AI-300 exam domains
The AI-300 exam is weighted across 5 domains. Pick any domain below to drill it — or read the full breakdown in the FAQ.
| Exam domain | Exam weight | Practice |
|---|---|---|
| Design and implement an MLOps infrastructure | 19% | Practice this topic |
| Implement machine learning model lifecycle and operations | 29% | Practice this topic |
| Design and implement a GenAIOps infrastructure | 24% | Practice this topic |
| Implement generative AI quality assurance and observability | 14% | Practice this topic |
| Optimize generative AI systems and model performance | 14% | Practice this topic |
Sample AI-300 questions
A sample of the AI-300 questions on this hub. Each links through to the full question, the correct answer, and an explanation of why every other option is wrong.
- You manage several registered models in an Azure Machine Learning workspace. A fraud-detection model has 12 versions, and versions 1 through 8 are obs…View question
- Your team's security policy prohibits storing storage account access keys or SAS tokens anywhere in the Azure Machine Learning workspace. Data scienti…View question
- Your team provisions a new Azure Machine Learning workspace inside a resource group named 'rg-ml-prod' that also contains the associated storage accou…View question
- Your team runs Azure Machine Learning training jobs that read from an Azure Blob Storage account. Security policy prohibits storing storage account ke…View question
- Your team stores large training datasets in an Azure Data Lake Storage Gen2 account that has hierarchical namespace enabled. You need to register this…View question
- Your team has an existing Azure Blob Storage account that already contains curated training data in a container named 'training-data'. You need to mak…View question
- You are training a classification model in Azure Machine Learning using a command job that logs a validation metric called 'val_auc' with MLflow. You…View question
- Your team maintains Azure Machine Learning infrastructure using Bicep templates stored in a GitHub repository. You need to configure a GitHub Actions…View question
- You maintain a fraud-detection model deployed to a managed online endpoint in Azure Machine Learning. A model monitor already computes prediction drif…View question
- You are configuring an automated machine learning classification job in Azure Machine Learning to predict fraudulent transactions. Only about 2% of th…View question
Key AI-300 terms
Start with these terms, then explore the full glossary. Each links to a plain-English definition written for the AI-300 exam.
AI-300 frequently asked questions
What is the AI-300 certification?+
Microsoft positions the Machine Learning Operations Engineer as having expertise setting up infrastructure for machine learning operations (MLOps) and generative AI operations (GenAIOps), together referred to as AI operations (AIOps).
It expects experience training, deploying, and maintaining ML models with Azure Machine Learning and operating generative AI apps with Microsoft Foundry, using Python, GitHub Actions, Bicep, and Azure CLI.
What topics are on the AI-300 exam?+
The AI-300 exam is organised into five weighted domains. The percentages below are the midpoints of Microsoft’s published ranges (for example 25–30%), normalized to total 100, so treat them as a study-time guide. The machine learning model lifecycle carries the most weight.
Design and implement an MLOps infrastructure (19%)
Covers creating and managing Azure Machine Learning workspace resources (datastores, compute, identity) and assets (data, environments, components, registries), and implementing infrastructure as code with Bicep, Azure CLI, and GitHub Actions.
Implement machine learning model lifecycle and operations (29%)
The largest domain. Covers orchestrating model training (MLflow, automated ML, hyperparameter tuning, pipelines), model registration and versioning, deploying models to real-time or batch endpoints with rollout and rollback, and monitoring for data drift and retraining.
Design and implement a GenAIOps infrastructure (24%)
Covers implementing Foundry environments and platform configuration (identity, RBAC, private networking, Bicep), deploying and managing foundation models for production (endpoints, versioning, provisioned throughput), and prompt versioning with source control.
Implement generative AI quality assurance and observability (14%)
Covers configuring evaluation and validation (test datasets, groundedness/relevance/coherence/fluency, safety evaluations) and implementing observability (monitoring, latency and cost metrics, logging and tracing).
Optimize generative AI systems and model performance (14%)
Covers optimizing RAG performance and accuracy (chunk sizes, similarity thresholds, embedding models, hybrid search, A/B testing) and implementing advanced fine-tuning and model customization with synthetic data.
