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Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) Syllabus

Design and implement an MLOps infrastructure

Provisioning Azure Machine Learning workspaces, assets and infrastructure as code.

19%

Weight

Questions

Marks

Machine Learning workspace resources

Creating and managing workspaces
Datastores
Compute targets
Workspace identity and access management

Machine Learning workspace assets

Data assets
Environments
Components
Sharing assets across workspaces with registries

Infrastructure as code for Machine Learning

GitHub integration with Azure Machine Learning
Deploying workspaces with Bicep and Azure CLI
Provisioning automation with GitHub Actions workflows
Restricting network access to workspaces
Git source control for ML projects

Implement machine learning model lifecycle and operations

Training, registering, deploying and monitoring models in production.

30%

Weight

Questions

Marks

Orchestrate model training

Experiment tracking with MLflow
Automated machine learning
Hyperparameter tuning
Distributed training for deep learning models
Training pipelines and comparing jobs

Model registration and versioning

Registering MLflow models
Packaging feature retrieval specifications
Responsible AI evaluation
Model lifecycle and archiving

Deploy models to production

Managed online (real-time) endpoints
Batch endpoints
Testing and troubleshooting endpoints
Progressive rollout and safe rollback

Monitor and maintain production models

Data drift detection
Production performance metrics
Retraining and alert triggers on thresholds
Model monitoring signals

Design and implement a GenAIOps infrastructure

Configuring Microsoft Foundry environments, deploying foundation models and managing prompts.

24%

Weight

Questions

Marks

Foundry environments and platform configuration

Foundry resources and projects
Managed identities and RBAC
Private networking and network security
Bicep templates and Azure CLI deployment

Deploy and manage foundation models

Serverless API and managed compute deployments
Selecting models for use cases
Model versioning and deployment strategies
Provisioned throughput units

Prompt versioning and management

Designing and developing prompts
Creating prompt variants
Comparing performance across prompts
Version control for prompts with Git

Implement generative AI quality assurance and observability

Evaluating and monitoring generative AI applications and agents.

14%

Weight

Questions

Marks

Evaluation and validation

Test datasets and data mapping
Groundedness, relevance, coherence and fluency metrics
Risk and safety evaluations
Automated evaluation workflows with built-in and custom evaluators

Observability

Continuous monitoring in Foundry
Latency, throughput and response-time metrics
Token consumption and cost tracking
Logging, tracing and debugging

Optimize generative AI systems and model performance

Improving RAG accuracy and customizing models with fine-tuning.

13%

Weight

Questions

Marks

RAG performance and accuracy

Similarity thresholds, chunk sizes and retrieval strategies
Selecting and fine-tuning embedding models
Hybrid semantic and keyword search
Relevance metrics and A/B testing

Advanced fine-tuning and customization

Fine-tuning methods
Synthetic data for fine-tuning
Monitoring fine-tuned model performance
Promoting fine-tuned models to production

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Learning Progress

Design and implement an MLOps infrastructure 85%

Implement machine learning model lifecycle and operations 92%

Practice Test Scores

95%

Latest Score

Above passing threshold

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