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Microsoft Certified: Machine Learning Operations Engineer Associate — Requirements & Free Practice

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Microsoft Certified: Machine Learning Operations Engineer Associate

Microsoft certification demonstrating Azure machine learning operations and generative AI operations skills.

Licensing authority: Microsoft · Application information

Initial by examination
Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions
Candidate Profile
Official recommended preparation: Azure MLOps and GenAIOps subject-matter expertise, traditional-model and generative-application experience, a data-science background, Python, entry-level DevOps, GitHub Actions, Bicep and Azure CLI. These published experience statements are preserved as preparation context, not invented prior-certification prerequisites.
Dates And Fees
The retrieved current official page actively adopts and schedules AI-300; no official start/end boundary for this credential, route or current English scope is stated. These dates remain null. Price varies by proctoring country/region; no numeric price is asserted.
Delivery And Accommodations
The examination is proctored and may contain interactive components. Microsoft generally offers test-center or online delivery, with actual online availability determined by the provider's scheduling options; do not guarantee an online slot. Request needed accommodations before scheduling, and satisfy the provider's security/system requirements for online delivery.
Language
English is the language currently listed for this examination.
Profile And Identity
Scheduling may prompt sign-in or Learn-profile creation; a personal Microsoft account is recommended. The profile's legal name must match legal ID. Address, phone and contact email are supplied to the provider during scheduling.
Qualification
Pass the AI-300 examination listed on this certification's detail page under Microsoft's certification-earning policy; the study guide requires a score of 700 or greater.
Scheduling
Rolling Pearson VUE appointments through the official certification detail page; no annual application window is asserted. Microsoft permits at most two certification examinations scheduled concurrently through Pearson VUE and scheduling no more than 90 days ahead. Take or cancel an existing appointment before registering another when at that limit.
Scope Boundary
Initial qualification by this examination only. No renewal route, other Microsoft certification direction or state license route is registered.

Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions

Provider
Pearson VUE
Format
Proctored examination; interactive components may occur.
Time limit
120 minutes
Passing score
700 or greater on the technical-examination scaled score of 1 to 1,000; not a percentage-correct threshold.
Examination outline

The authority publishes percentage ranges for each content area. The practice percentages below use the midpoint of each range, normalized to 100%; they are not exact percentages published by the authority. Practice question counts may differ slightly because of rounding.

  • Design and implement an MLOps infrastructure — Official range: 15–20% (Practice allocation: 18.92%)
    • Create and manage resources in a Machine Learning workspace
    • Create and manage assets in a Machine Learning workspace
    • Implement IaC for Machine Learning
  • Implement machine learning model lifecycle and operations — Official range: 25–30% (Practice allocation: 29.73%)
    • Orchestrate model training
    • Implement model registration and versioning
    • Deploy machine learning models for production environments
    • Monitor and maintain machine learning models in production
  • Design and implement a GenAIOps infrastructure — Official range: 20–25% (Practice allocation: 24.32%)
    • Implement Foundry environments and platform configuration
    • Deploy and manage foundation models for production workloads
    • Implement prompt versioning and management with source control
  • Implement generative AI quality assurance and observability — Official range: 10–15% (Practice allocation: 13.51%)
    • Configure evaluation and validation for generative AI applications and agents
    • Implement observability for generative AI applications and agents
  • Optimize generative AI systems and model performance — Official range: 10–15% (Practice allocation: 13.51%)
    • Optimize retrieval-augmented generation (RAG) performance and accuracy
    • Implement advanced fine-tuning and model customization
Examination sources