GCP•Professional•Professional Machine Learning Engineer

Professional Machine Learning Engineer

Design, build, and productionize ML models to solve business challenges using Google Cloud technologies. Leverage ML APIs, Vertex AI, TensorFlow, and other Google Cloud tools. Experience with ML model architecture, data engineering, and MLOps.

Note: This syllabus summary was last updated in November 2025. For the most current and detailed syllabus information, please visit the official certification provider's website.

Exam Summary

TIME

120

Minutes

TOTAL

Questions

PRICE

$200

USD

VALID

Years

PASS

70%

Required

Prerequisites (Optional)

Recommended experience before taking this exam

3+ years of industry experience
1+ year designing and managing ML solutions using Google Cloud
Experience with Python and ML frameworks

Exam Domains & Topics

Domain 1

15%

Frame ML Problems

Translate business challenges into ML use cases

Define ML problem types and success criteria

Identify when ML is appropriate

Define model output and success metrics

Consider responsible AI principles

Identify data requirements

Domain 2

19%

Architect ML Solutions

Design reliable, scalable, and secure ML infrastructure

Choose appropriate ML services and tools

Design data processing systems

Design model serving strategy

Select appropriate training and serving infrastructure

Design for hybrid and multi-cloud deployments

Domain 3

23%

Prepare and Process Data

Perform exploratory data analysis

Build data pipelines for model training and serving

Create training datasets

Handle missing and imbalanced data

Perform feature engineering

Use BigQuery, Dataflow, and Vertex AI Feature Store

Domain 4

23%

Develop ML Models

Build models using TensorFlow, Keras, and scikit-learn

Train models using Vertex AI

Use AutoML for model development

Tune hyperparameters

Perform model evaluation and validation

Implement transfer learning and pre-trained models

Domain 5

20%

Automate and Orchestrate ML Workflows

Implement ML pipelines using Vertex AI Pipelines

Deploy models to production

Monitor model performance

Implement CI/CD for ML

Implement model retraining strategies

Manage model versions and metadata

GCP•Professional•Professional Machine Learning Engineer

Professional Machine Learning Engineer

Note: This syllabus summary was last updated in November 2025. For the most current and detailed syllabus information, please visit the official certification provider's website.

Exam Summary

TIME

120

Minutes

TOTAL

Questions

PRICE

$200

USD

VALID

Years

PASS

70%

Required

Prerequisites (Optional)

Recommended experience before taking this exam

3+ years of industry experience
1+ year designing and managing ML solutions using Google Cloud
Experience with Python and ML frameworks

Exam Domains & Topics

Domain 1

15%

Frame ML Problems

Translate business challenges into ML use cases

Define ML problem types and success criteria

Identify when ML is appropriate

Define model output and success metrics

Consider responsible AI principles

Identify data requirements

Domain 2

19%

Architect ML Solutions

Design reliable, scalable, and secure ML infrastructure

Choose appropriate ML services and tools

Design data processing systems

Design model serving strategy

Select appropriate training and serving infrastructure

Design for hybrid and multi-cloud deployments

Domain 3

23%

Prepare and Process Data

Perform exploratory data analysis

Build data pipelines for model training and serving

Create training datasets

Handle missing and imbalanced data

Perform feature engineering

Use BigQuery, Dataflow, and Vertex AI Feature Store

Domain 4

23%

Develop ML Models

Build models using TensorFlow, Keras, and scikit-learn

Train models using Vertex AI

Use AutoML for model development

Tune hyperparameters

Perform model evaluation and validation

Implement transfer learning and pre-trained models

Domain 5

20%

Automate and Orchestrate ML Workflows

Implement ML pipelines using Vertex AI Pipelines

Deploy models to production

Monitor model performance

Implement CI/CD for ML

Implement model retraining strategies

Manage model versions and metadata

Professional Machine Learning Engineer

Exam Summary

Prerequisites (Optional)

Exam Domains & Topics

Frame ML Problems

Architect ML Solutions

Prepare and Process Data

Develop ML Models

Automate and Orchestrate ML Workflows

Cloud Practice Test

Professional Machine Learning Engineer

Exam Summary

Prerequisites (Optional)

Exam Domains & Topics

Frame ML Problems

Architect ML Solutions

Prepare and Process Data

Develop ML Models

Automate and Orchestrate ML Workflows