Machine Learning Operations Engineer Associate · AI-300
One workspace for passing Microsoft's AI-300 — a scenario-based exam about operationalizing ML and generative-AI on Azure: the model lifecycle in Azure Machine Learning, standing up GenAI systems in Microsoft Foundry, IaC + CI/CD, and GenAI quality, observability & optimization. Five domain decks and an 87-question practice exam do the heavy lifting, authored against the official skills-measured guide.
The honest read: this is an associate exam that tests operating ML and GenAI on Azure, not the math of ML — it assumes you already know ML fundamentals and basic Azure. Almost every question is “which Azure service fits this scenario,” so the decks + exam here are the core study. AI-300 is newly launched (2026) and recently in beta, so confirm it is generally available — and the exact price for your region — on learn.microsoft.com before booking. Real hands-on Azure ML + Foundry needs a subscription, so those labs point out honestly to the free Microsoft Learn Sandbox rather than pretending to host them.
The exam at a glance
verified July 2026† AI-300 is a new (2026) exam that was recently in beta. Microsoft scores role-based exams on a scaled 1–1000 range with 700 to pass; cost is region-based (~$165 USD). Confirm current general availability, cost, format, and policy on the official certification page before you book.
Certification roadmap
sequenceFundamentals · MCQ
Not required. Worth it only if Azure AI concepts (Azure OpenAI, cognitive services, ML basics) are new. Skip if you already work with Azure ML day to day.
Associate · MCQ + scenario/interactive · 120 min · 700/1000 · 1-year validity · ~$165
The target of this workspace. MLOps + GenAIOps on Azure: the model lifecycle in Azure Machine Learning, standing up generative-AI systems in Microsoft Foundry, IaC (Bicep/CLI) and CI/CD, and GenAI quality/observability/optimization. It tests OPERATING ML and GenAI — which Azure service fits which job — not the math of ML.
Azure AI-300 · AWS MLA · Google PMLE
AI-300 is the Azure leg of an 'MLOps across all three clouds' story alongside AWS MLA-C01 and Google PMLE. Same concepts, three vendors' tooling — a strong portfolio signal. The AWS path has a full workspace here too; Google PMLE's is in build.
Exam domains — weight your study by these
official weights| Skill area | Weight | What it covers |
|---|---|---|
| Implement the ML model lifecycle & operations | 25–30% | Running the lifecycle in Azure Machine Learning — workspaces & assets, data/datastores, environments, training as jobs, pipelines & components, the model registry, managed online + batch endpoints, and the scheduling/event triggers/retraining that operate it. |
| Design & implement a GenAIOps infrastructure | 20–25% | Standing up generative-AI systems in Microsoft Foundry — hubs & projects, the model catalog, deploying & fine-tuning models, prompt flow orchestration, connections, RAG with Azure AI Search grounding, agents, and deploying GenAI apps to endpoints. |
| Design & implement an MLOps infrastructure | 15–20% | The platform plumbing — provisioning workspaces/compute/endpoints as IaC (Bicep/ARM/Terraform/CLI), CI/CD with GitHub Actions or Azure DevOps (environments, approvals, staged promotion), and securing it with RBAC, managed identity, VNets, and Key Vault. |
| Implement GenAI quality assurance & observability | 10–15% | Evaluating GenAI quality (groundedness, relevance, coherence, fluency, similarity), content safety & responsible-AI filtering, prompt-injection defense, and monitoring deployed apps (Application Insights, token usage, latency, online evaluation, feedback). |
| Optimize GenAI systems & model performance | 10–15% | Tuning deployed systems for cost, latency, and quality — token/cost optimization, caching, model right-sizing, throughput (PTU vs pay-go, batching), retrieval-quality tuning for RAG, decoding parameters, and endpoint scaling. |
Model Lifecycle (25–30%) + GenAIOps infrastructure (20–25%) together are roughly half the exam — an engineering cert, not a data-science one. Microsoft publishes weights as ranges; treat them as guidance and re-check the current skills-measured guide (it's a new, evolving exam). The 99 deck cards are distributed to match these weights.
Flashcard decks — the recall layer
5 decks · 99 cardsThe other four areas
Practice exam
live · 87-question poolUse the per-area breakdown to find weak areas, then close them with the matching deck. These are original questions in the exam's scenario style — a strong readiness gauge, not a brain dump of real items.
Hands-on Azure ML + Foundry — the honest note
external · needs an Azure subscriptionAI-300 is multiple-choice, so you can pass on the decks + exam + reading. But building the muscle memory — actually running a train → register → deploy loop in Azure ML, and wiring a RAG flow in Foundry — needs a real Azure subscription, which we can't host for you. Rather than fake it, this path points you at the real thing:
Cost watch:managed online endpoints and compute instances bill by the hour even when idle, and Azure OpenAI bills per token — the top surprise bills. Delete endpoints, stop compute instances the moment you're done, and set a budget alert.
Skill map — where to practice each thing
the workspace| Skill | Practice with | Status |
|---|---|---|
| Which Azure service fits each job (all areas) | The 5 AI-300 decks (99 cards) | ready |
| Exam-format scenario recall & weak-area finding | AI-300 practice exam (draw 50, pass 70%) | ready |
| Hands-on Azure ML + Foundry (train → deploy → RAG) | Microsoft Learn Sandbox + free Azure account (external) | external |
| The official question style & scope | Microsoft Learn AI-300 learning paths + exam sandbox (external, free) | external |
A study routine
- 1.Learn the two spines first: Azure Machine Learning (classic lifecycle) and Microsoft Foundry (GenAI). Most questions live in one or the other — for every service, know whether it does lifecycle, GenAIOps, IaC/CI-CD, QA, or optimization. Do the Model Lifecycle deck first (25–30%).
- 2.Weight your time by the blueprint: Model Lifecycle (25–30%) + GenAIOps infra (20–25%) together are roughly half the exam. The two GenAI-QA and Optimize areas are smaller but easy points once you know the evaluators and cost/latency levers.
- 3.This is an OPERATIONS cert, not a model-training one: practice the plumbing — endpoints & deployments (traffic split), IaC (Bicep + CLI v2 YAML), CI/CD (GitHub Actions / Azure DevOps + OIDC), and RAG wiring — not the math of ML.
- 4.Drill the recurring 'which one' decisions until reflex: online vs batch endpoint, serverless vs PTU, RAG vs fine-tuning, managed VNet vs public, groundedness vs the other evaluators. The scenario's single CONSTRAINT (cost, latency, security, freshness) usually picks the answer.
- 5.Use the practice exam's per-area breakdown to find weak areas, then close them with the matching deck. Hit 70% (the real 700/1000 cut) three times before you book — and re-check the official study guide, since this is a new, evolving exam.
Curated resources — free-first
verified July 2026Everything you need to LEARN the AI-300 is free (the official study guide + Microsoft Learn paths + the Azure ML, Foundry, and Azure OpenAI docs). Only the exam voucher (~$165) and real Azure usage cost money. Every link checked live (July 2026); prices/versions shift, so treat costs as “as seen” — and re-check the study guide, since this exam is new.