Training plan
Develop AI cloud solutions on Azure (AI-200T00)
Module 1: Implement container application hosting on Azure
- Store and manage containers in Azure Container Registry
- Deploy containers to Azure App Service
Module 2: Deploy and manage apps on Azure Container Apps
- Deploy containers to Azure Container Apps
- Manage containers in Azure Container Apps
- Scale containers in Azure Container Apps
Module 3: Deploy and monitor applications on Azure Kubernetes Service
- Deploy applications to Azure Kubernetes Service
- Configure applications on Azure Kubernetes Service
- Monitor and troubleshoot applications on Azure Kubernetes Service
Module 4: Develop AI solutions with Azure Cosmos DB for NoSQL
- Build queries for Azure Cosmos DB for NoSQL
- Implement vector search on Azure Cosmos DB for NoSQL
- Optimize query performance for Azure Cosmos DB for NoSQL
Module 5: Develop AI solutions with Azure Database for PostgreSQL
- Build and query with Azure Database for PostgreSQL
- Implement vector search with Azure Database for PostgreSQL
- Optimize vector search in Azure Database for PostgreSQL
Module 6: Enhance AI solutions with Azure Managed Redis
- Implement data operations in Azure Managed Redis
- Implement event messaging with Azure Managed Redis
- Implement vector storage in Azure Managed Redis
Module 7: Integrate backend services for AI solutions
- Queue and process AI operations with Azure Service Bus
- Develop event-driven AI workflows with Azure Event Grid
- Build serverless AI backends with Azure Functions
Module 8: Manage application secrets and configuration for AI solutions
- Manage application secrets with Azure Key Vault
- Manage application settings with Azure App Configuration
Module 9: Observe and troubleshoot apps on Azure
- Instrument an app with OpenTelemetry
- Analyze app telemetry with logs and metrics
Design and Implement Microsoft DevOps solutions (AZ-400T00)
Module 1: Development for enterprise DevOps
- Introduction to DevOps
- Plan Agile with GitHub Projects and Azure Boards
- Design and implement branch strategies and workflows
- Collaborate with pull requests in Azure Repos
- Explore Git hooks
- Plan foster inner source
- Manage and configure repositories
- Identify technical debt
Module 2: Implement CI with Azure Pipelines and GitHub Actions
- Explore Azure Pipelines
- Manage Azure Pipeline agents and pools
- Describe pipelines and concurrency
- Design and implement a pipeline strategy
- Integrate with Azure Pipelines
- Introduction to GitHub Actions
- Learn continuous integration with GitHub Actions
- Design a container build strategy
Module 3: Design and implement a release strategy
- Create a release pipeline
- Explore release recommendations
- Provision and test environments
- Manage and modularize tasks and templates
- Automate inspection of health
Module 4: Implement a secure continuous deployment using Azure Pipelines
- Introduction to deployment patterns
- Implement blue-green deployment and feature toggles
- Implement canary releases and dark launching
- Implement A/B testing and progressive exposure deployment
- Integrate with identity management systems
- Manage application configuration data
Module 5: Manage infrastructure as code using Azure and DSC
- Explore infrastructure as code and configuration management
- Create Azure resources using Azure Resource Manager templates
- Create Azure resources using Azure CLI
- Explore Azure Automation with DevOps
- Implement Desired State Configuration (DSC)
- Implement Bicep
Module 6: Implement security and validate code bases for compliance
- Introduction to Secure DevOps
- Implement open-source software
- Software Composition Analysis
- Security Monitoring and Governance
Module 7: Design and implement a dependency management strategy
- Explore package dependencies
- Understand package management
- Migrate consolidate and secure artifacts
- Implement a versioning strategy
- Introduction to GitHub Packages
Module 8: Implement continuous feedback
- Implement tools to track usage and flow
- Develop monitor and status dashboards
- Share knowledge within teams
- Design processes to automate application analytics
- Manage alerts, blameless retrospectives and a just culture
Recommended prerequisite knowledge
Students must have 1 to 2 years of professional development experience and hands-on experience with Microsoft Azure. They must be able to program in an Azure-supported language (C#, JavaScript, Python, or Java) and understand the basics of HTTP and REST APIs.
