Track Google Professional Machine Learning Engineer practice status, review exam scope, and request IT Mastery coverage.
Professional Machine Learning Engineer is Google Cloud’s technical ML route for candidates who build, evaluate, productionize, and optimize ML models using Google Cloud technologies and proven ML practices.
This page tracks the Professional Machine Learning Engineer practice-bank rollout for IT Mastery. Dedicated simulator practice is not live yet, but you can review the exam snapshot, topic coverage, and related live AI and ML practice options while coverage is being prioritized.
| Area | Practical focus |
|---|---|
| Architecting low-code ML solutions | Choose managed Google Cloud AI and ML services where they fit. |
| Collaborating within and across teams | Align ML work with data, operations, security, and business constraints. |
| Scaling prototypes into models | Move from notebooks and experiments toward repeatable training and serving. |
| Serving and scaling models | Deploy, monitor, optimize, and operate ML systems in production. |
| Automating and orchestrating pipelines | Use repeatable ML workflows, orchestration, CI/CD, and governance patterns. |
| Monitoring AI solutions | Track quality, drift, reliability, safety, and operational performance. |
| If you need to practice… | Best page | Why |
|---|---|---|
| AWS ML engineering | MLA-C01 | Strong live route for feature prep, training, deployment, MLOps, and monitoring. |
| AWS AI and GenAI fundamentals | AIF-C01 | Useful live route for foundation models, responsible AI, and governance. |
| Google Cloud implementation basics | ACE | Best live Google Cloud route for IAM, projects, operations, and deployment basics. |