It assumes real hands-on experience — MLflow, Bicep/Azure CLI, RBAC, RAG tuning, fine-tuning lifecycle management — not just conceptual familiarity. Candidates without production MLOps experience typically need more time on the tooling than on the underlying ML theory.

AI-300 · Associate

AI-300 Study Guide 2026: How to Pass the Machine Learning Operations Engineer Exam

Q: Is AI-300 about building machine learning models?

No — AI-300 tests operating models and generative AI systems that already exist: deploying, monitoring, securing, and optimizing them in production (MLOps and GenAIOps). Model design and core data science are not the focus.

Q: How many questions are on the AI-300 exam, and how long is it?

About 40 questions in 100 minutes — roughly 2.5 minutes per question, which is workable for scenario-based items if you recognize the pattern quickly.

Q: What is the passing score for AI-300?

You need 700 out of 1000 on Microsoft's scaled scoring system. Treat it as a target rather than a literal percentage, since scaling shifts with question difficulty.

Q: Does AI-300 have a prerequisite certification?

No prerequisite is listed on Microsoft's official AI-300 pages — you can register and sit the exam directly.

Domain weights straight from Microsoft's official AI-300 study guide, and a week-by-week plan built around the domain that actually decides pass or fail: model lifecycle and operations.

TL;DR:AI-300 is Microsoft's Associate-level exam for the Machine Learning Operations Engineer credential — about 40 questions, 100 minutes, a 700+ passing score, $165 USD. It tests operating ML and generative AI systems already built, not designing them: MLOps infrastructure (15-20%), ML model lifecycle and operations (25-30%, the largest domain), GenAIOps infrastructure (20-25%), generative AI quality assurance and observability (10-15%), and generative AI optimization (10-15%).

What Is the AI-300 Exam?

AI-300 is the required exam for Microsoft Certified: Machine Learning Operations Engineer Associate. Microsoft's official study guide describes the audience as someone with a data science background, Python experience, and an entry-level understanding of DevOps practices — this is explicitly an operations role, not a data science or model-design role.

You're expected to train, optimize, deploy, and maintain traditional machine learning models with Azure Machine Learning, and to deploy, evaluate, monitor, and optimize generative AI applications and agents with Microsoft Foundry — together referred to as AI operations (AIOps). Expect MLflow, Bicep, Azure CLI, GitHub Actions, and RBAC to show up directly in scenario questions, not just as background knowledge.

AI-300 Exam Domains and Weightings

Domain	Weight	What it tests
MLOps infrastructure	15-20%	Workspace resources, identity/access, IaC with Bicep and Azure CLI, GitHub integration
ML model lifecycle and operations	25-30%	Training orchestration with MLflow, registration/versioning, production deployment, drift monitoring
GenAIOps infrastructure	20-25%	Foundry environment security, foundation model deployment, prompt versioning with Git
GenAI quality assurance & observability	10-15%	Groundedness/relevance/coherence/fluency metrics, cost and performance monitoring
GenAI optimization	10-15%	RAG tuning, hybrid search, advanced fine-tuning and model customization

Where to put your study hours

Model lifecycle and operations alone is worth more than the two smallest domains combined — weight your time accordingly rather than splitting it evenly across five domains:

Domain	Suggested hours (of ~35 total)
MLOps infrastructure (15-20%)	~6 hours
Model lifecycle & operations (25-30%)	~10 hours
GenAIOps infrastructure (20-25%)	~8 hours
Quality assurance & observability (10-15%)	~5 hours
Optimization (10-15%)	~6 hours

AI-300 Study Plan: Week by Week

This plan front-loads model lifecycle and operations — the largest domain — right after the infrastructure basics, since most of the GenAIOps and optimization content builds on the same deployment and monitoring patterns.

Week 1· MLOps Infrastructure

Workspace resources and assets, RBAC scoping, Bicep/Azure CLI deployment, GitHub integration and Actions, network restriction.

Week 2· Model Lifecycle

MLflow experiment tracking, AutoML, hyperparameter tuning, model registration, real-time vs. batch endpoints, progressive rollout and rollback.

Week 3· GenAIOps Infrastructure

Foundry environment security (managed identity, RBAC, private networking), serverless vs. provisioned-throughput deployment, prompt versioning with Git.

Week 4· Quality, Observability & Optimization

Groundedness/relevance/coherence/fluency metrics, cost and performance monitoring, RAG tuning, fine-tuning lifecycle management.

Week 5 (buffer)· Review

Full-length practice questions across all five domains, revisit any domain scoring under 80%.

Best Study Resources for AI-300

Microsoft's own documentation covers the product surface the exam draws from — start there before adding anything paid:

Azure Machine Learning documentation — MLOps infrastructure and model lifecycle
Azure AI Foundry documentation — GenAIOps, deployment, and observability

For exam-style practice, MSCertQuiz maintains a full AI-300 practice test (40 questions free, no card required, 500 total) weighted against the same domains above — see the practice questions and cheat sheet that go with this guide.

Exam Day Tips for AI-300

At roughly 2.5 minutes per question, recognize the scenario pattern (drift detection, rollback, provisioned throughput vs. serverless) rather than re-deriving the right answer from first principles.
When a scenario mentions a "known, fixed" workload volume, that's almost always a provisioned-throughput question, not a serverless one — the two are frequently confused.
If a choice fixes the symptom but skips the built-in tool made for the job (MLflow, RBAC, IaC), it's the distractor — this exam tests whether you'd use the platform correctly, not just whether something technically works.
Groundedness, relevance, coherence, and fluency sound similar but test different failure modes — don't guess between them under time pressure; know the definitions cold.

MSCertQuiz sells practice-exam access for AI-300 and other Microsoft certifications; this guide is written by the same team that builds those questions. The official Microsoft Learn resources above are what you need at minimum, free.

Common Questions

Is AI-300 about building machine learning models?

No — it tests operating models and generative AI systems already built: deploying, monitoring, securing, and optimizing them in production. Model design isn't the focus.

Is AI-300 hard?

It assumes real hands-on experience with MLflow, IaC, RBAC, and RAG tuning — candidates without production MLOps experience usually need more time on the tooling than the theory.