Exam Details
Microsoft · AI-300
Deploy, monitor, and manage machine learning models using MLOps practices on Azure.
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Who is this for?
Level: Intermediate. This exam focuses on operationalizing machine learning and generative AI solutions on Azure. While there are no formal prerequisites, Microsoft officially recommends having a data science background with practical experience in Python programming. You should be comfortable training, optimizing, and deploying models using Microsoft Foundry, alongside basic DevOps practices like using GitHub Actions and CLIs.
Are you ready?
You are fully prepared if you can actively build, deploy, and manage production-grade AI models while explaining how they integrate into real-world business applications. Challenge your MLOps and GenAIOps skills with our scenario-based quizzes!
Overview
The AI-300 certification focuses on MLOps practices within the Azure ecosystem, covering how machine learning models are deployed, monitored, and maintained in production environments. It is designed for professionals who work at the intersection of data science and DevOps, ensuring that models move from experimentation to reliable, scalable production systems. The exam tests your understanding of model lifecycle management, including versioning, monitoring, retraining, and performance tracking. It also covers pipelines, automation, and integration with Azure Machine Learning services. Candidates are expected to understand how to operationalize models, handle drift, and maintain consistency across environments. This certification is particularly relevant as organizations move beyond building models to deploying them in real-world applications. Many teams struggle with maintaining models over time, and MLOps addresses this gap by introducing structured processes and tooling. Professionals with this certification typically work as MLOps engineers, ML engineers, or cloud engineers supporting AI workloads. As adoption of machine learning increases across industries, the need for operational expertise around ML systems continues to grow steadily.
FAQ
This certification is designed for MLOps engineers, ML engineers, and cloud engineers who specialize in operationalizing machine learning workloads on Azure. While there are no formal required certifications to sit for the exam, candidates should have:
The AI-300 exam typically consists of 40–60 questions. You are generally given 100 to 120 minutes to complete the assessment. The question types include:
The exam focuses on both traditional MLOps and emerging GenAIOps practices. The topics are distributed as follows:
To pass the AI-300 exam, you must achieve a scaled score of at least 700 out of 1000. Any score below this mark is considered a fail. Scaled scores are used to maintain consistency across different versions of the exam that may vary slightly in difficulty.
Preparation should involve a mix of theoretical study and hands-on practice. High-value resources include:
The standard cost for the AI-300 exam is $165 USD. However, pricing varies based on your geographic location and local currency. Some professionals may be eligible for discounts through:
If you do not pass the AI-300 exam on your first attempt, you must wait at least 24 hours before retaking it. For subsequent attempts:
The Microsoft Certified: Machine Learning Operation (MLOps) Engineer Associate certification is valid for one year from the date you pass the exam. To maintain the certification:
As organizations move from experimental AI to production-grade applications, the demand for operational expertise is surging. Holding this certification positions you for several high-growth roles:
After mastering MLOps on Azure, you can further specialize or broaden your expertise with these related credentials: