Microsoft Certified: Machine Learning Operations Engineer Associate

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Exam at a Glance

AI-300 measures whether you can do the day-to-day work of an AI operations engineer on Azure: running machine learning models in production with Azure Machine Learning, and operating generative AI applications and agents with Microsoft Foundry.

 

What the Exam Covers

The exam has five domains across two products. The two Azure Machine Learning domains and the three Microsoft Foundry domains each make up about half of the exam, so prepare across both MLOps and GenAIOps; model lifecycle and operations by itself is the highest-weight domain.

 

Design and implement an MLOps infrastructure — 15-20%

Azure Machine Learning workspaces, datastores, compute targets and workspace access; data assets, environments, components and cross-workspace registries; infrastructure as code (secure GitHub integration, Bicep and Azure CLI, GitHub Actions provisioning, network restrictions, Git source control).

 

Implement machine learning model lifecycle and operations — 25-30%

Training orchestration (MLflow tracking, automated machine learning, hyperparameter sweeps, distributed training, pipelines); model registration and versioning (feature retrieval specifications, MLflow models, responsible AI evaluation, archiving); real-time and batch endpoints with managed inference, troubleshooting, progressive rollout and safe rollback; production monitoring (data drift, performance metrics, retraining and alert triggers).

 

Design and implement a GenAIOps infrastructure — 20-25%

Microsoft Foundry resources and projects, managed identities and role-based access control, private networking, Bicep and Azure CLI deployments; foundation model deployment through serverless API endpoints and managed compute, model selection, versioning and deployment strategies, provisioned throughput units; prompt design, prompt variants and comparison, prompt version control in Git.

 

Implement generative AI quality assurance and observability — 10-15%

Evaluating applications and agents (test datasets and data mapping, groundedness, relevance, coherence and fluency metrics, risk and safety evaluations, automated workflows with built-in and custom evaluators); observability (continuous monitoring in Foundry, latency and throughput, token and cost tracking, logging, tracing and debugging).

 

Optimize generative AI systems and model performance — 10-15%

Tuning retrieval-augmented generation (similarity thresholds, chunk sizes, retrieval strategies, embedding model selection and fine-tuning, hybrid semantic plus keyword search, relevance metrics and A/B testing); advanced fine-tuning and model customization (fine-tuning methods, synthetic data, monitoring fine-tuned models, managing them to production).

 

The full bullet-level list lives in the official study guide. Treat it as your checklist:

The Official AI-300 Study Guide

 

How This Maps to CertiAce Practice Modules

The CertiAce question bank has two practice modules that mirror the two official learning paths (one per half of the exam), so a weak module tells you which domains to revisit:

 

Where AI-300 Fits

AI-300 has no official prerequisites. If you are new to Azure AI, AI-901 is a gentle on-ramp but not required. If you already work with Azure Machine Learning or Microsoft Foundry, go straight to AI-300.

Certification and Exam Details Page

 

Before You Start

The exam assumes practical exposure, not just reading, and tests operations judgment rather than data science theory. Check yourself against this table. Anything unfamiliar is where your preparation should start:

 

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Step-by-Step Study Plan

How long you need depends on where you start. Treat these estimates as planning guidance and adjust them to your starting knowledge and weekly study hours:

 

Step 1: Read the Official Study Guide

Skim the full skills-measured list once, and mark every bullet you could not explain to a colleague. That marked-up list is your personal syllabus: everything else in this plan exists to clear it.

The Official Study Guide

 

Step 2: Schedule Your Exam

Choose a realistic target date after reviewing the skills list and your available study time. A date on the calendar turns studying into a countdown, so pick one using the prep-time table above and plan backwards from it. The exam is offered in English only. If the exam is not available in your native language, check the English as a Second Language accommodation and arrange any approved extra time before booking.

Exam accommodations

Certification and Exam Details Page

 

Step 3: Work Through the Official Learning Path

Complete the AI-300 course modules on Microsoft Learn. Its two learning paths, Operationalize machine learning models (MLOps) and Operationalize generative AI applications (GenAIOps), mirror the exam's two halves and the two CertiAce practice modules. Take notes on concepts you cannot explain in simple terms, and flag anything that needs hands-on practice. You will come back to those in Step 5.

Official Learning Path Course Page

 

Step 4: Use a Supplementary Video Resource

No dedicated AI-300 video course exists yet: the exam is still young, and Microsoft Learn's channel has only a short course preview. Until one appears, freeCodeCamp's free 5.5-hour MLflow course is the best full-length substitute: experiment tracking, model versioning, prompt management and systematic evaluation are exactly the mechanics the model-lifecycle domain tests, and Azure Machine Learning is MLflow-native, so the concepts transfer even though the course is Databricks-flavored. For the Foundry half, pair it with John Savill's Azure AI Foundry Overview from the resource table below.

Learn MLOps with MLflow and Databricks – Full Course for Machine Learning Engineers

 

Step 5: Get Hands-On Practice

This is the step that matters most. AI-300 question stems describe operational situations (a deployment failing its probes, a sweep that finds no metric, an evaluation run that regresses), and hands-on time is what makes the answers obvious. Microsoft publishes two official, free lab sites for the course, mirroring the two learning paths: eight MLOps challenges and seven GenAIOps exercises, from setup through training and evaluation to deployment, monitoring and tracing. Make sure you touch:

• Compute target selection and scale-to-zero settings
• Blue/green traffic splitting and mirrored traffic on a managed online endpoint
• Model monitoring with production and ground-truth data
• Foundry deployment types and provisioned throughput
• Running a cloud evaluation and reading its results
• Reading a trace for a slow or failing generative AI call

Azure trial credit is a useful starting point; check model and compute availability and each exercise's prerequisites before deploying. Scale idle training clusters to zero and stop unused compute instances to avoid their compute-hour charges. Storage, networking, and deployed endpoints may still incur charges.

MLOps Challenges

GenAI Operations (GenAIOps) Workload Labs

 

Step 6: Benchmark Your Knowledge

Use CertiAce to benchmark your readiness module by module. The practice modules map to the exam domains as shown above, so use the module mapping and the topics behind your missed questions to identify the skills-measured areas to revisit. Microsoft also offers a free official practice assessment for AI-300 on AI Skills Navigator (sign in to launch it). Aim for consistent performance across every module, not one lucky high score; if a topic is unstable, go back to learning plus hands-on practice.

CertiAce AI-300 Exam Practice

Official Practice Assessment on AI Skills Navigator

 

Step 7: Take the Exam

The day before, review only your weak topics (no brand-new material) and revisit compute targets, deployment types, traffic splitting versus mirroring, monitoring signals and evaluator categories. On exam day, read each question for what it is truly asking, eliminate wrong options first, and watch for wording that implies constraints such as least privilege, lowest cost, no client disruption, or without storing secrets. In this exam the constraint usually is the answer key.

Worth knowing before you sit down:

• Microsoft Learn is available during the exam in a split screen. Use it to check details you genuinely need: the timer keeps running, and the exam is deliberately too long to look up every answer. Browsing is limited to Microsoft Learn itself (no Q&A, practice assessments, or profile), and personal notes and other websites are unavailable.
• You can take unscheduled breaks, but the clock keeps running and you cannot return to questions you already saw.
• Case studies lock when you leave them: finish each one before moving on, because you cannot revisit its questions afterwards.
• Labs may appear: AI-300's 120-minute allotment is the one Microsoft uses for role-based exams that may contain labs. The introduction screen tells you whether yours does; if it does, budget extra time for them.
• Try the exam sandbox beforehand so the question formats and interface hold no surprises: Exam Sandbox

 

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Additional Learning Resources