Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate ( AI-300) Practice Exam

Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) Practice Exam

About Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate ( AI-300) Exam

The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) certification is designed for professionals who want to validate their expertise in building, automating, deploying, and monitoring AI operations solutions on Azure. This certification focuses on machine learning operations (MLOps) and generative AI operations (GenAIOps), together forming modern AI operations (AIOps) environments. It is ideal for professionals responsible for creating scalable AI infrastructure using Azure Machine Learning, Microsoft Foundry, GitHub Actions, and infrastructure as code (IaC) practices.

Who should take the Exam?

The AI-300 certification is ideal for professionals involved in deploying, automating, and monitoring ML and generative AI solutions on Azure.

• MLOps Engineers
• AI Engineers
• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Azure Cloud Engineers
• Professionals working on LLMOps, RAG, and AI agents

Skills Validated

This certification validates your ability to:

• Design and implement MLOps infrastructure on Azure
• Manage the machine learning model lifecycle
• Build and maintain GenAIOps environments
• Implement AI quality assurance and observability
• Optimize RAG systems, prompts, and model performance
• Automate deployment workflows using GitHub Actions, Bicep, and Azure CLI

Knowledge Gained

By preparing for the AI-300 certification, you will gain expertise in:

• Designing Azure MLOps and GenAIOps infrastructure
• Automating training, deployment, and monitoring pipelines
• Managing ML model versioning and lifecycle operations
• Implementing prompt evaluation and GenAI observability
• Optimizing RAG pipelines, embeddings, and retrieval
• Using GitHub Actions, Bicep, and Azure CLI for AI automation
• Monitoring latency, token usage, cost, and drift
• Improving LLM and ML model performance in production

Skills Required

To succeed in AI-300, candidates should be comfortable with:

• Python programming
• Machine learning workflows
• Azure Machine Learning
• Microsoft Foundry
• GitHub Actions
• Infrastructure as code using Bicep
• Azure CLI and command-line workflows
• Model evaluation and monitoring concepts
• RAG, embeddings, and prompt optimization
• Entry-level DevOps practices

Career Opportunities 

This certification supports roles such as:

• MLOps Engineer
• GenAIOps Engineer
• Azure AI Engineer
• Machine Learning Platform Engineer
• LLM Operations Engineer
• AI Infrastructure Engineer
• RAG Systems Engineer

Course Outline 

The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate ( AI-300) Exam covers the following - 

Domain 1 - Design and implement an MLOps infrastructure (15–20%)

1.1 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

1.2 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

1.2 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

Domain 2 - Implement machine learning model lifecycle and operations (25–30%)

2.1 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

2.2 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

2.3 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

2.4 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

Domain 3 - Design and implement a GenAIOps infrastructure (20–25%)

3.1 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

3.2 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

3.3 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

Domain 4 - Implement generative AI quality assurance and observability (10–15%)

4.1 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

4.2 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

Domain 5 - Optimize generative AI systems and model performance (10–15%)

5.1 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

5.2 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