AI 900: Microsoft Azure AI Fundamentals exam Practice Sets

This practice sets help professional, students to be pass the Microsoft Certification Exam AI 900 Microsoft Azure AI Fundamentals. This practice set has been designed based on latest/revised syllabus of AI-900: Microsoft Azure AI Fundamentals.

With the help of this practice sets, professional, students will be experts in the following skill sets

• Describe AI workloads and considerations

• Describe fundamental principles of machine learning on Azure

• Describe features of computer vision workloads on Azure

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

• Describe features of conversational AI workloads on Azure

Following topics/sub topics question covered in this practice sets so I would like to request you that before attempting this practice sets please go through each modules and its sub section.

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 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 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 servic