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Microsoft Exam AI-900 Questions Answers Test Simulator

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AI-900 Exam Topics

Describe Artificial Intelligence workloads and considerations 15-20%

Identify features of common AI workloads

  • identify prediction/forecastingworkloads
  • 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 areused 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

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