A solid understanding of version control (Git), Agile methodologies, and key principles of software development and production deployment is recommended. Familiarity with modern architectures (serverless, containers) and application security fundamentals (authentication/authorization, secrets management) is an asset, particularly for the AI-200 component.
Credentials and certification
Exams features
- Codes: AI-200 and AZ-400
- Titles: Developing AI Cloud Solutions on Azure and Design and Implement Microsoft DevOps solutions
- Duration: 100 minutes for AI-200 and 150 minutes for AZ-400
- Number of Questions: 40 to 60
- Question Format: Multiple choice, multiple response, scenario-based
- Passing Score: 700 out of 1000
- Cost: $0 (included in your training)
Exams topics
- Develop AI cloud solutions on Azure
- Deploy and run AI applications with compute and containerization models (Azure Container Apps, AKS)
- Design and use data services adapted to AI workloads (Cosmos DB for NoSQL, PostgreSQL with pgvector, Azure Managed Redis)
- Integrate and orchestrate services via event-driven and message-oriented architectures (Event Grid, Service Bus)
- Secure, observe, monitor, and troubleshoot AI applications on Azure (secrets, configuration, monitoring, diagnostics)
- Configure processes and communications
- Design and implement source code control
- Design and implement build and release pipelines Develop a security and compliance plan
- Develop an instrumentation strategy (monitoring, logging, tracing, and observability)
Flexible Certification Pathway Options
Multiple Routes to Microsoft Certified: DevOps Engineer Expert Certification
The Microsoft Certified: DevOps Engineer Expert certification offers flexible pathways to accommodate different profiles and experiences. While our recommended pathway combines AI-200 (Develop AI cloud solutions on Azure) with AZ-400 (Designing and Implementing Microsoft DevOps Solutions), Microsoft also recognizes other Associate certifications as a starting point for accessing the Expert pathway.
Choose Your Foundation Certification
-
Option 1: AI-200 (recommended path)
-
Option 2: AZ-104
- Focus on developing AI-driven applications and modern cloud solutions on Azure
- Ideal for back-end developers designing serverless architectures
- Coverage of key services for AI workloads: hosting, data, security, and observability
- Perfect for IT professionals with infrastructure management experience
- Covers Azure administration, resource management, and governance
- Strong foundation for professionals transitioning from operations to DevOps
- Explore AZ-104 Azure Administrator certification training
Your Existing Certification Counts
Already certified? If you hold either the AI-200 (Azure AI Cloud Developer Associate) or AZ-104 (Azure Administrator Associate) certification, you can proceed directly to AZ-400 (Designing and Implementing Microsoft DevOps Solutions) to obtain your Expert-level credential. This flexible approach recognizes your existing expertise while building upon your specialized knowledge.
Complete Expert Certification
Regardless of your chosen foundation, completing AZ-400 (Designing and Implementing Microsoft DevOps Solutions) will earn you the prestigious Microsoft Certified: DevOps Engineer Expert certification, validating your comprehensive skills in DevOps practices, continuous integration/continuous deployment (CI/CD), and Azure infrastructure management.
Ready to advance your career? Contact us to discuss which pathway aligns best with your professional goals and current expertise.
Eccentrix Corner Articles: DevOps Engineer Expert AI200-AZ400 Resources
Explore our technical articles on DevOps Engineer Expert AI200-AZ400 published on Eccentrix Corner. These resources dive deeper into key concepts, share best practices, and provide practical guides to maximize your learning and certification success. Our experts share real-world insights to help you master DevOps engineering on Microsoft Azure.
Azure DevOps Engineer Training
The Azure DevOps Engineer Expert training program combines two key courses to provide participants with a comprehensive skill set in modern application development and DevOps practices. This program includes AI-200: Develop AI cloud solutions on Azure and AZ-400: Designing and Implementing Microsoft DevOps Solutions, fostering a deep understanding of designing, deploying, and operating cloud applications—including AI-driven solutions—with continuous integration and continuous delivery pipelines.
