Exam Details
Microsoft · AI-300
Prepare for AI-300: Operationalize ML and generative AI models using MLOps and GenAIOps on Azure.
Overview
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FAQ
The AI-300 certification validates your ability to operationalize both traditional machine learning models and generative AI solutions on Azure. This associate-level exam targets ML engineers who manage the full lifecycle from training and deployment to monitoring and optimization. You'll demonstrate skills in building MLOps infrastructure, implementing GenAIOps with Microsoft Foundry, evaluating generative AI quality, and optimizing deployed models for cost and performance. As organizations scale ML and generative AI from experiments to production, MLOps engineers who bridge data science and DevOps are critical.
You're a fit for AI-300 if you:
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| Domain | Weight | What This Means |
|---|---|---|
| Design and implement a MLOps infrastructure | 15–20% | Create and manage data assets in Machine Learning Workspace and implement infrastructure as code for ML |
| Implement machine learning model lifecycle and operations | 25–30% | Train, deploy, and manage traditional ML models on Azure Machine Learning, including workspaces, pipelines, and model registries |
| Design and implement a GenAIOps infrastructure | 20–25% | Set up Microsoft Foundry environments, deploy foundation models, and manage prompt versioning and security settings for production |
| Implement generative AI quality assurance and observability | 10–15% | Evaluate generative AI outputs, monitor agent and model behavior in production, and catch quality regressions before users do |
| Optimize generative AI systems and model performance | 10–15% | Tune cost, latency, and accuracy trade-offs for deployed generative AI systems running in production |
You're probably ready if you can:
You might need more prep if:
If this feels too advanced:
If you know the basics but want to build confidence:
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: