Professional Machine Learning Engineer exam — Information & Free Practice

US · Examination information

200 free questions · Answer explanations · No signup required

Professional Machine Learning Engineer exam

Examination outline

Architecting low-code AI solutions (13%)
- Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform
- Building AI solutions using Google Cloud AI APIs or foundational models
Collaborating within and across teams to manage data and models (16%)
- Exploring and preprocessing data for ML
- Model prototyping using notebooks
- Tracking and running ML experiments
Scaling prototypes into ML models (21%)
- Building models given the task considering cost, complexity, latency, and scalability
- Training models
- Choosing appropriate hardware for training
Serving and scaling models (20%)
- Serving models
- Scaling online model serving
Automating and orchestrating ML pipelines (18%)
- Developing end-to-end ML pipelines
- Automating model retraining
Monitoring AI solutions (13%)
- Identifying risks to AI solutions
- Monitoring, testing, and troubleshooting AI solutions

Examination sources

This examination may be required or accepted as an alternative, depending on the license and application pathway.