AI-300

AI-300: Microsoft Certified Machine Learning Operations MLOps Engineer Associate Training Course

Master the Operational Logic of Azure MLOps, GenAIOps, and Production AI Systems

The AI-300 Training Course is an advanced, industry-aligned program engineered for IT professionals pursuing Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (beta). This training course transcends traditional exam preparation by adopting the AAAdemy Atomic Deconstruction methodology, stripping complex Azure AI operations into operational layers, component specifications, step-by-step execution paths, and exam-ready workflows.

Strategic Focus on Azure AI Operations

Rooted in the latest official AI-300 exam objectives, this training integrates the core pillars of Azure Machine Learning, Microsoft Foundry, GitHub Actions, Bicep, Azure CLI, production evaluation, observability, and optimization. Learners move beyond basic service recognition to master:

• MLOps Infrastructure Logic: Creating Azure Machine Learning workspaces, datastores, compute targets, assets, registries, network restrictions, and IaC automation.

• Model Lifecycle Operations: Tracking experiments with MLflow, registering models, deploying online and batch endpoints, testing endpoint behavior, and implementing rollout or rollback controls.

• GenAIOps Platform Engineering: Configuring Microsoft Foundry environments, managed identities, RBAC, private networking, foundation model deployments, throughput, and prompt version control.

• Quality and Observability: Building evaluation datasets, applying groundedness and safety metrics, monitoring latency and token cost, and using logs or traces for production troubleshooting.

• Optimization and Customization: Tuning RAG retrieval, embeddings, hybrid search, A/B testing, synthetic data, fine-tuning, and production model performance.

Task-Oriented & Scenario-Based Learning

Through the Operational Skills Matrix, participants gain practical insight into command validation, deployment state inspection, metric interpretation, trace analysis, evaluation workflow design, and production troubleshooting. The curriculum prepares candidates to interpret complex scenarios, from identity failures in Azure Machine Learning workspaces to groundedness regressions in Microsoft Foundry-based generative AI applications.

The course also trains exam-question transformation: learners practice how AI-300 scenarios are asked, how distractors are designed, why the correct answer resolves the operational constraint, and which command, metric, trace, log, or evaluation result proves the decision.

Table of Contents

1. Study Plan for AI-300 Exam

2. Study Methods and Key Points

3. Knowledge Explanation

Design and implement an MLOps infrastructure

• Create and manage resources in an Azure Machine Learning workspace

• Create and manage assets in an Azure Machine Learning workspace

• Implement infrastructure as code for Azure Machine Learning

Implement machine learning model lifecycle and operations

• Orchestrate model training in Azure Machine Learning

• Implement model registration and versioning

• Deploy machine learning models for production environments

• Monitor and maintain machine learning models in production

Design and implement a GenAIOps infrastructure

• Implement Foundry environments and platform configuration

• Deploy and manage foundation models for production workloads

• Implement prompt versioning and management with source control

Implement generative AI quality assurance and observability

• Configure evaluation and validation for generative AI applications and agents

• Implement observability for generative AI applications and agents

Optimize generative AI systems and model performance

• Optimize retrieval-augmented generation performance and accuracy

• Implement advanced fine-tuning and model customization

4. Practice Questions and Answers