This training is ideal for developers and IT professionals who want to earn the Microsoft Certified: DevOps Engineer Expert certification and excel at implementing DevOps practices (CI/CD, source control, security, compliance, instrumentation, and observability) for cloud-native applications at scale.
Why Choose the DevOps Engineer Expert Training Bundle?
DevOps practices are essential for delivering high-quality applications efficiently and consistently. This program equips participants with the skills needed to optimize development workflows, automate infrastructure, and ensure seamless deployments in Azure environments. By earning the DevOps Engineer Expert certification, you validate your ability to build scalable solutions and effectively manage development pipelines.
This training combines hands-on development on Azure with advanced DevOps methodologies, preparing you for the AI-200 and AZ-400 exams as well as real-world challenges.
Key Skills Developed in the Training
Develop modern cloud applications, including AI-driven solutions
Learn to design and implement applications on Azure using serverless and containerized models (Azure Functions, Azure Container Apps, AKS), and to integrate services via event-driven and message-oriented architectures (Event Grid, Service Bus).Design data layers adapted to AI workloads
Develop solutions with Azure Cosmos DB for NoSQL, Azure Database for PostgreSQL (pgvector), and Azure Managed Redis (caching, streaming, vector search) to support high-performance and reliable AI scenarios.Set up CI/CD pipelines and automate delivery
Master the design and implementation of build and deployment pipelines, test automation, and source code management (Git) with DevOps tools.Implement DevOps Practices and Improve Collaboration
Understand the principles of DevOps culture and apply them to streamline collaboration, standardize processes, and accelerate delivery cycles.Secure and Govern Environments and Deployments
Apply security best practices: secrets and configuration management, authentication/authorization, compliance, and safeguards for reliable deployments at scale.Observe, Monitor, and Troubleshoot in Production
Develop an observability approach (monitoring, logs, traces, diagnostics) to detect incidents, troubleshoot quickly, and continuously improve application performance and resilience.- Design Scalable and Resilient Architectures
Learn to build solutions that can scale, remain available, and meet business requirements (scalability, reliability, resilience), while remaining maintainable and measurable in operation.
Comprehensive, Instructor-Led Training
This training bundle is delivered by Microsoft-certified experts who provide real-world scenarios and practical exercises. Participants benefit from live interactions, hands-on labs, and detailed guidance, ensuring they are well-prepared for certification exams and workplace applications.
Who Should Attend?
This training is ideal for:
- Developers looking to transition into DevOps roles and master the delivery of modern cloud applications, including AI-driven solutions on Azure
- IT professionals seeking hands-on expertise in continuous integration and continuous deployment (CI/CD), source code control, security, compliance, and observability
- Azure administrators who want to expand their skills into development (serverless, containers) and DevOps practices
- Teams and organizations that want to standardize and industrialize their DevOps practices on Azure (automation, governance, monitoring, reliability) to accelerate deployment while reducing risk
Elevate Your Skills with Comprehensive DevOps Training
The Microsoft Certified: DevOps Engineer Expert (AI-200 + AZ-400) training bundle equips you with the skills to develop and deploy cloud-native applications efficiently. Enroll today to achieve your certification and advance your career in DevOps.
AI-200 Exam Success Strategies
For the required AI-200 exam success strategies, please consult the dedicated page for this course before continuing with the expert-level tips.
AZ-400 Exam Success Strategies
Mastering the AZ-400 certification is the final step toward achieving the DevOps Engineer Expert credential. Strategic preparation, effective time management, and optimal mental performance are equally crucial for success.
