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AI-300

Machine Learning Operations Engineer Associate Study Guide

A study path for Microsoft Azure AI-300 Practice Exam — what to know before you start, and the order worth doing things in.

Exam at a glance

Length100–120 min (may include labs)
Questions40–60
Passing score700 / 1000
FormatsMultiple choice, drag-and-drop, case studies, possibly labs
Microsoft Learn accessAvailable (split-screen)
RenewalEvery 12 months, free online assessment
PrerequisiteNone

AI-300: exam domains

Dive deep into each exam objective with comprehensive study materials.

15-20%
Design and Implement an MLOps Infrastructure
Provision Azure Machine Learning workspaces, datastores, and compute, then automate that provisioning with Bicep, the Azure CLI, and GitHub Actions.
25-30%
Implement Machine Learning Model Lifecycle and Operations
Orchestrate training with MLflow, AutoML, and sweep jobs, then register, deploy with safe rollout, and monitor models in production.
20-25%
Design and Implement a GenAIOps Infrastructure
Configure Microsoft Foundry projects, identities, and network isolation, deploy foundation models for production, and version prompts with Git.
10-15%
Implement Generative AI Quality Assurance and Observability
Evaluate quality and safety with Foundry's built-in evaluators, then observe cost, performance, and failures in production with tracing.
10-15%
Optimize Generative AI Systems and Model Performance
Tune RAG retrieval quality with chunking and hybrid search, and apply advanced fine-tuning and synthetic data to customize model behavior.

Where this exam fits

AI-901 · optional groundworkAI-300AI-500 · Multi-Agent AI Solutions Expert (beta)
Download PDF cheatsheet
Complete AI-300 study guide in PDF format

Prerequisites

No formal prerequisite, but the exam assumes experience operating machine learning and generative AI systems in production. AI-901 or AI-103 are useful optional groundwork if you're newer to Azure AI.

Recommended background

  • ML lifecycle concepts — training, versioning, deployment, and monitoring
  • GenAIOps concepts — evaluation, observability, and safety for LLM-based systems
  • Azure Machine Learning basics — workspaces, endpoints, and pipelines
  • Comfort reading Python and interpreting evaluation metrics

Step-by-step study plan

1
Review the official study guide

Open Microsoft's official skills-measured breakdown and use it as your checklist — it's updated whenever the exam changes.

Official AI-300 study guide
2
Schedule your exam

Pick a date through the official Microsoft certification page and plan your study time backwards from it. Give yourself 2–4 weeks if you already use the tech weekly, 4–8 weeks if you're newer to it.

Exam & scheduling page
3
Go through the official learning path

Complete Microsoft's free, self-paced modules for this exam. Take notes on anything you can't explain simply — that's what to revisit.

Official documentation
4
Get hands-on practice

AI-300 rewards recognizing real scenarios and trade-offs, not memorization. Spin up a free-tier environment and actually try the concepts — model deployment, prompt evaluation, monitoring configs — rather than just reading about them.

5
Benchmark your knowledge

Use FabricPrep's AI-300 practice questions to find weak spots, and sit the timed Shield exam to build comfort with exam-day pacing.

Practice AI-300 now — 60 free questions
6
Take the exam

The day before: review only your weak topics, don't cram new material. On exam day: read each question carefully, eliminate wrong answers first, and watch for wording that implies a constraint (least privilege, cost, performance).

Additional resources

Official AI-300 study guide
The authoritative skills-measured breakdown, updated whenever the exam changes
Operationalize generative AI applications
Matches the GenAIOps and quality-assurance domains directly
Azure Machine Learning docs
Canonical reference for the model-lifecycle domain, the largest single weight
Ready to test yourself?

60 realistic AI-300 practice questions with instant feedback and detailed explanations.

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