Microsoft Certified: Machine Learning Operations Engineer Associate

What the exam actually covers

This is an operations exam. The published domains are designing and implementing an MLOps infrastructure, implementing the machine learning model lifecycle, designing and implementing a GenAIOps infrastructure, implementing generative AI quality assurance and observability, and optimising generative AI systems and model performance. Microsoft groups the last three under the term AI operations.

That scope is wider than the Azure Data Scientist Associate it replaces. DP-100 was built around training and deploying models with Azure Machine Learning. This paper keeps that ground and adds the generative side: deploying, evaluating, monitoring and optimising generative AI applications and agents on Microsoft Foundry, with observability treated as a domain in its own right.

The toolchain is specific and worth reading closely before you commit. Microsoft names Azure Machine Learning, Microsoft Foundry, GitHub Actions, and infrastructure as code with Bicep and the Azure CLI. It also states an expectation of entry-level DevOps understanding including command-line work, so this paper reaches further toward platform engineering than a data science credential normally does.

The sitting runs 120 minutes, is proctored, and may include interactive components. Microsoft does not publish a question count, and we record that as unpublished rather than estimating one.

Who it suits

People who already keep models running in production, and data scientists whose role has drifted toward operations. If you write the pipeline, the deployment and the monitoring rather than only the model, this maps onto the work.

It is the natural destination for anyone who was aiming at DP-100 before it retired on 1 June 2026. The caveat is the English-only delivery noted below, which is a step back from the ten languages DP-100 offered.

How it compares

The closest comparison is the Databricks Machine Learning Professional, which also weights MLOps heavily and also assumes production experience. The difference is the platform and the generative AI coverage: Databricks tests its own lakehouse tooling, while this adds an explicit GenAIOps and observability component that the Databricks paper does not carry.

Against the AWS Machine Learning Engineer - Associate, the two are closely matched in intent. Both are operations-focused associate exams covering deployment, monitoring and retraining. AWS runs a three-year term against this one-year term, and the AWS paper has also recently widened into generative and agentic AI.

Against the Google Cloud Professional Machine Learning Engineer, Google's sits at a higher level of experience and spans more of the modelling lifecycle.

Within Azure, this and Azure AI Apps and Agents Developer Associate divide the old ground between them: that one builds the applications, this one operates them.

What it costs to hold

Microsoft does not publish a price on the certification page, stating only that it depends on the country or region where the exam is proctored. Microsoft's exam FAQ gives US$165 as the typical associate figure, so that is what we record at medium confidence, before local taxes.

The term is one year, renewed free through a short unproctored open-book assessment within a six-month window before expiry. The money cost is low and the attention cost is annual.

Before you book

Check the language. The exam is currently offered in English only, which is the main practical constraint on this credential and the clearest difference from the certification it replaces.

Check the AI-300 study guide before buying preparation material. The exam went to beta in March 2026 with general availability expected in May, so third-party courses may still be written against DP-100, which does not cover the GenAIOps half of this syllabus.