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100+ Free GCP ML Engineer Practice Questions

Pass your Google Cloud Professional Machine Learning Engineer exam on the first try — instant access, no signup required.

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2026 Statistics

Key Facts: GCP ML Engineer Exam

50-60

Questions

Google Cloud certification page

2 hrs

Exam Time

Google Cloud certification page

$200

Exam Fee

Google Cloud certification page

6

Exam Domains

Professional ML Engineer exam guide

3+ yrs

Recommended Experience

Google Cloud certification page

14 days

Retake Wait Period

Google Cloud exam policies

Google's current PMLE exam covers 50-60 questions in 2 hours at $200. The 2026 version includes generative AI content covering Model Garden and Vertex AI Agent Builder. Six domains are tested: Architecting Low-Code AI Solutions (~12%), Collaborating to Manage Data and Models (~16%), Scaling Prototypes (~18%), Serving and Scaling Models (~19%), Automating and Orchestrating ML Pipelines (~22%), and Monitoring AI Solutions (~13%). Delivered via Pearson VUE online or at testing centers.

About the GCP ML Engineer Exam

The Google Cloud Professional Machine Learning Engineer certification validates your ability to design, build, deploy, and optimize ML and AI solutions on Google Cloud. The 2026 exam now covers generative AI including Vertex AI Agent Builder, Model Garden, and foundation model fine-tuning alongside conventional ML topics like MLOps pipelines, model serving, and monitoring.

Questions

60 scored questions

Time Limit

2 hours

Passing Score

Not published (~70% estimated)

Exam Fee

$200 (Google Cloud)

GCP ML Engineer Exam Content Outline

~12%

Architecting Low-Code AI Solutions

AutoML, BigQuery ML, pre-trained APIs, Model Garden, Vertex AI Agent Builder, and RAG

~16%

Collaborating to Manage Data and Models

Feature Store, ML Metadata, experiment tracking, model governance, data privacy, and team collaboration

~18%

Scaling Prototypes into ML Models

Model training, distributed training, hyperparameter tuning, feature engineering, and evaluation metrics

~19%

Serving and Scaling Models

Vertex AI Endpoints, batch prediction, autoscaling, traffic splitting, and model optimization

~22%

Automating and Orchestrating ML Pipelines

Vertex AI Pipelines, TFX, Cloud Composer, CI/CD for ML, and data validation

~13%

Monitoring AI Solutions

Model monitoring, drift detection, Explainable AI, responsible AI, and fairness evaluation

How to Pass the GCP ML Engineer Exam

What You Need to Know

  • Passing score: Not published (~70% estimated)
  • Exam length: 60 questions
  • Time limit: 2 hours
  • Exam fee: $200

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

GCP ML Engineer Study Tips from Top Performers

1Focus on Vertex AI Pipelines and MLOps — the largest domain at 22% weighting
2Practice with the Google Cloud Free Tier to get hands-on Vertex AI experience
3Study generative AI integration using Model Garden and Vertex AI Agent Builder
4Master model monitoring concepts: data drift, prediction drift, and feature attribution
5Review TFX components and Kubeflow Pipelines SDK for pipeline orchestration

Frequently Asked Questions

How many questions are on the GCP ML Engineer exam?

The exam contains 50-60 multiple choice and multiple select questions, to be completed within 2 hours.

What is the GCP ML Engineer exam fee?

The registration fee is $200 USD plus applicable tax. This applies to both online-proctored and testing center sessions.

What score do I need to pass the GCP ML Engineer exam?

Google does not publish an exact passing score. Based on candidate reports, it is estimated to be around 70% correct answers.

Does the 2026 exam cover generative AI?

Yes. The current exam version includes generative AI tasks such as building solutions with Model Garden, Vertex AI Agent Builder, and evaluating generative AI solutions.

Can I take the GCP ML Engineer exam remotely?

Yes. Since February 2026, exams are delivered through Pearson VUE for both online-proctored and testing center options.

What experience does Google recommend for this exam?

Google recommends 3+ years of industry experience including 1+ year designing and managing ML solutions using Google Cloud. Proficiency in Python and SQL is expected.