Is the AI-300 hard?+
AI-300 is an associate exam that expects genuine AIOps skill — automating pipelines, wiring CI/CD, evaluating generative apps, and optimizing RAG and fine-tuning — not just recognition of features.
The difficulty comes from the breadth across classic MLOps and modern GenAIOps and from scenario questions that hinge on the right infrastructure, evaluation metric, or optimization technique. Building in Azure Machine Learning and Foundry is the key.
How many questions are on the AI-300 exam and how long is it?+
Microsoft does not fix a single public question count for AI-300; it typically presents roughly 40–60 questions in a mix of formats — multiple choice, multiple response, and case studies — and Microsoft allots 120 minutes to complete the assessment.
Our full-length practice mock uses a 60-question, 120-minute session so you can rehearse pacing under realistic time pressure before test day.
What score do you need to pass the AI-300?+
Microsoft scores AI-300 on a scale of 1 to 1,000, and the passing score is 700. That is a scaled score rather than a raw 70%, because Microsoft adjusts for question difficulty, and there is no penalty for wrong answers, so you should never leave a question blank. Our practice mock uses a 70% threshold to give you a comparable target.
How much does the AI-300 exam cost?+
The AI-300 exam fee is set by Microsoft and varies by region — check the Microsoft certification page for current pricing. Microsoft associate certifications are valid for one year and can be renewed for free through an online assessment on Microsoft Learn. Everything on this hub is free.
Who should take the AI-300?+
AI-300 is aimed at MLOps and GenAIOps engineers and data scientists who operationalize ML and generative AI solutions on Azure.
Microsoft recommends a data-science background with Python, entry-level DevOps practices (GitHub Actions, CLIs), and IaC with Bicep and Azure CLI, though there is no formal prerequisite.
What jobs and salaries can the AI-300 lead to?+
AI-300 maps to roles such as MLOps engineer, machine learning engineer, and AI platform engineer responsible for deploying and operating models and generative AI applications.
How much any certification affects pay depends heavily on geography, seniority, and hands-on experience, so treat any single salary figure with caution. AI-300 is best viewed as validation of AIOps skill rather than a guaranteed raise on its own.
How long does it take to study for the AI-300?+
Candidates with MLOps or data-science experience often need four to eight weeks; those newer to Azure Machine Learning or Foundry should plan longer. The most efficient path is to study each domain while automating training and deployment pipelines and building evaluations.
Review every explanation, including for questions you answered correctly, because AI-300 distractors are built from plausible but incorrect operational choices. Use the per-domain results here to find and shore up your weakest area, then finish with full-length timed mocks.
How should you prepare for the AI-300?+
Study the five domains above, giving the heaviest weight to the model lifecycle and GenAIOps infrastructure, then drill scenario questions domain by domain. Every MockAPI question reveals a full explanation and tells you why each wrong answer is wrong — essential for an exam that turns on exact MLOps and GenAIOps choices.
When you can answer scenarios comfortably, move to full-length timed mocks alongside hands-on practice in Azure Machine Learning and Foundry. Use the glossary to keep concepts like MLflow, data drift, and groundedness straight, and aim to score consistently above the pass mark before you book.
Can you take the AI-300 exam online?+
Yes. Microsoft delivers AI-300 through Pearson VUE, so you can test at a physical Pearson VUE centre or online with remote proctoring (OnVUE). The online exam requires a private, quiet room, a clear workspace, a webcam and microphone, a stable connection, and government-issued photo ID, with a proctor monitoring you and a room scan before you start.
If you do not pass, Microsoft requires a 24-hour wait before a second attempt; after that, a 14-day wait applies between further attempts, with a limit of five attempts in a 12-month period, and each attempt needs its own registration.
What certification should you take after the AI-300?+
After AI-300, common next steps include the AI Apps and Agents Developer (AI-103) for the build side, or the Azure Solutions Architect Expert (AZ-305) for broader Azure design.
For many, the real next step is owning ML and generative AI operations in production. Pairing AI-300 with hands-on experience is what turns the certificate into a career.