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
Who this course is for:
- Managers, Azure Data Science Professional and student
Software Development Manager
MCA || ADCA || DAC || LLB
▪ 17+ years of experience in software development and worked on various job profile like software engineer, team lead and now working as a software development manager.
▪ Experience in full stack development (Web, Desktop) using Microsoft, Mobile App development using Xamarin, Native iOS and Native Android.
▪ OUTSYSTEMS SOLUTIONS ARCHITECTURE.
▪ MANAGING PROJECT INVOVLING .NET, MEAN, PYTHON, NATIVE (ANDROID, IOS) AND HYBRID(XAMARIN) MOBILE DEVELOPMENT.
▪ MS OFFICE AUTOMATION.
▪ AGILE BASED DEVELOPMENT & DELIVERY.
▪ ARCHITECTURE, DESIGN AND IMPLEMENTATION OF END-TOEND SOLUTION.
▪ CHANGE MANAGEMENT.
▪ LOW CODE DEVELOPMENT USING OUTSYSTEMS, ORACLE APEX PLATFORM