AI-300: Machine Learning Operations (MLOps) Engineer Associate

Pick how to study

• Try 10 free AI-300 questions
• AI-300 mock exam: timed, you pick the length
• AI-300 flashcards: spaced repetition
• AI-300 decision sheets: look-alike services, settled

The AI-300 exam

• Questions: 311

What AI-300 covers

Every question and flashcard is mapped to the AI-300: Machine Learning Operations (MLOps) Engineer Associate skills outline so you only revise what is on the exam.

• 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
• Optimize generative AI systems and model performance

Why earn the AI-300 certification in 2026

AI-300 is Microsoft's associate-level certification for machine learning operations engineers. Microsoft expects candidates to have subject matter expertise setting up MLOps and generative AI operations infrastructure on Azure, with a data science background in Python and entry-level DevOps experience. There is no prerequisite certification. The exam covers MLOps infrastructure, the machine learning model lifecycle, GenAIOps infrastructure, generative AI quality assurance, and optimising generative AI systems.

Who should sit this exam

MLOps engineers productionising Azure Machine Learning workloads, data scientists shipping models to endpoints, platform engineers building ML pipelines, and DevOps professionals automating ML lifecycle workflows.

Exam tips

• Master Azure Machine Learning pipelines, environments, and component-based authoring
• Know managed online and batch endpoints, blue-green deployment, and traffic splitting
• Understand model monitoring: data drift, prediction drift, and performance degradation alerts
• Review CI/CD with Azure DevOps and GitHub Actions for ML, plus responsible AI dashboards

AI-300 study resources

• Build a study plan: tell us your exam date and we schedule daily practice and review sessions backwards from the date.
• Decision sheets: each sheet settles one choice between look-alike services, verdict first, then the comparison, then the traps.

Frequently asked about AI-300

What does the AI-300 exam cover?

AI-300 covers operationalising machine learning workloads on Azure: building Azure Machine Learning pipelines, deploying managed endpoints, monitoring models for drift, and applying CI/CD and responsible AI practices to ML projects.

How long should I study for AI-300?

Most candidates need 8 to 12 weeks given the breadth of MLOps tooling involved. Prior experience with Azure Machine Learning and a CI/CD platform such as Azure DevOps or GitHub Actions shortens that significantly.

Is AI-300 worth it?

AI-300 fills the gap between AI-102 (AI engineering) and DP-100 (data science) by validating production ML skills. As more organisations move models out of notebooks, MLOps engineers are among the harder Azure roles to hire for.

What is the passing score for AI-300?

700 out of 1000.

How many questions are on AzurePrep for AI-300?

AzurePrep covers AI-300 with practice questions written to the published skills outline, spanning Azure Machine Learning, model deployment, monitoring, CI/CD for ML, and responsible AI. The current question count is shown on the AI-300 landing page.

Ready for AI-300?

Try the practice surface for free. Answer a few questions, see the spacing loop, then decide. Try 10 free AI-300 questions or browse the catalogue.