Machine Learning Operations (MLOps) Engineer Associate

categoryTopics Covered

Microsoft's own objectives, in the order the exam presents them. Each area links to its section on Microsoft Learn. Checked 2026-10-26.

1. 1Design and implement an MLOps infrastructure15–20%expand_more

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   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
2. 2Implement machine learning model lifecycle and operations25–30%expand_more

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   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
3. 3Design and implement a GenAIOps infrastructure20–25%expand_more

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   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
4. 4Implement generative AI quality assurance and observability10–15%expand_more

   open_in_newThis area on Microsoft Learn

   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
5. 5Optimize generative AI systems and model performance10–15%expand_more

   open_in_newThis area on Microsoft Learn

   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