AI-300 Study Guide: Every Skill Area of Microsoft’s MLOps Exam, Explained

AI-300 (Operationalizing Machine Learning and Generative AI Solutions) is the exam for Microsoft Certified: Machine Learning Operations Engineer Associate, the certification that replaced DP-100. It tests two jobs in one: MLOps for traditional models on Azure Machine Learning, and GenAIOps for generative AI apps and agents on Microsoft Foundry.

You get 120 minutes, need 700 to pass, and pay $165 in the US. This guide goes through Microsoft’s five skill areas one at a time: what the outline lists, what it means in practice, and how to study each part.

AI-300 at a glance

Checked against Microsoft Learn’s AI-300 study guide and Machine Learning Operations Engineer Associate certification page on October 6, 2026.

What AI-300 is really testing

Microsoft calls the job AI operations (AIOps): setting up and running the infrastructure for both machine learning operations and generative AI operations on Azure. The audience profile asks for experience training, optimizing, deploying and maintaining traditional models in Azure Machine Learning, and deploying, evaluating, monitoring and optimizing generative AI apps and agents in Microsoft Foundry. You work with data scientists, DevOps teams and stakeholders, and you automate everything you can.

In practice, the first two skill areas (40–50% of the exam) live in Azure Machine Learning, and the last three (40–55%) live in Foundry. Neither half is optional. Our honest review of AI-300 covers whether that mix fits your career; this page is about passing it.

The five skill areas at a glance

Microsoft notes that the bullets under each skill illustrate how it assesses that skill and that related topics may be covered. Most questions cover generally available features, though Preview features can appear if they are commonly used.

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

What the outline lists:

• Workspace resources: create and manage a Machine Learning workspace, datastores and compute targets, and configure identity and access management for workspaces.
• Workspace assets: data assets, environments and components, and sharing assets across workspaces with registries.
• Infrastructure as code: GitHub integration with secure access, deploying workspaces and resources with Bicep and the Azure CLI, automating provisioning with GitHub Actions workflows, restricting network access to workspaces, and Git source control for ML projects.

How to study it: do the whole loop once yourself. Write a Bicep template for a workspace, deploy it from a GitHub Actions workflow, register a data asset and an environment, and share a component through a registry. Then lock the workspace’s network access down and fix whatever breaks. This domain rewards people who have actually provisioned things, not people who have read about provisioning.

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

The heaviest skill area, and the one closest to the old DP-100.

What the outline lists:

• Orchestrating training: experiment tracking with MLflow, automated ML, notebooks for exploration, hyperparameter tuning, training scripts, distributed training for large and deep learning models, training pipelines, and comparing model performance across jobs.
• Registration and versioning: packaging a feature retrieval specification with the model artifact, registering an MLflow model, evaluating a model against responsible AI principles, and managing the lifecycle through archiving.
• Production deployment: real-time or batch endpoints with managed inference, testing and troubleshooting endpoints, and progressive rollout with safe rollback.
• Monitoring: detecting data drift, tracking production performance metrics, and configuring retraining or alert triggers when thresholds are crossed.

How to study it: train one model end to end with MLflow tracking on, run a sweep job for hyperparameters, wrap it in a pipeline, register it, and deploy it to both a real-time and a batch endpoint. Then practice a progressive rollout: put a second deployment behind the same endpoint, shift some traffic to it, and roll back. Finish by setting up data drift monitoring with an alert. If you can explain when to choose real-time versus batch and how a rollback works, you have the core of this domain.

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

What the outline lists:

• Foundry environments: creating and configuring Foundry resources and projects, identity with managed identities and role-based access control (RBAC), network security and private networking, and deploying infrastructure with Bicep and the Azure CLI.
• Foundation models in production: deploying through serverless API endpoints or managed compute, choosing the right model for a use case, versioning and production deployment strategies, and provisioned throughput units for high-volume workloads.
• Prompt versioning: designing prompts, creating prompt variants and comparing their performance, and keeping prompts under version control in Git.

How to study it: stand up a Foundry project with a managed identity and least-privilege RBAC, deploy one model as a serverless API and one on managed compute, and write down when you would pick each and when provisioned throughput is worth paying for. Treat prompts like code: keep two variants in a Git repo and compare them. The outline puts identity, networking, model choice and throughput in the same skill area, so study them together rather than as separate topics.

