Microsoft Certified: Azure AI Fundamentals

This certification demonstrates that the recipient has knowledge of Azure AI Fundamentals, can
describe artificial intelligence workloads and considerations, knows fundamental principles of
machine learning on Azure, knows features of computer vision workloads on Azure and can describe
features of conversational AI workloads on Azure.

Why Take The Microsoft Certified: Azure AI Fundamentals AI-900 Exam?

The need AI tech professionals is going to increase dramatically in the near future and passing the
exam will help you secure an excellent position in the industry.

Increase Your Salary:

• The average salary for someone who holds a Microsoft Certified: Azure AI Fundamentals
  certification is around $164,500.

Prepare For The Future:

• You will also be prepared for a role as an AI Engineer Data scientist, Developer or Solutions
  Architect.
• Having an Azure AI Fundamentals certification is a great way to prepare for other Azure role-
  based or specialty certifications.

Abilities Validated By The Certification:

• 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

Recommended Knowledge & Experience:

• Ability to demonstrate knowledge of core data concepts not unlike relational and
  non-relational data, types of workloads like transactional or analytical and how they
  are actualized using Azure data services.
• Candidates for the Azure AI Fundamentals certification should have foundational knowledge
  of core data concepts and how they are implemented using Microsoft Azure data services.

Exam Topics & Scoring:

Microsoft Certified: Azure AI Fundamentals AI-900 Exam

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 common features of feature selection and engineering
• 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

• automated Machine Learning tool
• 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, recognition, and 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

• 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 Intelligence Service (LUIS)
• identify capabilities of the Speech service
• identify capabilities of the Text Translator 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 Azure services for conversational AI

• identify capabilities of the QnA Maker service
• identify capabilities of the Bot Framework

Prepare for your exam:

The best way to prepare is with first-hand experience. Taking advantage of the opportunities that
Phoenix TS provides will assist you with gathering all the knowledge and skills you’ll need for
certification.

Phoenix TS Microsoft Certified: Azure AI Fundamentals – Learning Pathways