Microsoft Azure AI Fundamentals AI-900 Exam Guide

Overview

This course provides a clear understanding of core AI concepts, Microsoft Azure AI services, and the essential strategies behind responsible and effective AI implementation. Learners will gain foundational knowledge that is increasingly valuable across technology, business, and data-driven roles.

By exploring real Azure tools and services, you will learn how to apply AI workloads, understand machine learning fundamentals, and recognize key use cases in computer vision, natural language processing, and generative AI. This course equips you with the practical insights needed to confidently prepare for the AI-900 certification and apply your skills professionally.

The curriculum blends theory with hands-on examples, providing both conceptual clarity and demonstrations of Azure Machine Learning, Azure Cognitive Services, and Azure OpenAI Service. You’ll see how real-world AI solutions are designed, deployed, and evaluated.

This course is ideal for beginners, aspiring AI practitioners, IT professionals, and business users looking to understand AI on Azure. No prior coding or data science experience is required.

Syllabus

• Identify Features of Common AI Workloads
In this section, we explore key features of Azure AI workloads, including data monitoring, content moderation, computer vision, and NLP, emphasizing their practical applications in business solutions.
• Identify the Guiding Principles for Responsible AI
In this section, we cover key considerations for responsible AI development, including accountability, inclusiveness, and reliability.
• Identify Common Machine Learning Techniques
In this section, we explore regression, classification, and clustering machine learning techniques, focusing on their practical applications and evaluation metrics for data analysis and prediction.
• Describe Core Machine Learning Concepts
In this section, we explore features, labels, and data preparation in machine learning, focusing on training and validation datasets and their roles in model development.
• Describe Azure Machine Learning Capabilities
In this section, we explore Azure ML capabilities, including AutoML, data services, and model deployment.
• Identify Common Types of Computer Vision Solutions
In this section, we cover computer vision solutions, including image, object, and facial analysis features.
• Identify Azure Tools and Services for Computer Vision Tasks
In this section, we explore Azure AI Vision, Face, and Video Indexer services for image analysis, facial recognition, and video insights, emphasizing their pre-built and customizable machine learning capabilities.
• Identify Features of Common NLP Workload Scenarios
In this section, we explore NLP workload features like key phrase extraction, entity recognition, and sentiment analysis with practical applications.
• Identify Azure Tools and Services for NLP Workloads
In this section, we explore Azure AI services for NLP tasks, focusing on language analysis, speech processing, and translation capabilities to support application development with text, speech, and multilingual content.
• Identify Features of Generative AI Solutions
In this section, we cover Generative AI features, ethical considerations, and real-world applications.
• Identify Capabilities of Azure OpenAI Service
In this section, we cover Azure OpenAI Service's natural language, code, and image generation capabilities for AI applications in Azure.

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Taught by

Packt - Course Instructors

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Subjects

Programming