ML and AI workloads and how to implement them on Azure. The Azure AI Fundamentals exam can also 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.


Who should take the exam?

Microsoft Azure AI Fundamentals (AI-900) Exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required. However, it is suggested to have some general programming knowledge or experience. This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.


Skills Acquired

The Microsoft Azure AI Fundamentals (AI-900) Exam helps to prove your skills and expertise in the following domains -

  • 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
  • Features of conversational AI workloads on Azure.


Course Outline

The Microsoft Azure AI Fundamentals (AI-900) Exam covers latest exam topics -

Domain 1 - 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


Domain 2 - 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 Wizard UI
  • azure Machine Learning designer


Domain 3 - 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


Domain 4 - 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 Text Translator service


Domain 5 - 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 the capabilities of the Azure Bot Service


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What are our Practice Exams?

  • Practice exams have been designed by professionals and domain experts that simulate exam scenario.
  • Practice exam questions have been created on the basis of content outlined in the official documentation.
  • Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
  • Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
  • You can also create your own practice exam based on your choice and preference 


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