Exam AI-200 Developing AI Cloud Solutions on Azure (Video Course)

Description

Master the essential skills and knowledge to become a certified Azure AI Cloud Developer Associate with this comprehensive, hands-on exam preparation course. 

Overview:

The AI-200: AI Cloud Solutions Developer Exam Prep course is designed to empower professionals with the latest knowledge and practical skills required to excel in the rapidly evolving landscape of AI development. Organizations are increasingly embedding AI capabilities into their applications and workflows, creating a growing demand for developers who can build, integrate, and optimize intelligent solutions at scale. This course provides an in-depth exploration of AI development concepts, tools, and best practices, ensuring learners are well-prepared for certification and real-world challenges.

Through engaging lectures and demonstrations, learners will gain a comprehensive understanding of how to develop and integrate AI solutions using the latest frameworks and platforms. By the end of the course, participants will have the confidence and expertise to pass the AI-200 certification exam and make impactful contributions to their organizations as AI Solutions Developers.

About the Instructor:

Tiago Costa is an AI & Cloud Architect and Advisor, and a public speaker for the AI and Microsoft Cloud. For the past few years, he has been architecting and developing solutions using Microsoft Azure & AI for some Fortune 500 companies. Due to his extensive real-world experience, Tiago regularly teaches AI and Microsoft Azure classes worldwide. .

Skill Level:

• Beginner
• Intermediate

Learn How To:

• Develop containerized solutions on Azure
• Develop AI solutions by using Azure data management services
• Connect to and consume Azure services
• Secure, monitor, troubleshoot Azure solutions

Course requirement:

• Azure SDKs and third-party SDKs used in Azure.
• Azure data management services.
• Azure monitoring and troubleshooting.
• Azure messaging and eventing.
• Vector databases.
• Python programming.
• Implementing containerized applications on Azure.

Who Should Take This Course:

• Software developers and engineers looking to build and integrate AI-powered applications using Microsoft Azure AI services
• Developers transitioning from traditional application development into AI-driven solution development
• Professionals preparing to validate their skills with the AI-200 certification
• Cloud architects and solutions architects who want to deepen their understanding of AI development patterns and integration
• Microsoft partners and consultants who need to demonstrate AI development expertise to clients

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, Prentice Hall, 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.

Sample Content

Table of Contents

Course Introduction

Module 1: Develop Containerized Solutions on Azure

Lesson 1: Implement Container Application Hosting

1.1 Build, store, version, and manage container images by using Azure Container Registry

1.2 Build and run images by using Azure Container Registry Tasks

1.3 Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets

Lesson 2: Implement Container-Orchestrated Solutions

2.1 Deploy applications to Azure Container Apps, including environment configuration and revision management

2.2 Implement event-driven scaling by using Kubernetes Eventdriven Autoscaling (KEDA) in Container Apps

2.3 Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files

2.4 Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity

Module 2: Develop AI Solutions by Using Azure Data Management Services

Lesson 3: Develop AI Solutions by Using Azure Cosmos DB for NoSQL

3.1 Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries

3.2 Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels

3.3 Store and retrieve embeddings and execute vector similarity search for semantic retrieval

3.4 Implement a change feed processor to detect and handle new or updated items

Lesson 4: Develop AI Solutions by Using Azure Database for PostgreSQL

4.1 Connect and query Azure Database for PostgreSQL by using SDKs

4.2 Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types

4.3 Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead

4.4 Configure compute, memory, and storage resources to support vector workloads

4.5 Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter

4.6 Implement connection optimization to improve throughput and minimize latency

Lesson 5: Integrate Azure Managed Redis in AI Solutions

5.1 Implement Azure Managed Redis data operations, including caching, expiration, and invalidation

5.2 Implement vector indexing to enable similarity search

Module 3: Connect to and Consume Azure Services

Lesson 6: Develop Event- and Message-Based AI Solutions

6.1 Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions

6.2 Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries

Lesson 7: Develop and Implement Azure Functions

7.1 Build serverless APIs, including implementing triggers and bindings

7.2 Configure and deploy function apps

Module 4: Secure, Monitor, and Troubleshoot Azure Solutions

Lesson 8: Implement Secure Azure Solutions

8.1 Secure secrets by using Azure Key Vault, including rotation and retrieval

8.2 Store and retrieve app configuration information by using Azure App Configuration

Lesson 9: Monitor and Troubleshoot Azure Solutions

9.1 Trace distributed systems by using OpenTelemetry SDKs

9.2 Write KQL queries to analyze logs and metrics

Updates

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