Microsoft Machine Learning Operations Engineer Associate (AI-300)

About the AI-300 exam

AI-300, Operationalizing Machine Learning and Generative AI Solutions, is the exam for the Microsoft Certified: Machine Learning Operations Engineer Associate certification. It covers what Microsoft calls AI operations: running classic machine learning models with Azure Machine Learning (MLOps) and running generative AI apps and agents with Microsoft Foundry (GenAIOps). Microsoft pitches it at people with a data science background who write Python and know basic DevOps, such as GitHub Actions, the Azure CLI and Bicep.

The questions are less about building a model and more about everything around it: where it trains, how it is tracked and registered, how it is deployed and rolled back safely, and how you notice when it stops working. On the generative side they ask how a foundation model is deployed, secured, evaluated and kept affordable. Distractors tend to be valid tools used at the wrong stage, such as monitoring where an evaluation was needed, or a managed online endpoint where a batch endpoint fits the workload.

Exam format

• Questions: Microsoft doesn't publish an exact number; it says most of its certification exams have 40 to 60. Item types can include case studies, drag-and-drop and hot area as well as multiple choice, and there may be interactive components.
• Time: 120 minutes.
• Passing score: 700 on a scaled score out of 1,000.
• Cost and delivery: the price depends on the country where you take it. It is proctored and scheduled through Pearson VUE.
• Language: English only at the moment.
• Validity: 1 year. You renew for free by passing an online assessment on Microsoft Learn.

What the exam covers

The study guide splits the exam into five domains:

• Design and implement an MLOps infrastructure (15-20%). Machine Learning workspaces, datastores, compute targets, environments, components and registries for sharing assets, plus workspace deployment with Bicep, the Azure CLI and GitHub Actions, and network isolation.
• Implement machine learning model lifecycle and operations (25-30%). MLflow tracking and autologging, automated ML, hyperparameter sweeps, distributed training and pipelines, model registration, real-time and batch endpoints, safe rollout with traffic splitting or mirroring, and data drift monitoring with retraining triggers.
• Design and implement a GenAIOps infrastructure (20-25%). Foundry resources and projects, managed identity and RBAC, private networking, serverless and managed compute deployments, provisioned throughput units, and versioning prompts in Git.
• Implement generative AI quality assurance and observability (10-15%). Test datasets, quality evaluators such as groundedness, relevance, coherence and fluency, risk and safety evaluations, continuous monitoring, token cost tracking and tracing.
• Optimize generative AI systems and model performance (10-15%). RAG tuning (chunk size, similarity thresholds, hybrid search, embedding choice), A/B testing, fine-tuning methods, synthetic training data, and taking a fine-tuned model to production.

How the practice questions here are organised

There are 10 practice tests of 10 questions each, and every test includes at least one question from each of the five domains, weighted towards the model lifecycle as the real exam is. You'll see questions on picking a training compute target, sharing models through a registry, validating a new deployment with mirrored traffic, sizing PTUs and wiring up continuous evaluation. Each question explains the right answer and why each wrong option falls short, with a link to the Microsoft Learn page it is based on.

The full practice exam has 50 questions in 120 minutes, split across the domains in proportion to their weights, with answers hidden until you submit. Because Microsoft doesn't publish how the scaled score maps to correct answers, the mock passes you at 70%.

A study plan that works

• Take Test 1 cold, then note which half (MLOps or GenAIOps) costs you more marks.
• Get hands-on with both platforms: run a training job with MLflow tracking in Azure Machine Learning, deploy it to an online endpoint, then deploy a model in Foundry and run an evaluation against it.
• Learn the Azure CLI ml extension and a small Bicep template, because infrastructure as code appears in two domains.
• Read every explanation for the questions you got wrong or guessed, and revisit a weak test a few days later rather than straight away.
• Finish with the full practice exam in one timed sitting.

These questions are original, written from the public Microsoft Learn documentation and the official study guide. They are not real exam questions, and this site is not affiliated with Microsoft.