AZ-400 Exam Statistics & Success Rates
- Average Pass Rate: 55-60% on first attempt (Microsoft Expert level average)
- Most Common Score Range: 710-760 for passing candidates
- Average Study Time: 8-10 weeks for experienced DevOps engineers
- Retake Rate: 35-40% of candidates require a second attempt
- Top Failure Areas: Implement and manage build infrastructure (41%), Design and implement a release strategy (38%), Develop a security and compliance plan (36%)
Study Method Comparison
| Study Approach | Duration | Pass rate | Best For |
|---|---|---|---|
|
Hands-on Practice Only |
6-7 weeks |
40-50% |
Experienced DevOps engineers |
|
Documentation + Practice |
8-9 weeks |
60-65% |
Methodical learners |
|
Training + Labs + Practice |
8-10 weeks |
75-80% |
Comprehensive preparation |
|
Practice Tests Only |
3-4 weeks |
30-40% |
Not recommended |
Strategic Study Approach
- Create an 8-10 week study timeline – Expert-level certifications require substantial preparation time
- Follow the 70-20-10 rule – 70% hands-on practice with Azure DevOps, GitHub, and CI/CD pipelines, 20% reading documentation, 10% practice tests
- Focus on scenario-based learning – AZ-400 emphasizes real-world DevOps implementation over memorization
- Study in 90-minute focused blocks with 15-minute breaks to maximize retention
Common Exam Pitfalls to Avoid
- Don’t confuse Azure DevOps vs. GitHub – understand when to use Azure Repos vs. GitHub, Azure Pipelines vs. GitHub Actions
- CI/CD pipeline stages – know the differences between build, release, deployment, and approval stages
- Source control strategies – distinguish between Git flow, GitHub flow, trunk-based development, and feature branches
- Testing types and placement – understand unit tests, integration tests, load tests, and when to run each in the pipeline
- Security scanning tools – know when to use dependency scanning, container scanning, static code analysis, and secret scanning
- Deployment strategies – distinguish between blue-green, canary, rolling, and feature flags
Topic Weight Distribution
| Exam Domain | Weight | Focus Areas | Priority |
|---|---|---|---|
|
Develop an Instrumentation Strategy |
5-10% |
Logging, monitoring, telemetry, Application Insights |
Medium |
|
Develop a Site Reliability Engineering Strategy |
5-10% |
Incident response, blameless postmortems, chaos engineering |
Medium |
|
Develop a Security and Compliance Plan |
10-15% |
Secure DevOps, secrets management, compliance automation |
High |
|
Manage Source Control |
10-15% |
Git strategies, branch policies, code reviews |
High |
|
Facilitate Communication and Collaboration |
10-15% |
Work item tracking, dashboards, team collaboration |
High |
|
Define and Implement Continuous Integration |
20-25% |
Build pipelines, package management, code quality |
Critical |
|
Define and Implement Continuous Delivery |
10-15% |
Release pipelines, deployment patterns, infrastructure as code |
High |
Exam Day Time Management
- Allocate 2 minutes per question on average – expert exams have more complex scenarios
- Read case studies completely first before attempting related questions
- Flag uncertain questions and return to them – don’t get stuck on difficult pipeline configurations
- Reserve 20 minutes at the end for reviewing flagged questions and checking answers
Managing Exam Stress & Performance
- Get 7-8 hours of quality sleep the night before – avoid last-minute cramming
- Arrive 30 minutes early to settle in and complete check-in procedures calmly
- Use deep breathing techniques if you feel overwhelmed during the exam
- Trust your DevOps experience – your first instinct is usually correct on implementation questions
Technical Preparation Tips
- Practice building complete CI/CD pipelines – work through end-to-end scenarios from source control to production deployment
- Master Azure Pipelines YAML syntax – understand triggers, stages, jobs, steps, variables, and templates
- Understand GitHub Actions workflows – know how to configure workflows, actions, and secrets
- Review infrastructure as code – practice with ARM templates, Bicep, and Terraform for Azure deployments
Final Week Preparation
- Take 2-3 practice exams to identify knowledge gaps and build confidence
- Review Microsoft’s official exam objectives one final time
- Avoid learning new concepts – focus on reinforcing DevOps patterns you already know
- Prepare your exam day logistics – route to test center, required identification, arrival time
Mental Preparation Strategies
- Visualize success scenarios – imagine yourself confidently configuring pipelines and deployments
- Remind yourself of your DevOps experience – you’ve likely implemented CI/CD pipelines before
- Stay positive during difficult questions – every candidate faces challenging DevOps scenarios
- Remember that 700/1000 passes – you don’t need perfection, just solid DevOps implementation skills
How to Schedule Your AZ-400 Exam
- Official Testing Provider: Pearson VUE is Microsoft’s authorized testing partner for AZ-400
- Scheduling Process: Create a Pearson VUE account, search for “AZ-400”, select your preferred test center and date
- Exam Cost: Included with your Eccentrix training – exam voucher provided for this expert-level certification
- Scheduling Timeline: Book at least 2-3 weeks in advance for better time slot availability
- Rescheduling Policy: Free rescheduling up to 24 hours before your exam appointment
- Required ID: Government-issued photo ID (passport, driver’s license) matching your registration name exactly
Success Mindset: Approach AZ-400 as a validation of your existing DevOps engineering and CI/CD implementation skills rather than a test of memorized facts. Your practical experience with Azure DevOps, GitHub, pipeline automation, and deployment strategies is your greatest asset. Passing AZ-400 completes your journey to the DevOps Engineer Expert certification.