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

What the outline lists:

• Evaluation: test datasets and data mapping, quality metrics (groundedness, relevance, coherence and fluency), risk and safety evaluations for harmful content, and automated evaluation workflows with built-in and custom metrics, for both applications and agents.
• Observability: continuous monitoring in Foundry, latency, throughput and response times, cost metrics such as token consumption and resource usage, and logging, tracing and debugging for production troubleshooting.

How to study it: learn the four quality metrics well enough to say which one catches which failure. An answer can be fluent and coherent but still ungrounded, and the outline lists each metric separately, so be able to tell them apart. Run one automated evaluation in Foundry against a small test dataset, then look at what monitoring shows you for latency and token use.

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

What the outline lists:

• RAG performance: tuning similarity thresholds, chunk sizes and retrieval strategies, selecting and fine-tuning embedding models for a domain, hybrid search that combines semantic and keyword retrieval, and judging RAG quality with relevance metrics and A/B tests.
• Fine-tuning: advanced fine-tuning methods, creating and managing synthetic data for fine-tuning, monitoring a fine-tuned model’s performance, and taking it from development to production.

How to study it: build a small RAG app and change one setting at a time (chunk size, then similarity threshold, then hybrid search on and off), measuring relevance after each. That experiment teaches the tradeoffs faster than any reading. For fine-tuning, focus on when it beats better retrieval or prompting, and how synthetic data fits in.

If you studied for DP-100

DP-100’s outline covered designing and preparing an ML solution, exploring data and running experiments, training and deploying models, and optimizing language models. A good share of the Azure Machine Learning material carries over into AI-300’s first two skill areas: workspaces, MLflow, automated ML, pipelines and endpoints.

What is new is the operations layer: infrastructure as code with Bicep and GitHub Actions, network isolation, progressive rollout and drift-triggered retraining, and an entire Foundry half on GenAIOps, evaluation and observability. Our DP-100 retirement guide explains what happened to the old certification and what existing holders keep.

Free official prep resources

• The AI-300 study guide, which has the full skills-measured list summarized above.
• Microsoft Learn training: self-paced learning paths and modules, or an instructor-led course, linked from the certification page.
• The free Practice Assessment on AI Skills Navigator. You need to sign in to AI Skills Navigator to launch it.
• The exam sandbox, which lets you try the question types in the real exam interface before test day.
• Exam Readiness Zone videos on Microsoft Learn.

One tip for non-native English speakers: AI-300 is offered in English only, and Microsoft says that when an exam is not available in your preferred language you can request an additional 30 minutes. Ask for it when you register.

Suggested order of attack

1. Domain 2 first. It is the biggest and the foundation the others assume.
2. Then Domain 1, using the model from Domain 2 as the thing you automate and secure.
3. Then Domain 3, the biggest Foundry area, which reuses the same infrastructure-as-code and identity ideas.
4. Domains 4 and 5 last, together. They are 20–30% of the exam combined and build directly on a working Foundry deployment.
5. Finish with the Practice Assessment, then revisit the skill bullets for whichever area scored lowest.

Comparing clouds? The AWS counterpart is the Machine Learning Engineer – Associate; see our AWS ML Engineer study guide and MLA-C02 changes. If you build AI apps more than you operate them, Microsoft’s AI-103 (Azure AI Apps and Agents Developer) may be the better fit, and our overview of Microsoft AI certifications in 2026 maps the whole path.

After you pass: renewal

Like other Microsoft associate certifications, the Machine Learning Operations Engineer Associate expires one year after you earn it. Renewal is a free online assessment on Microsoft Learn, and the renewal window opens six months before the expiry date. Our guide to AI certification renewal rules and the renewal tracker help you keep the date.

HOW TO // AI is not affiliated with or endorsed by Microsoft. AI-300, DP-100, Microsoft Certified: Machine Learning Operations Engineer Associate, Azure Machine Learning and Microsoft Foundry are trademarks of Microsoft Corporation; we reference them descriptively. All content is original and based on Microsoft’s published study guide. Check the official certification page before you book.

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Related guides

• Is the AI-300 Certification Worth It? An Honest Look at Microsoft’s MLOps Cert
• DP-100 Is Retired: What Replaced Azure’s Data Science Certification
• Microsoft AI Certifications in 2026: The New Path, Explained
• AI-103 Study Guide & Cheat Sheet (Azure AI Apps & Agents Developer)