Microsoft Machine Learning Operations Engineer Associate
The AI-300 exam is designed for professionals who implement and configure Azure. It validates your ability to design and implement an MLOps infrastructure, implement machine learning model lifecycle and operations, design and implement a GenAIOps infrastructure, implement generative AI quality assurance and observability, and optimise generative AI systems and model performance.
Exam details
- Certification
- Microsoft Machine Learning Operations Engineer Associate
- Exam code
- AI-300
- Level
- Associate
- Duration
- Approximately 100 minutes
- Questions
- Typically 40–60 questions
- Passing score
- 700 out of 1000
- Cost
- USD $165 — typical price; the exact fee varies by country/region and currency, and local taxes may apply.
- Prerequisites
- None required. Python, Azure Machine Learning, Foundry, and basic DevOps experience are expected.
- Languages
- English (additional languages may be available)
- Delivered by
- Pearson VUE
- Renewal
- Free online assessment, every 12 months
Skills measured
Design and implement an MLOps infrastructure (15–20%)
- Create Machine Learning workspaces, datastores, compute, data assets, and environments.
- Share assets with registries and deploy infrastructure with Bicep, Azure CLI, and GitHub Actions.
- Configure identity, network access, and source control for machine learning projects.
Implement machine learning model lifecycle and operations (25–30%)
- Orchestrate training with MLflow, automated ML, pipelines, and hyperparameter tuning.
- Register, version, evaluate, and archive models, including responsible AI checks.
- Deploy real-time and batch endpoints, then monitor drift and trigger retraining.
Design and implement a GenAIOps infrastructure (20–25%)
- Create Foundry projects with managed identities, RBAC, and private networking.
- Deploy foundation models, version them, and configure provisioned throughput.
- Version and compare prompts in Git.
Implement generative AI quality assurance and observability (10–15%)
- Build evaluation datasets and metrics for groundedness, relevance, coherence, and safety.
- Monitor latency, throughput, token cost, tracing, and logging in Foundry.
Optimise generative AI systems and model performance (10–15%)
- Tune RAG retrieval, embeddings, and hybrid search.
- Fine-tune models, manage synthetic data, and promote custom models to production.
Study resources & practice tests
Frequently asked questions
Who should take AI-300?
AI-300 is for MLOps engineers, machine learning engineers, and AI operations specialists. You should already train and deploy models in Azure Machine Learning and run generative apps or agents in Microsoft Foundry. Microsoft replaced DP-100 with AI-300 after DP-100 retired on 1 June 2026.
What is the average salary for a Machine Learning Operations Engineer?
This is a new job title in 2026. Closest published bands are ML engineers and Azure AI engineers: about $120,000 to $165,000 in the United States, about £67,500 median for advertised Azure developer and ML roles in the UK, and about €70,000 to €100,000 in Germany for comparable cloud and AI engineers. There is no single published EMEA average.
Where do I find exam questions to practice?
You will find websites selling exam dumps or brain dumps that claim to be real AI-300 questions. Those materials are not official. Microsoft treats using brain dumps as cheating. It can invalidate your score, revoke certifications you already hold, and ban you from future exams. Microsoft publishes a free Practice Assessment for AI-300 on AI Skills Navigator. Those questions are examples only, not the live exam. You can try the Microsoft exam sandbox to see the question types and timer, but it does not use real exam questions. Practise the skills measured in Azure Machine Learning and Foundry rather than memorising leaked items.
Does the exam include official labs I can use to practice?
Microsoft does not publish a stable list of which certification exams include labs, and a lab can be dropped for a candidate if the live environment is unavailable. Role-based Azure exams such as AI-300 may include performance-based labs in a live cloud environment. You only find out at the start of your sitting. For practice, use Azure Machine Learning, Microsoft Foundry, GitHub Actions, and Bicep in a subscription. Official instructor-led AI-300 training delivered by a Microsoft Learning Partner includes labs. Those labs are for the course, not a copy of the exam. The Microsoft exam sandbox shows traditional question types only. It does not give you the lab virtual machine.