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