AI-300 study guide
AI-300 · Work through your exam, one topic at a time.
Exam AI-300 is the requirement for the Microsoft Certified: Machine Learning Operations Engineer Associate credential. It targets engineers who build and operate AI workloads on Azure across two tracks: classic MLOps using Azure Machine Learning, and GenAIOps using Microsoft Foundry. Microsoft expects candidates to have a data science background with Python, entry-level DevOps familiarity (GitHub Actions, command-line interfaces), and hands-on experience with infrastructure as code using Bicep and Azure CLI. The exam measures five areas: designing and implementing MLOps infrastructure (15–20%), machine learning model lifecycle and operations (25–30%), GenAIOps infrastructure (20–25%), generative AI quality assurance and observability (10–15%), and optimizing generative AI systems and model performance (10–15%). The AI-300 study guide has 5 sections and about 1,690 words.
01Design and Implement an MLOps Infrastructure
MLOps infrastructure questions test whether you can build a repeatable, governed path from a data scientist's notebook to a production deployment. Know the resource hierarchy: a workspace holds compute, environments, datastores, and job definitions; a registry shares models, components, and environments across workspaces; datastores point at storage while data assets are versioned references to actual data.
331 words, about 2 min02Implement Machine Learning Model Lifecycle and Operations
Lifecycle questions pick up where the model finishes training. The spine is: run a training pipeline → evaluate metrics → pass a quality gate → register a versioned model with lineage → deploy → monitor → retrain or roll back .
337 words, about 2 min03Design and Implement a GenAIOps Infrastructure
GenAIOps is MLOps with nondeterministic artifacts: prompts, indexes, model deployments, and evaluation suites all need versioning and promotion. Think of the pipeline as ideate → experiment → evaluate → deploy → monitor → iterate .
335 words, about 2 min04Implement Generative AI Quality Assurance and Observability
GenAI QA splits into offline evaluation (before release, against a labeled golden dataset) and online observability (after release, on real traffic). Questions often hinge on knowing which one the scenario describes.
338 words, about 2 min05Optimize Generative AI Systems and Model Performance
Optimization questions ask you to trade quality against cost and latency without breaking grounding. Work the levers in order: prompt , retrieval , model , infrastructure .
349 words, about 2 min