AI-300 Practice Exam - MLOps Engineer | Free Practice Questions

AI-300 validates your skills as an MLOps engineer responsible for operationalising machine learning workloads on Azure. As organisations move ML projects from notebooks into production, engineers who can build CI/CD for models, monitor drift, and manage Azure Machine Learning at scale are increasingly valuable. The exam emphasises automation, governance, and responsible AI in production.

Who should take AI-300

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.

What AI-300 covers

  • MLOps
  • Azure Machine Learning
  • Model Deployment
  • Model Monitoring
  • CI/CD for ML
  • Responsible AI

Study tips for AI-300

  • 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 question bank

AzurePrep includes 311 AI-300 practice questions written to the published Microsoft skills outline. Questions span the full exam domain, not a recycled dump. Every question includes a detailed explanation and documentation reference so you understand why each answer is correct.

Frequently asked questions

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.

AzurePrep is an independent practice-exam platform and is not affiliated with or endorsed by Microsoft.