Directory / Microsoft

Microsoft Certified: Machine Learning Operations Engineer Associate

An operations exam split between classical MLOps and GenAIOps: deploying foundation models, prompt versioning, evaluation metrics, tracing and RAG tuning.

What it signals

Practical assessment: Includes interactive, code or case-study items. Proctored, in the 120-minute category that may contain labs, with interactive items.
Identity assurance: Proctored with identity verification. Proctored, scheduled through Pearson VUE (official). Online (OnVUE) or test-centre choice is referenced in official support pages about online proctored exams and third-party guides but was not confirmed per exam. Microsoft Learn accessible during the exam. English only at time of research.
Depth: Associate. Associate level.
Experience expected: About 1 year. Expects a data science background plus DevOps basics; no official year count.
Portability: Mixed. MLflow, Git, GitHub Actions and MLOps practice transfer; Azure ML/Foundry specifics do not.
Verifiability: Public badge or registry. Credential is shareable via a Microsoft Learn verification link/transcript carrying an 'Online Verifiable' tag that an employer can open (profile must be public).

For hiring managers

Only current Microsoft credential with substantial classical ML content now that DP-100 is retired, but it tests operating models more than building them.
Roughly half the blueprint is GenAIOps: foundation model deployment, prompt versioning, evaluation metrics (groundedness, relevance), tracing, RAG tuning and fine-tuning.
URL slug differs from the certification name, which can confuse link checks.

Verifying it

Candidate shares a link from their Microsoft Learn profile: either a per-credential Share link (credentials carry an 'Online Verifiable' tag) or a transcript share link / PDF. The Learn profile must be in public view for the shared credential to be verifiable. Retired credentials remain shareable and stay on the transcript.

What it covers

Published exam outline

Design and implement an MLOps infrastructure 15-20%
Implement machine learning model lifecycle and operations 25-30%
Design and implement a GenAIOps infrastructure 20-25%
Implement generative AI quality assurance and observability 10-15%
Optimize generative AI systems and model performance 10-15%

By topic

GenAI apps: Substantial (20 to 35%). Prompt management, evaluation, RAG optimisation and model selection account for roughly 25-30% across the three GenAI domains.
Agents: Touched on (under 10%). Agents appear only as something to evaluate and monitor.
Classical ML: Some (10 to 20%). Training orchestration, AutoML, hyperparameter tuning and fine-tuning total roughly 15-20%.
Operations: Core focus (over 35%). Infrastructure, deployment, rollout, drift monitoring and observability are the core, well over 35%.
Data: Touched on (under 10%). Datastores, data assets and test/synthetic datasets are a small share.
Responsible AI: Touched on (under 10%). Responsible AI model evaluation, safety evaluations and access/network security are a few bullets.

Exam details

Who the provider says it is for. Engineers setting up MLOps and GenAIOps infrastructure on Azure who work with data scientists and DevOps teams. Role: AI Engineer.

Format: Microsoft does not publish per-exam formats. Official question types across the programme: multiple choice, drag and drop, build list, hot area, active screen, case studies, and (on some role-based exams) labs. Exam page states: 'This exam will be proctored. You may have interactive components to complete as part of this exam.' 120 minutes matches the category that may contain labs.
Delivery: Proctored, scheduled through Pearson VUE (official). Online (OnVUE) or test-centre choice is referenced in official support pages about online proctored exams and third-party guides but was not confirmed per exam. Microsoft Learn accessible during the exam. English only at time of research.
Code or hands-on work: Objectives involve Python, MLflow, Bicep, Azure CLI and GitHub Actions workflows, so reading code/config is expected. Labs possible but unconfirmed.
Prerequisites: None
Recommended experience: Data science background, Python programming, entry-level DevOps (GitHub Actions, CLIs), experience with Azure Machine Learning, Microsoft Foundry, Bicep and Azure CLI.
Renewal: Free, online, UNPROCTORED, open-book renewal assessment on Microsoft Learn; six-month eligibility window before expiry; unlimited attempts until expiry; each pass extends by one year. If it lapses, the full exam must be retaken.
Price notes: USD figure is from third-party sources; Microsoft prices vary by country.

Preparing

Not confirmed

These details were not available from an official source at the time of research.

priceUsd (official pages only say price depends on country; USD figure is from third-party sources)
questionCount (not published per exam)
exact question formats for this specific exam (Microsoft does not disclose)
online-vs-test-centre delivery options were not confirmed on this exam's own page
exact GA date
whether the live exam contains labs
official statement that AI-300 replaces DP-100

Sources (11)