Frequently Asked Questions about the Microsoft AI-200 + AZ-400 bundle training (FAQ)
What topics are covered in this training bundle?
This program covers the development of modern cloud applications on Azure, including AI-driven solutions (AI-200), as well as the DevOps practices necessary to deliver and operate these applications at scale (AZ-400). You will cover serverless and containerized hosting (Functions, Container Apps, AKS), event-driven integration and messaging (Event Grid, Service Bus), data services suitable for AI workloads (Cosmos DB, PostgreSQL + pgvector, Azure Managed Redis), and then the implementation of CI/CD, source code control, security/compliance, and observability.
Who is this training designed for?
It is aimed at developers and IT professionals who want to master the delivery of cloud applications on Azure with robust DevOps practices. It is also suitable for those transitioning to DevOps roles, as well as teams that want to standardize their pipelines, strengthen security, and improve reliability in production.
What are the prerequisites for this course?
One to two years of development experience, hands-on experience with Azure, and an intermediate level in a programming language (C#, JavaScript, Python, or Java) are recommended. A foundation in HTTP/REST APIs, version control (Git), Agile methodologies, and an understanding of security fundamentals (authentication/authorization, secrets management) are highly advised. Familiarity with serverless, containerized, and event-driven architectures is a plus.
How does this training prepare students for the AI-200 and AZ-400 exams?
The training is scenario-oriented and covers the expected skills: service design and integration, choice of hosting and data models, security, monitoring and troubleshooting (AI-200), then implementation of CI/CD pipelines, source code management, security/compliance and instrumentation/observability (AZ-400). The end-to-end approach helps develop the reasoning required for the exams.
What tools and technologies are used during this training?
Depending on the modules and labs, you will work with Azure Functions, Azure Container Apps, AKS, Event Grid, Service Bus, Cosmos DB for NoSQL, Azure Database for PostgreSQL (pgvector), Azure Managed Redis, as well as DevOps tools (Git version control, CI/CD pipelines) and observability/diagnostic tools.
What are the benefits of earning the DevOps Engineer Expert certification?
It validates your ability to design and implement DevOps practices on Azure to deliver reliable applications at scale: automation, security, compliance, monitoring, and continuous improvement. It is a recognized credential for high-responsibility roles (DevOps Engineer, Cloud Engineer, Platform/Release Engineer) and for cloud transformation projects.
Is the DevOps Engineer Expert training available as a live virtual class?
Yes. It can be followed in an interactive virtual classroom, with an instructor, demonstrations, live discussions and practical exercises.
What types of hands-on labs are included in the DevOps Engineer Expert training?
The labs generally focus on concrete scenarios: serverless and containerized deployments, event-driven/messaging integration, data service configuration, security (secrets/config), then implementation of CI/CD pipelines, source code management, instrumentation/observability and best operating practices.
