Microsoft Exam AI-900 Questions Answers Test Simulator

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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