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Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
| Useful links | Description |
|---|---|
| How to earn the certification | Some certifications only require passing one exam, while others require passing multiple exams. |
| Certification renewal | Microsoft associate, expert, and specialty certifications expire annually. You can renew by passing a free online assessment on Microsoft Learn. |
| Your Microsoft Learn profile | Connecting your certification profile to Microsoft Learn allows you to schedule and renew exams and share and print certificates. |
| Exam scoring and score reports | A score of 700 or greater is required to pass. |
| Exam sandbox | You can explore the exam environment by visiting our exam sandbox. |
| Request accommodations | If you use assistive devices, require extra time, or need modification to any part of the exam experience, you can request an accommodation. |
About the exam
Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured
Audience profile
As a candidate for this Microsoft Certification, you should have subject matter expertise in setting up infrastructure for machine learning operations (MLOps) and generative AI operations (GenAIOps) solutions on Azure, together referred to as AI operations (AIOps). You need experience training, optimizing, deploying, and maintaining traditional machine learning models by using Azure Machine Learning, in addition to experience deploying, evaluating, monitoring, and optimizing generative AI applications and agents by using Microsoft Foundry.
You should have a data science background with experience in Python programming and an entry-level understanding of DevOps practices, including using tools like GitHub Actions and working with command-line interfaces (CLIs).
Additionally, you need knowledge and experience in MLOps by using:
Machine Learning.
Foundry.
GitHub Actions.
Infrastructure as code (IaC) practices with Bicep and Azure CLI.
Your responsibilities for this role include:
Designing and implementing MLOps infrastructure.
Implementing machine learning model lifecycle and operations.
Designing and implementing GenAIOps infrastructure.
Implementing generative AI quality assurance and observability.
Optimizing generative AI systems and model performance.
You work with data scientists, DevOps teams, and stakeholders to deliver scalable AI solutions with comprehensive automation and monitoring.
Skills at a glance
Design and implement an MLOps infrastructure (15–20%)
Implement machine learning model lifecycle and operations (25–30%)
Design and implement a GenAIOps infrastructure (20–25%)
Implement generative AI quality assurance and observability (10–15%)
Optimize generative AI systems and model performance (10–15%)
Design and implement an MLOps infrastructure (15–20%)
Create and manage resources in a Machine Learning workspace
Create and manage a workspace
Create and manage datastores
Create and manage compute targets
Configure identity and access management for workspaces
Create and manage assets in a Machine Learning workspace
Create and manage data assets
Create and manage environments
Create and manage components
Share assets across workspaces by using registries
Implement IaC for Machine Learning
Configure GitHub integration with Machine Learning to enable secure access
Deploy Machine Learning workspaces and resources by using Bicep and Azure CLI
Automate resource provisioning by using GitHub Actions workflows
Restrict network access to Machine Learning workspaces
Manage source control for machine learning projects by using Git
Implement machine learning model lifecycle and operations (25–30%)
Orchestrate model training
Configure experiment tracking with MLflow
Use automated machine learning to explore optimal models
Use notebooks for experimentation and exploration
Automate hyperparameter tuning
Run model training scripts
Manage distributed training for large and deep learning models
Implement training pipelines
Compare model performance across jobs
Implement model registration and versioning
Package a feature retrieval specification with the model artifact
Register an MLflow model
Evaluate a model by using responsible AI principles
Manage model lifecycle, including archiving models
Deploy machine learning models for production environments
Deploy models as real-time or batch endpoints with managed inference options
Test and troubleshoot model endpoints
Implement progressive rollout and safe rollback strategies
Monitor and maintain machine learning models in production
Detect and analyze data drift
Monitor performance metrics of models deployed to production
Configure retraining or alert triggers when thresholds are exceeded
Design and implement a GenAIOps infrastructure (20–25%)
Implement Foundry environments and platform configuration
Create and configure Foundry resources and project environments
Configure identity and access management with managed identities and role-based access control (RBAC)
Implement network security and private networking configurations
Deploy infrastructure using Bicep templates and Azure CLI
Deploy and manage foundation models for production workloads
Deploy foundation models by using serverless API endpoints and managed compute options
Select appropriate models for specific use cases
Implement model versioning and production deployment strategies
Configure provisioned throughput units for high-volume workloads
Implement prompt versioning and management with source control
Design and develop prompts
Create prompt variants and compare performance across different prompts
Implement version control for prompts by using Git repositories
Implement generative AI quality assurance and observability (10–15%)
Configure evaluation and validation for generative AI applications and agents
Create test datasets and data mapping for comprehensive model evaluation
Implement AI quality metrics, including groundedness, relevance, coherence, and fluency
Configure risk and safety evaluations for harmful content detection
Set up automated evaluation workflows by using built-in and custom evaluation metrics
Implement observability for generative AI applications and agents
Examine continuous monitoring in Foundry
Monitor performance metrics, including latency, throughput, and response times
Track and optimize cost metrics, including token consumption and resource usage
Configure detailed logging, tracing, and debugging capabilities for production troubleshooting
Optimize generative AI systems and model performance (10–15%)
Optimize retrieval-augmented generation (RAG) performance and accuracy
Optimize retrieval performance by tuning similarity thresholds, chunk sizes, and retrieval strategies
Select and fine-tune embedding models for domain-specific use cases and accuracy improvements
Implement and optimize hybrid search approaches combining semantic and keyword-based retrieval
Evaluate and improve RAG system performance by using relevance metrics and A/B testing frameworks
Implement advanced fine-tuning and model customization
Design and implement advanced fine-tuning methods
Create and manage synthetic data for fine-tuning
Monitor and optimize fine-tuned model performance
Manage a fine-tuned model from development through production deployment
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course |
| Find documentation | Artificial Intelligence overview Generative AI documentation Microsoft 365 Copilot documentation Microsoft 365 documentation |
| Ask a question | Microsoft Q&A | Microsoft Docs |
| Get community support | Microsoft 365 Copilot community hub Microsoft 365 community hub |
| Follow Microsoft Learn | Microsoft Learn - Microsoft Tech Community |
| Find a video | Exam Readiness Zone Browse other Microsoft Learn shows |