Exam objectives
PMLEProfessional Machine Learning Engineer exam objectives
The published blueprint is the contract: the exam can only test what is on this list. Domains carry their official weightings, and objectives with a practice page link straight to questions written for that objective.
Titles and weightings from the official objectives. Google Cloud exam page ↗
Architecting low-code AI solutions
13%of examCollaborating within and across teams to manage data and models
16%of examScaling prototypes into ML models
21%of examServing and scaling models
20%of examAutomating and orchestrating ML pipelines
18%of examMonitoring AI solutions
13%of examObjectives marked Practice open a page of original questions written for that objective, each with a full explanation cited to Google Cloud documentation.
Keep reading
- ML Engineer practice questions
Free sample questions with the full explanation on every answer.
- Free ML Engineer practice test
10 original questions, playable now. No account, no card.
- ML Engineer exam format
How many questions, how long, what the items look like, and how long it stays valid.
- ML Engineer passing score
The exact cut score, what kind of number it is, and the retake terms.
- How hard is ML Engineer?
An honest difficulty read from the format, the clock and the weights.
- What ML Engineer costs
The voucher price, the retake, and what renewal costs across the cycle.
- ML Engineer guide
Who it is for, study plans by experience level, and whether it is worth it.