Microsoft Certified: Machine Learning Operations Engineer Associate (AI‑300) Practice Exams

About the Azure AI-300 exam

Exam at a glance

AI-300 is Microsoft's new 2026 associate certification for AI operations, the discipline of running machine learning and generative AI in production on Azure. Passing it earns the Microsoft Certified: Machine Learning Operations Engineer Associate credential, the successor to the retired DP-100 Azure Data Scientist Associate. The exam spans two worlds in one blueprint: classical MLOps on Azure Machine Learning, and generative AI operations (GenAIOps) on Microsoft Foundry.

Domain weighting

• Design and implement an MLOps infrastructure: 19%
• Implement machine learning model lifecycle and operations: 30%
• Design and implement a GenAIOps infrastructure: 24%
• Implement generative AI quality assurance and observability: 14%
• Optimize generative AI systems and model performance: 13%

Who this exam is for

AI-300 targets MLOps and GenAIOps engineers who set up the infrastructure and operate machine learning and generative AI on Azure. You are expected to train, optimize, deploy, and maintain traditional models with Azure Machine Learning, and to deploy, evaluate, monitor, and optimize generative AI applications and agents with Microsoft Foundry. Questions are scenario-based and hands-on, mixing portal, Azure CLI, Bicep, and GitHub Actions tasks.

Prerequisites

There are no formal prerequisites. Microsoft recommends a data-science background with hands-on model training and evaluation, working Python, and familiarity with Azure Machine Learning, plus entry-level DevOps skills: basic CI/CD, source control, the command line, and infrastructure as code with Bicep and the Azure CLI. If you hold DP-100, most of the classical machine learning material will already be familiar and your focus shifts to the generative AI domains.

Why take this certification

• The new AI-operations credential. AI-300 is the first Microsoft certification to bring MLOps and GenAIOps together in a single exam, matching how real teams now ship and run AI systems.
• The successor to DP-100. As DP-100 retires, AI-300 is the current path for the machine learning operations role, so your certification stays aligned with Microsoft's active track.
• Tools teams actually use. The blueprint is built around Azure Machine Learning, Microsoft Foundry, MLflow, GitHub Actions, Bicep, and the Azure CLI, alongside RAG, fine-tuning, and AI evaluation and observability.
• Hands-on builder altitude. This is an operations exam that rewards judgment over recall. It proves you can put models and generative apps into production and keep them healthy, not just build them in a notebook.

What you'll learn in the AI-300 exam

AI-300 is hands-on and scenario-driven. Most questions describe a short operational task where two answers both technically work, and the better one follows a single instinct that runs through the whole exam: prefer the managed, least-privilege, reproducible option over the one you wire by hand, grant broadly, or click together in the portal, unless a stated constraint overrides it. Often the first move is naming the lifecycle stage or observability pillar the scenario lives in, and the right tool follows.

Core services and tools you'll be tested on

• MLOps infrastructure: Azure Machine Learning workspaces, datastores, compute targets, data assets, environments, components, and cross-workspace registries; identity and access for workspaces; infrastructure as code with Bicep and the Azure CLI; GitHub Actions provisioning; managed virtual network isolation.
• Model lifecycle and operations: MLflow experiment tracking, automated machine learning, hyperparameter tuning, training pipelines, model registration and versioning, responsible AI evaluation, real-time and batch managed endpoints, progressive rollout and safe rollback, data drift and performance monitoring, and retraining or alert triggers.
• GenAIOps infrastructure: Microsoft Foundry environments and projects, managed identities and role-based access control, private networking, foundation-model deployment via serverless API endpoints and managed compute, provisioned throughput units, and prompt versioning and variant comparison in Git.
• Quality assurance and observability: evaluation datasets and data mapping, AI quality metrics such as groundedness, relevance, coherence, and fluency, risk and safety evaluators, automated evaluation in CI/CD, continuous monitoring, latency, throughput, and token-cost tracking, plus logging and tracing.
• Optimization: RAG tuning across chunk size, similarity thresholds, embedding models, hybrid and semantic search, and A/B testing, along with advanced fine-tuning, synthetic data, and managing a fine-tuned model from development to production.

Operational judgment patterns you'll need to recognize

• Choosing the managed, least-privilege, code-defined option, such as a managed online endpoint, a managed identity over a stored key, or a Bicep deployment, over the hand-wired equivalent.
• Deciding between a managed online endpoint and self-hosting your own Kubernetes cluster, and between real-time and batch inference, for a given latency and cost target.
• Reaching for the right optimization lever when a generative app underperforms: retrieval fixes what the model knows, while fine-tuning fixes how it behaves.
• Automating measurement, such as built-in evaluators inside an evaluate() run wired into CI/CD and continuous evaluation that pages you, instead of manual spot checks.
• Detecting data drift and wiring retraining or alert triggers so a degrading model does not fail silently.
• Optimizing RAG or fine-tuning by changing one variable at a time and proving the gain on a fixed evaluation dataset.

How the practice exams help

Each free question and every premium exam mirrors the scenario style Microsoft uses: a short stem with constraints and several plausible options. Detailed explanations cover not just why the right answer is right but why each distractor is wrong, and cite the official Microsoft Learn documentation, so you learn the trade-offs rather than memorizing answers.

How to prepare for the AI-300 exam

A successful AI-300 plan combines structured study with daily hands-on time in Azure Machine Learning and Microsoft Foundry. A recommended approach:

1. Study the skills measured (2 to 3 weeks). Walk the official AI-300 study guide and the Microsoft Learn AI-300 learning path. Prioritize the model lifecycle domain (30%) and the GenAIOps infrastructure domain (24%), which together account for more than half of the exam.
2. Hands-on labs (2 to 3 weeks). Stand up an Azure Machine Learning workspace and a Microsoft Foundry project. Train and register a model with MLflow, deploy a managed online endpoint, and wire model monitoring. On the generative side, deploy a foundation model, version prompts in Git, run an evaluate() job, and tune a RAG pipeline. Pair the portal with the Azure CLI so you build command-line reps at the same time.
3. Practice IaC and CI/CD (1 week). Build a GitHub Actions workflow that provisions the workspace with Bicep and promotes a model, since automation and reproducibility run through every domain and are frequently the difference between two otherwise valid answers.
4. Practice exams (1 to 2 weeks). Take timed practice tests to surface weak areas. Detailed explanations on every option help you learn the reasoning rather than memorize answers. Aim for consistent scores above 80% before you schedule.

Recommended timeline

Plan on 6 to 10 weeks of focused study (8 to 12 hours per week) for engineers who already have some Azure Machine Learning exposure. Candidates coming from DP-100 can move faster through the classical machine learning domains and should spend the extra time on GenAIOps infrastructure, quality and observability, and optimization.

Official resources

Read the official AI-300 study guide for the exact skills measured, review how to earn the Machine Learning Operations Engineer Associate certification, and walk the AI-300T00 learning path. A Microsoft practice assessment is not yet published for this exam; it usually appears within eight weeks of an exam leaving beta, so lean on hands-on labs and these practice tests in the meantime.

Frequently asked questions