Program profile · official sources
Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions
Microsoft Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions assesses MLOps and GenAIOps work on Azure. It covers MLOps infrastructure, the machine-learning model lifecycle, GenAIOps infrastructure, generative-AI observability and performance optimization. Expected background includes data science, Python, entry-level DevOps, GitHub Actions, command-line interfaces, Azure Machine Learning, Microsoft Foundry and infrastructure as code using Bicep or Azure CLI. A score of 700 or greater is required to pass. The reviewed study guide does not state the fee, sitting duration, delivery method or exact credential award name. It separately states that Microsoft associate, expert and specialty certifications expire annually and can be renewed by passing a free online Microsoft Learn assessment.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
- Format
- Proctored certification exam
- Credential
- industry certification
- Prerequisites
- Expected background includes data science, Python, entry-level DevOps, GitHub Actions, CLIs, Azure Machine Learning, Microsoft Foundry and infrastructure as code with Bicep or Azure CLI.
What’s covered
- Design and implement MLOps infrastructure
- Implement machine-learning model lifecycle and operations
- Design and implement GenAIOps infrastructure
- Implement generative-AI quality assurance and observability
- Optimize generative-AI systems and model performance
Prerequisites: Expected background includes data science, Python, entry-level DevOps, GitHub Actions, CLIs, Azure Machine Learning, Microsoft Foundry and infrastructure as code with Bicep or Azure CLI.
What topics are covered on the Exam AI-300?
The Exam AI-300 covers five main domains focused on operationalizing machine learning and generative artificial intelligence on Azure. The largest section is managing the machine learning model lifecycle and operations, comprising 25 to 30 percent of the exam. Building GenAIOps infrastructure accounts for 20 to 25 percent, while designing and implementing MLOps infrastructure makes up 15 to 20 percent. The remaining sections test generative AI quality assurance and observability, at 10 to 15 percent, and optimizing generative AI systems and model performance, also at 10 to 15 percent.
What background knowledge is expected before taking the AI-300 exam?
Candidates taking the Exam AI-300 should have prior experience in data science, Python programming, and entry-level DevOps. Microsoft expects practitioners to be familiar with setting up MLOps and GenAIOps on Azure, which includes training, optimizing, deploying, and maintaining traditional machine learning models. Candidates must also have experience deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Technical proficiency with GitHub Actions, command-line interfaces, Azure Machine Learning, and infrastructure as code utilizing Bicep or the Azure CLI is also expected.
How much does the AI-300 exam cost and what is the required passing score?
To pass Exam AI-300, candidates must achieve a score of 700 or greater. The reviewed Microsoft study guide does not publish the exam fee, sitting duration, delivery method or exact title of the credential awarded. It does state the broader policy that Microsoft associate, expert and specialty certifications expire annually and can be renewed by passing a free online assessment on Microsoft Learn. Because the guide does not name AI-300's exact credential award, that policy is reported as a qualified Microsoft policy rather than assigned to an unstated credential category.
What you actually do
- Design and implement MLOps infrastructure — Exam domain; 15–20%. · Source
- Machine-learning model lifecycle and operations — Exam domain; 25–30%. · Source
- Design and implement GenAIOps infrastructure — Exam domain; 20–25%. · Source
- Generative-AI quality assurance and observability — Exam domain; 10–15%. · Source
- Optimize generative-AI systems and model performance — Exam domain; 10–15%. · Source
What the credential is
- Credential type
- industry certification · Source
- Issuer
- Microsoft · Source
- Expiry and renewal
- Microsoft states that its associate, expert and specialty certifications expire annually and can be renewed by passing a free online assessment on Microsoft Learn; the reviewed source does not name AI-300's exact credential award. · Source
- Exam requirement
- A score of 700 or greater is required to pass. The exact credential award name is not stated in the reviewed source. · Source
- Exam required
- Yes · Source
Who should skip it
- Skip it if you need a beginner introduction; the audience profile expects data-science, Python and entry-level DevOps experience. · Source
- Skip it if you need vendor-neutral operations coverage; the measured skills use Azure Machine Learning and Microsoft Foundry. · Source
- Confirm booking details separately if you require a verified fee, sitting duration or delivery method; this source does not state them. · Source
Alternatives in the registry
Related by topic or level.
- AWS Certified AI Practitioner (AIF-C01) — USD 100 (One initial exam attempt; check applicable regional pricing and taxes. Optional preparation and retakes are not included.); 90 minutes (exam only; not preparation time).
- AI Fundamentals — Check official price; Approximately 9 hours.
- Introduction to Generative AI — Check official price; 45 minutes.
Change log
- : Initial recorded review of official exam, scope, price and credential facts. · Source