AI-900 Exam Study Guide (Microsoft Azure AI Fundamentals)
You can access AI-900 Exam Microsoft official page here
Preparing for the AI-900 Microsoft Azure AI Fundamentals Certificate exam? Don’t know where to start? This post is the AI-900 Certificate Study Guide (with links to each exam objective).
I have curated a list of articles from Microsoft documentation for each objective of the AI-900 exam. I hope this article will help you to prepare for the AI-900 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.
Skills Measured
- Describe Artificial Intelligence Workloads and Considerations (15-20%)
- Describe Fundamental Principles of Machine Learning on Azure (30-35%)
- Describe Features of Computer Vision Workloads on Azure (15-20%)
- Describe Features of Natural Language Processing (NLP) Workloads on Azure (15-20%)
- Describe Features of Conversational AI Workloads on Azure (15-20%)
AI-900 Exam Free Study Guide
For each test objective, I’ve compiled a thorough collection of articles from Microsoft documentation. Please feel free to forward this page to your friends and coworkers in order to assist them in preparing for the test .
Describe Artificial Intelligence Workloads and Considerations (15-20%)
Identify Features of Common AI Workloads
- Identify prediction/forecasting workloads
- Demand Forecasting
- Demand forecasting & price optimization
- Demand forecasting for shipping and distribution
- Personalized offers
- Identify features of anomaly detection workloads
- Anomaly detector
- Identify computer vision workloads
- Applying content tags to images
- Detect common objects in images
- Detect popular brands in images
- Categorize images by subject matter
- Describe images with human-readable language
- Identify natural language processing or knowledge mining workloads
- Choosing a natural language processing technology in Azure
- Identify conversational AI workloads
- Conversational AI tools: To build, connect & manage intelligent bots
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
- Linear Regression
- Identify classification machine learning scenarios
- Classification modules
- Identify clustering machine learning scenarios
- Clustering modules
Describe Core Machine Learning Concepts
- Identify features and labels in a dataset for machine learning
- Framing: Key ML Terminology
- Describe how training and validation datasets are used in machine learning
- About train, validation & test sets in Machine learning
- Describe how machine learning algorithms are used for model training
- Which machine learning algorithm should I use?
- Select and interpret model evaluation metrics for classification and regression
- Metrics for Classification models
- Metrics for Regression models
Identify Core Tasks in Creating a Machine Learning Solution
- Describe common features of data ingestion and preparation
- AI Workflow: Data ingestion
- Data Preparation for Machine Learning
- Describe feature engineering and selection
- Beginner’s guide to feature selection in Python
- Representation: Feature Engineering
- Describe common features of model training and evaluation
- Model Training
- Evaluate model
- Introduction to Machine learning model evaluation
- Describe common features of model deployment and management
- Deploy real-time machine learning services with Azure Machine Learning
- Machine Learning Operations (MLOps)
Describe Capabilities of No-Code Machine Learning with Azure Machine Learning Studio
- Automated ML Wizard UI
- What is automated machine learning (AutoML)?
- Azure Machine Learning designer
- What is an 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
- Train image classification models with MNIST data & scikit-learn
- Identify features of object detection solutions
- Detect common objects in images
- Identify features of optical character recognition solutions
- Optical Character Recognition (OCR)
- Identify features of facial detection, recognition, and analysis solutions
- Face detection and attributes
- Face recognition concepts
- How to analyze videos in real-time?
Identify Azure Tools and Services for Computer Vision Tasks
- Identify capabilities of the Computer Vision service
- What is Computer Vision?
- Identify the capabilities of the Custom Vision service
- What is Custom Vision?
- Identify capabilities of the Face service
- What is the Azure Face service?
- Identify capabilities of the Form Recognizer service
- What is Form Recognizer?
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
- How to extract key phrases using Text Analytics?
- Identify features and uses for entity recognition
- Entity recognition cognitive skill
- Identify features and uses for sentiment analysis
- Sentiment analysis with cognitive service & Azure
- Identify features and uses for language modeling
- MSRLM: A scalable language modeling toolkit
- Identify features and uses for speech recognition and synthesis
- What is Speech service?
- Get started with speech-to-text
- Identify features and uses for translation
- Translator app features
Identify Azure Tools and Services for NLP Workloads
- Identify the capabilities of the Text Analytics service
- What is the Text Analytics API?
- Identify the capabilities of the Language Understanding Service (LUIS)
- What is Language Understanding (LUIS)?
- Identify the capabilities of the Speech service
- What is Speech service?
- Identify capabilities of the Text Translator service
- Translator: Value of Translator
Describe Features of Conversational AI Workloads on Azure (15-20%)
Identify Common Use Cases for Conversational AI
- Identify features and uses for webchat bots
- Web Chat overview
- Identify common characteristics of conversational AI solutions
- Microsoft Conversational AI tools
Identify Azure Services for Conversational AI
- Identify capabilities of the QnA Maker service
- What is QnA Maker?
- Identify capabilities of the Azure Bot Service
- Azure Bot Service overview
This brings us to the end of the AI-900 Azure AI Fundamentals Study Guide.
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