AI-300: Machine Learning Operations Engineer Associate Exams
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Prepare for the AI-300 exam: Operationalizing Machine Learning and Generative AI Solutions
Prepare for the AI-300 exam, Operationalizing Machine Learning and Generative AI Solutions, with 6 practice tests of 250 exam style questions each — 1,500 questions in total — built to match the format and depth of the live exam. Passing AI-300 earns the Microsoft Certified: Machine Learning Operations Engineer Associate credential, the successor to the retired DP-100.
Exam Domain Coverage
Each AI-300 practice test mirrors the real exam blueprint and its five weighted domains:
Design and implement an MLOps infrastructure (19%)
Implement machine learning model lifecycle and operations (30%)
Design and implement a GenAIOps infrastructure (24%)
Implement generative AI quality assurance and observability (14%)
Optimize generative AI systems and model performance (13%)
You will practice the same tooling the AI-300 exam covers: Azure Machine Learning, Microsoft Foundry, MLflow, GitHub Actions, Bicep, and the Azure CLI, alongside RAG optimization, fine-tuning, and AI evaluation and observability.
Detailed Explanations
Every AI-300 question includes a detailed explanation. You get the reasoning behind the correct answer, a clear note on why each distractor is wrong, and references to the official Microsoft Learn documentation so you can study further at the source. This turns each attempt into a focused study session rather than just a score.
Exam Format
The real AI-300 exam runs 120 minutes with a passing score of 700 and is delivered in English, and it targets MLOps and GenAIOps engineers who already work with Azure Machine Learning and Python. Use these AI-300 practice tests to find your weak areas, close them, and build the confidence to pass on your first attempt.
These practice tests are unofficial and are not affiliated with or endorsed by Microsoft.
A data-science background with hands-on experience training and evaluating machine learning models
Working knowledge of Python and familiarity with Azure Machine Learning
Entry-level DevOps skills, including basic CI/CD, source control, and the command line
Work through 1,500 exam-style AI-300 questions across 6 full-length practice tests of 250 questions each, with detailed explanations for every item
Design and implement MLOps and GenAIOps infrastructure on Azure using Bicep, the Azure CLI, and GitHub Actions for repeatable, automated deployments
Manage the machine learning model lifecycle with Azure Machine Learning and MLflow: training, registration, deployment, and monitoring in production
Build and optimize generative AI systems with Microsoft Foundry, RAG, and fine-tuning, applying AI evaluation and observability for quality assurance
MLOps and GenAIOps engineers preparing for the Microsoft AI-300 certification exam
Data scientists and machine learning engineers moving into model operations and deployment on Azure
DP-100 certified professionals upgrading to the new Machine Learning Operations Engineer Associate credential
