Review Microsoft Machine Learning Operations Engineer Associate (AI-300) MLOps, GenAIOps, model lifecycle, deployment, monitoring, quality, and observability traps before using the AI-300 practice page.
AI-300 is an operations exam for production AI systems. Use this cheat sheet to separate classic MLOps from GenAIOps before you try the free diagnostic, topic drills, or adjacent live Azure practice banks.
Use this with practice. Review the lifecycle checklist, then open the live AI-300 page for the free diagnostic, topic drills, and related IT Mastery practice paths.
| Field | Detail |
|---|---|
| Issuer | Microsoft |
| Certification name | Machine Learning Operations Engineer Associate |
| Exam code | AI-300 |
| Route focus | Operationalizing machine learning and generative AI solutions |
| Status in IT Mastery | Sample questions with Notify me form |
| Domain | Weight | What to know | Common trap |
|---|---|---|---|
| MLOps infrastructure | 15-20% | Environments, compute, registries, pipelines, source control, and deployment paths | Starting with tooling before defining reproducibility and ownership |
| ML model lifecycle and operations | 25-30% | Training runs, evaluation, registry use, approvals, deployment, rollback, and monitoring | Treating a notebook result as production-ready evidence |
| GenAIOps infrastructure | 20-25% | Prompt workflows, model deployments, retrieval, safety controls, and operational boundaries | Assuming GenAI operations are the same as a normal API release |
| Generative AI quality and observability | 10-15% | Quality evaluation, groundedness, safety, telemetry, tracing, and incident review | Measuring only uptime while ignoring answer quality |
| Generative AI optimization | 10-15% | Latency, cost, prompt quality, retrieval quality, model choice, and evaluation feedback | Optimizing token cost while degrading answer reliability |
| Distinction | How to decide |
|---|---|
| Experiment vs production run | Experiments explore; production runs must be reproducible, traceable, monitored, and approved. |
| Model registry vs artifact storage | A registry tracks versions, stages, lineage, and deployment state; storage only holds files. |
| Deployment vs release | Deployment places a model or app in an environment; release exposes it through controlled traffic, approval, or rollout. |
| Model drift vs data drift | Model drift is degraded prediction behavior; data drift is changed input distribution. |
| MLOps vs GenAIOps | MLOps manages model lifecycle; GenAIOps adds prompt, grounding, response-quality, safety, and observability concerns. |
| Offline evaluation vs production monitoring | Offline evaluation checks before release; production monitoring watches real behavior and incidents. |
| Prompt update vs model update | Prompt changes can alter output without changing model weights; both need review when risk is high. |
On the AI-300 page, tag every miss as infrastructure, lifecycle, GenAIOps, quality, observability, or optimization. If you miss lifecycle questions, drill model registry and release controls. If you miss GenAIOps questions, review prompt, grounding, evaluation, safety, and monitoring boundaries.