Exam AI-900: Microsoft Azure AI Fundamentals, 2nd Ed. (Video), 2nd Edition

Prove your knowledge of artificial intelligence and machine learning within Microsoft Azure with this leading-edge certification badge.

Exam AI-900: Microsoft Azure AI Fundamentals, 2nd Ed. reflects the latest AI-900 exam changes, ensuring learners are up to date. The exam encompasses revised topics on AI workloads, machine learning, natural language processing, computer vision, and cognitive services. Passing this exam demonstrates a candidate's solid understanding of AI technologies and their capabilities to leverage Azure AI services to architect and implement intelligent solutions. This is a key exam for Microsoft because of AI, and because it is a fundamentals-level exam (which typically has the broadest audience and reach). Test takers for that exam will increase given the burst of AI in the market and companies starting to take it seriously. This certification exam is the gateway for individuals aiming to prove their knowledge and skills in artificial intelligence and Microsoft Azure services.

Tim Warner caters to diverse learning styles, combining lectures, demos, and hands-on exercises. The curriculum, crafted by Tim Warner, a seasoned expert in AI and Microsoft Azure, promises an engaging, insightful, and comprehensive exploration of the AI-900 exam topics. Warner's unique delivery--known for its clarity, engagement, and depth--ensures learners not only prepare for the exam but also apply their knowledge practically.

The updated AI-900 exam and, by extension, our course delve into AI's transformative role across sectors. We explore machine learning's predictive powers, natural language processing's communication breakthroughs, computer vision's perceptive capabilities, and more, through Azure's comprehensive suite of AI services. Real-world applications are highlighted, showing AI's impact on improving efficiency, decision-making, and innovation.

Skill Level:

• Beginner to Intermediate

Learn How To:

• Understand and apply the features of AI workloads including generative AI
• Grasp guiding principles for responsible AI
• Explore machine learning techniques and Azure Machine Learning capabilities
• Navigate computer vision solutions with Azure AI Vision and Face detection services
• Implement NLP workload scenarios using Azure AI Language and Speech services
• Utilize generative AI solutions through Azure OpenAI Service

Course requirement:

Pre-requisites:

• The candidate should have a basic understanding of machine learning concepts and the ability to use Azure software/tech.

Who Should Take This Course:

Job titles:

• Developer, Engineer, Business Analyst, Data Analyst, IT Pro.
• This certification exam is the gateway for individuals aiming to prove their knowledge and skills in artificial intelligence and Microsoft Azure services.

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of Contents

Introduction

Lesson 1: Identify Features of Common AI Workloads

1.1 Identify features of content moderation and personalization workloads

1.2 Identify computer vision workloads

1.3 Identify natural language processing workloads

1.4 Identify knowledge mining workloads

1.5 Identify document intelligence workloads

1.6 Identify features of generative AI workloads

Lesson 2: Identify Guiding Principles for Responsible AI

2.1 Describe considerations for fairness in an AI solution

2.2 Describe considerations for reliability and safety in an AI solution

2.3 Describe considerations for privacy and security in an AI solution

2.4 Describe considerations for inclusiveness in an AI solution

2.5 Describe considerations for transparency in an AI solution

2.6 Describe considerations for accountability in an AI solution

Lesson 3: Identify Common Machine Learning Techniques

3.1 Identify regression machine learning scenarios

3.2 Identify classification machine learning scenarios

3.3 Identify clustering machine learning scenarios

3.4 Identify features of deep learning techniques

Lesson 4: Describe Azure Machine Learning Capabilities

4.1 Identify features and labels in a dataset for machine learning

4.2 Describe how training and validation datasets are used in machine learning

4.3 Describe capabilities of automated machine learning

4.4 Describe data and compute services for data science and machine learning

4.5 Describe model management and deployment capabilities in Azure Machine Learning

Lesson 5: Identify Common Types of Computer Vision Solutions

5.1 Identify features of image classification solutions

5.2 Identify features of object detection solutions

5.3 Identify features of optical character recognition solutions

5.4 Identify features of facial detection and facial analysis solutions

5.5 Describe capabilities of the Azure AI Vision service

5.6 Describe capabilities of the Azure AI Face detection service

Lesson 6: Identify Features of Common NLP Workload Scenarios

6.1 Identify features and uses for key phrase extraction

6.2 Identify features and uses for entity recognition

6.3 Identify features and uses for sentiment analysis

6.4 Identify features and uses for language modeling

6.5 Identify features and uses for speech recognition and synthesis

6.6 Identify features and uses for translation

6.7 Describe capabilities of the Azure AI language service

6.8 Describe capabilities of the Azure AI speech service

Lesson 7: Identify Features of Generative AI Solutions

7.1 Identify features of generative AI models

7.2 Identify common scenarios for generative AI

7.3 Identify responsible AI considerations for generative AI

Lesson 8: Identify Capabilities of Azure OpenAI Service

8.1 Describe natural language generation capabilities of Azure OpenAI Service

8.2 Describe code generation capabilities of Azure OpenAI Service

8.3 Describe image generation capabilities of Azure OpenAI Service

Summary

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