Microsoft Azure AI Fundamentals (AI-900) Cert Prep by Microsoft Press

Introduction

• Introduction

1. Identify Features of Common AI Workloads

• Learning objectives
• Identify features of content moderation and personalization workloads
• Identify computer vision workloads
• Identify natural language processing workloads
• Identify knowledge mining workloads
• Identify document intelligence workloads
• Identify features of generative AI workloads

2. Identify Guiding Principles for Responsible AI

• Learning objectives
• 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

3. Identify Common Machine Learning Techniques

• Learning objectives
• Identify regression machine learning scenarios
• Identify classification machine learning scenarios
• Identify clustering machine learning scenarios
• Identify features of deep learning techniques

4. Describe Azure Machine Learning Capabilities

• Learning objectives
• Identify features and labels in a dataset for machine learning
• Describe how training and validation datasets are used in machine learning
• Describe capabilities of automated machine learning
• Describe data and compute services for data science and machine learning
• Describe model management and deployment capabilities in Azure Machine Learning

5. Identify Common Types of Computer Vision Solutions

• Learning objectives
• Identify features of image classification solutions
• Identify features of object detection solutions
• Identify features of optical character recognition solutions
• Identify features of facial detection and facial analysis solutions
• Describe capabilities of the Azure AI Vision service
• Describe capabilities of the Azure AI Face detection service

6. Identify Features of Common NLP Workload Scenarios

• Learning objectives
• 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
• Describe capabilities of the Azure AI language service
• Describe capabilities of the Azure AI speech service

7. Identify Features of Generative AI Solutions

• Learning objectives
• Identify features of generative AI models
• Identify common scenarios for generative AI
• Identify responsible AI considerations for generative AI

8. Identify Capabilities of Azure OpenAI Service

• Learning objectives
• Describe natural language generation capabilities of Azure OpenAI Service
• Describe code generation capabilities of Azure OpenAI Service
• Describe image generation capabilities of Azure OpenAI Service

Summary

• Next steps