Search...

Quick Feedback

GCP Professional ML Engineer Study Guide

Domain-by-domain study guide for Google Cloud Professional Machine Learning Engineer (Professional ML Engineer). Covers all 6 exam objectives with topic checklists, quick notes, and practice links.

Download PDF Notes

Exam Overview

Key facts before you start studying

Duration

120 min

Questions

Exam Code

Professional ML Engineer

Domains

Quick Navigation

Jump to a specific exam domain

1. Architecting Low-Code ML Solutions (17%)
2. Collaborating Within and Across Teams (17%)
3. Scaling Prototypes Into ML Models (17%)
4. Serving and Scaling Models (17%)
5. Automating and Orchestrating ML Pipelines (16%)
6. Monitoring ML Solutions (16%)

Domain 1

17%

Architecting Low-Code ML Solutions

BigQuery ML, AutoML, pre-built APIs, RAG architectures

Topics to Master

BigQuery ML
Vertex AI AutoML
Pre-built APIs
Document AI
Agent Builder

Domain 2

17%

Collaborating Within and Across Teams

Data validation, responsible AI, model cards, ML metadata

Topics to Master

TFDV
Vertex Explainable AI
Model cards
Responsible AI
ML Metadata

Domain 3

17%

Scaling Prototypes Into ML Models

Custom training, distributed training, hyperparameter tuning

Topics to Master

Custom Training
Distributed Training
Hyperparameter Tuning
GPU selection

Domain 4

17%

Serving and Scaling Models

Vertex AI Endpoints, batch prediction, model optimization

Topics to Master

Vertex AI Endpoints
Online Prediction
Batch Prediction
Model Optimization

Domain 5

16%

Automating and Orchestrating ML Pipelines

Vertex AI Pipelines, Kubeflow, CI/CD for ML, Feature Store

Topics to Master

Vertex AI Pipelines
KFP SDK
Feature Store
CI/CD for ML

Domain 6

16%

Monitoring ML Solutions

Drift detection, model monitoring, retraining strategies

Topics to Master

Model Monitoring
Drift Detection
Retraining Triggers
Cloud Monitoring

Continue Your Prep

Free CertStud resources for GCP-PMLE

Study Notes Exam Study Plan Start Practice Test

Back to GCP Professional ML Engineer

GCP Professional ML Engineer Study Guide

Domain-by-domain study guide for Google Cloud Professional Machine Learning Engineer (Professional ML Engineer). Covers all 6 exam objectives with topic checklists, quick notes, and practice links.

Download PDF Notes

Exam Overview

Key facts before you start studying

Duration

120 min

Questions

Exam Code

Professional ML Engineer

Domains

Quick Navigation

Jump to a specific exam domain

1. Architecting Low-Code ML Solutions (17%)
2. Collaborating Within and Across Teams (17%)
3. Scaling Prototypes Into ML Models (17%)
4. Serving and Scaling Models (17%)
5. Automating and Orchestrating ML Pipelines (16%)
6. Monitoring ML Solutions (16%)

Domain 1

17%

Architecting Low-Code ML Solutions

BigQuery ML, AutoML, pre-built APIs, RAG architectures

Topics to Master

BigQuery ML
Vertex AI AutoML
Pre-built APIs
Document AI
Agent Builder

Domain 2

17%

Collaborating Within and Across Teams

Data validation, responsible AI, model cards, ML metadata

Topics to Master

TFDV
Vertex Explainable AI
Model cards
Responsible AI
ML Metadata

Domain 3

17%

Scaling Prototypes Into ML Models

Custom training, distributed training, hyperparameter tuning

Topics to Master

Custom Training
Distributed Training
Hyperparameter Tuning
GPU selection

Domain 4

17%

Serving and Scaling Models

Vertex AI Endpoints, batch prediction, model optimization

Topics to Master

Vertex AI Endpoints
Online Prediction
Batch Prediction
Model Optimization

Domain 5

16%

Automating and Orchestrating ML Pipelines

Vertex AI Pipelines, Kubeflow, CI/CD for ML, Feature Store

Topics to Master

Vertex AI Pipelines
KFP SDK
Feature Store
CI/CD for ML

Domain 6

16%

Monitoring ML Solutions

Drift detection, model monitoring, retraining strategies

Topics to Master

Model Monitoring
Drift Detection
Retraining Triggers
Cloud Monitoring

Continue Your Prep

Free CertStud resources for GCP-PMLE

Study Notes Exam Study Plan Start Practice Test