AI-300 Study Guide
A curated learning path for the AI-300 exam: the best documentation, videos, blog posts and tutorials for every exam objective.A curated learning path for the AI-300 exam: the best documentation, videos, blog posts and tutorials for every exam objective, in the order worth studying them.
Microsoft Certified: Machine Learning Operations Engineer Associate
Before You Start Studying
Before diving into the study sections, review the prerequisites and recommended background knowledge below to understand what will help you succeed.
Recommended Exam Path
AI-300 has no official prerequisites and no required prior exam.
If you are completely new to Azure AI, AI-900: Microsoft Certified – Azure AI Fundamentals is a gentle on-ramp for terminology and the Azure AI service landscape — but it is optional, and candidates who already work with Azure Machine Learning or Microsoft Foundry can go straight to AI-300.
If you come from a data science background, expect a shifted focus compared to modeling-centric exams: less theory, more operating models and AI applications reliably in production — deployment, automation, monitoring, and evaluation.
Prerequisites
There are no strict prerequisites to start AI-300 preparation, but the exam assumes practical exposure rather than just reading.
You should be comfortable with:
- Python (reading and adapting training and scoring scripts)
- The Azure Machine Learning studio, CLI v2, and SDK v2 at a basic level
- Entry-level DevOps practice: Git, GitHub Actions workflows, working in a terminal
- Navigating Azure: resource groups, managed identities, role assignments
Recommended Background Knowledge
AI-300 tests operations judgment, not data science theory.
Azure Machine Learning Fundamentals
- What a workspace is and the resources created with it
- Compute target types: compute instance, compute cluster, serverless, attached Kubernetes
- Data assets, datastores, environments, and jobs
MLOps Basics
- The model lifecycle: experiment tracking with MLflow, model registration and versioning
- Online endpoints vs batch endpoints
- Why you monitor models after deployment (drift, production performance)
Generative AI and Microsoft Foundry Awareness
- Foundation model deployments and their billing shapes (pay-per-token vs provisioned throughput)
- Retrieval-augmented generation at a concept level
- Why generative applications need evaluation and content safety
DevOps and IaC Awareness
- What GitHub Actions workflows do
- Service principal vs OpenID Connect federated authentication
- Reading Bicep templates and Azure CLI commands
Step-by-Step Study Guide
Step 1: Review the Official Study Guide
What to do:
- Open the official AI-300 study guide
- Read the skills measured sections
- Note any topics that are new to you
- Use it as your checklist throughout your prep
Skills at a glance:
- Design and implement an MLOps infrastructure (15–20%)
- Implement machine learning model lifecycle and operations (25–30%)
- Design and implement a GenAIOps infrastructure (20–25%)
- Implement generative AI quality assurance and observability (10–15%)
- Optimize generative AI systems and model performance (10–15%)
Note the weighting: the two Azure Machine Learning domains together are 40–50% of the exam, and the three generative AI domains are the other half. You cannot pass on either half alone.
Link to The Official Study Guide
Step 2: Schedule Your Exam
What to do:
- Choose a date that gives you enough time for study and practice
- Schedule the exam through the official Microsoft certification page
- Put the date on your calendar and plan backwards
Recommended timing:
- If you already work with Azure Machine Learning or Foundry weekly: 2 to 4 weeks
- If MLOps and generative AI operations are new to you: 4 to 8 weeks
Certification and Exam Details Page
Step 3: Go Through the Official Learning Path
What to do:
- Complete the two learning paths from the official AI-300T00 course
- Take notes on concepts you cannot explain in simple terms
- Flag areas that require additional hands-on practice
The course maps exactly onto the exam's two halves — one learning path per half:
Official Learning Path Course Page
Step 4: Watch the Best Available Video Deep Dives
AI-300 is a young exam and no dedicated full video course exists yet. Until one appears, these cover the exam's core ground at real depth:
What to do:
- Watch John Savill's Azure AI Foundry Overview (~1.5 hours) — model catalog, deployment options, evaluations, tracing, fine-tuning, and safety: the GenAIOps half of the exam
- Watch freeCodeCamp's MLflow MLOps course — experiment tracking, model registry, and deployment; Azure Machine Learning is MLflow-native, so the concepts transfer directly
- Pause and replicate key actions in your own Azure environment
- Write down any topics where you feel uncertain
Tips:
- Treat this as both learning and revision
- These are topic deep dives, not exam crams — pair them with your Step 1 checklist so you always know which skill area you are covering
John Savill: Azure AI Foundry Overview
freeCodeCamp: Learn MLOps with MLflow
Step 5: Get Hands-On Practice
This is the step that decides the exam. 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, self-paced lab repositories for the AI-300 course, mirroring the two learning paths:
- MLOps labs — 8 challenges: AutoML, MLflow-tracked training, sweep jobs, pipelines, GitHub Actions CI/CD, managed online endpoint deployment and monitoring
- GenAIOps labs — 7 exercises: Foundry setup, prompt management and versioning, automated cloud evaluation, monitoring and distributed tracing, fine-tuning comparison
Make sure you cover: compute target selection, blue/green traffic splitting and mirrored traffic, model monitoring with ground truth data, Foundry deployment types, running an evaluation, and reading traces.
A free Azure trial or a low-budget subscription is enough — compute clusters with minimum nodes 0 and serverless compute keep lab costs near zero when idle.
Step 6: Benchmark Your Knowledge
What to do:
- Use CertiAce to benchmark your readiness
- Practice time management and case studies
- Take Microsoft's free official Practice Assessment — for AI-300 it lives on AI Skills Navigator (sign-in required), linked from the certification page
- Try the exam sandbox so the question UI holds no surprises on exam day
Recommended target:
- Aim for consistent performance, not one lucky high score
- If a topic is unstable, return to learning + hands-on
Step 7: Take the Exam
The day before:
- Review your weak topics only
- Revisit the operational decision points: compute targets, deployment types, traffic splitting vs mirroring, monitoring signals, evaluator categories
- Avoid learning brand new topics
On exam day:
- Read questions carefully and identify what they are truly asking
- Eliminate wrong options first
- Watch for wording that implies constraints such as least privilege, lowest cost, no client disruption, scale to zero when idle, or without storing secrets — in this exam the constraint usually IS the answer key
- When two options both seem to work, pick the one Microsoft documents as the purpose-built mechanism for the stated requirement
Additional Learning Resources
Blogs
- https://ravikirans.com/ai-300-mlops-generative-ai-study-guide/
- https://k21academy.com/azure-aiml/microsoft-ai-300-certification/
- https://techcommunity.microsoft.com/blog/skills-hub-blog/new-certification-for-machine-learning-operations-mlops-engineers/4494111
YouTube
Microsoft Reactor Series
- https://developer.microsoft.com/en-us/reactor/series/s-1485/
- https://developer.microsoft.com/en-us/reactor/series/s-1655/
Other Resources
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