Google Cloud
PMLE - Professional Machine Learning Engineer
Validates skills in designing, building, and productionizing ML models to solve business challenges using Google Cloud technologies. Covers the ML workflow from data preparation to model deployment and monitoring.
401+Questions
6Domains
24Competencies
Time Limit
120 minutes
Passing Score
70%
Question Bank
401+
Exam Domains
This exam covers 6 domains with 24 competencies. Each domain is weighted to reflect its importance on the actual exam.
1
Architecting Low-Code ML Solutions
Focuses on developing and implementing ML models and AI solutions using low-code platforms and Google Cloud APIs.
0.12%
Weight
3 Competencies
- Developing ML models by using BigQuery ML
- Building AI solutions by using ML APIs
- Training models by using AutoML
2
Collaborating Within and Across Teams to Manage Data and Models
Involves exploration, preprocessing, management, and collaboration in ML projects and data science workspaces.
0.16%
Weight
5 Competencies
- Exploring and preprocessing organization-wide data
- Managing a data science workspace
- Managing features and datasets for ML
- Managing ML models and versions
- Managing and tracking ML experiments
3
Scaling Prototypes Into ML Training and Serving Systems
Designing scalable training and serving infrastructures and pipelines for ML operations.
0.18%
Weight
4 Competencies
- Developing training and serving infrastructure
- Implementing training pipelines
- Implementing serving pipelines
- Implementing ML workflow orchestration
4
Serving and Scaling Models
Focused on testing, monitoring, troubleshooting, and optimizing ML models in production environments.
0.2%
Weight
4 Competencies
- Troubleshooting ML solutions and models
- Testing for target performance
- Tuning performance of ML solutions
- Building an ML solution monitoring strategy
5
Automating and Orchestrating ML Pipelines
Focused on automation and orchestration of ML pipelines and workflows.
0.21%
Weight
4 Competencies
- Orchestrating ML training and inference workflows
- Designing and implementing ML workflows
- Implementing ML pipeline automation
- Developing end-to-end ML pipelines
6
Monitoring, Optimizing, and Maintaining ML Solutions
Includes monitoring, optimization, and maintenance strategies to ensure reliable ML solutions.
0.13%
Weight
4 Competencies
- Ensuring ML solution reliability
- Maintaining ML solutions
- Tuning performance and optimizing ML solutions for cost and efficiency
- Monitoring and troubleshooting model and infrastructure performance
How to Prepare
ProctorPulse uses AI to generate original practice questions aligned with the official Google Cloud exam blueprint.
1
Study the Domains
Review the exam domains and competencies to understand what you need to know.
2
Practice Questions
Take practice exams with 401+ questions covering every domain and competency.
3
Track & Improve
Review explanations, track your scores, and focus on weak areas until you are ready.
