Use a completed test
A timed exam-ready test is preferred when one is available.
Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate · Microsoft
Choose your next best preparation step
Use your completed test evidence, Personalized Study Plan, and remaining study schedule to choose your next practical step.
✓READYNext step✓Testevidence▤Studyplan→Nextaction
How readiness guidance works
How readiness guidance is formed
Your completed test evidence and study-plan progress are combined to recommend one practical next step.
Check the current plan
Compare the remaining schedule with the tasks still planned in your study plan.
Choose the next action
Practice, reinforce gaps, or continue the plan with fresh evidence.
What readiness means for Operationalizing Machine Learning and Generative AI Solutions
AI-300 readiness means independently making, implementing, and verifying production operations decisions across Azure Machine Learning and Microsoft Foundry. Use recent scenario and hands-on evidence to choose the next study action; a readiness result does not predict passing the exam.
- Provision repeatable ML and generative AI environments, then verify identity, access, networking, compute, and versioned assets against the workload requirements.
- Connect a tracked training run to a registered and evaluated model version, test its serving endpoint, and demonstrate a controlled rollout and rollback.
- Justify a foundation-model deployment and prompt release with workload, capacity, version, and evaluation evidence.
- Distinguish quality and safety findings from latency, throughput, and token-cost signals; use logs and traces to explain an application or agent failure.
- Compare retrieval or customization changes with a measured baseline and representative data, retaining release and monitoring evidence.
- Repeat the decision on a changed scenario after a delay and explain why the closest alternative fails; recognition of a familiar answer alone is insufficient evidence.
Guidance only — not a pass prediction.
AI-300 readiness by exam domain
Review one readiness dimension per AI-300 exam domain, in blueprint order. The 19%, 29%, 24%, 14%, and 14% values below are internal guidance weights totaling 100%, not official Microsoft blueprint percentages. Microsoft publishes separate domain weight ranges in the knowledge model.
| Exam dimension | Weight | Evidence focus |
|---|---|---|
| Design and implement an MLOps infrastructure | 19% | Recreate a secure workspace and a set of versioned reusable assets. |
| Implement machine learning model lifecycle and operations | 29% | Connect tracked training to a registered model, a safe endpoint release, and production monitoring. |
| Design and implement a GenAIOps infrastructure | 24% | Prepare a Foundry environment and manage a controlled model and prompt release. |
| Implement generative AI quality assurance and observability | 14% | Build an evaluation report and diagnose an application or agent trace. |
| Optimize generative AI systems and model performance | 14% | Justify a RAG optimization or customization change through measured comparison. |
What to review before your next practice test
Use these fixed study suggestions from the published Readiness package before you test again.
1Priority 1 — Production model deployment
- Select endpoint types
- Release and recover models safely
2Priority 2 — Generative AI evaluation and safety validation
- Build representative evaluation datasets
- Combine quality and safety evidence
Maintain: RAG retrieval optimization
- Repair the two weakest dimensions.
- Use mixed scenarios requiring evidence and rejected-option explanations.