PMLE Practice Tests

Google Cloud Professional Machine Learning Engineer

Prepare for Google Cloud Professional Machine Learning Engineer with practice for AI solution architecture, team collaboration, model scaling, serving, pipeline orchestration, monitoring, and responsible AI.

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Duration

2 hours

Questions

50-60 questions

Cost

$200 USD

Where to register

Google Cloud

Issued by Google Cloud. Delivered via Online-proctored or onsite-proctored exam. Google recommends 3+ years of industry experience, including 1 or more years designing and managing Google Cloud solutions.

01·Overview

Certification overview

The format, prerequisites, and what to expect on exam day.

Exam details

Exam Code
Professional ML Engineer
Duration
2 hours
Questions
50-60 questions
Format
Multiple choice and multiple select
Passing Score
Not disclosed
Cost
$200 USD
Validity
2 years

Prerequisites

3+ years of industry experience
1+ year designing and managing solutions using Google Cloud
Minimum proficiency in Python and SQL for interpreting exam scenarios
Experience with data platforms, distributed processing, ML pipelines, and MLOps concepts

02·Domains

Exam domains

Topics on the official blueprint, with their relative weight.

Architecting low-code AI solutions

13%

Develop ML models with BigQuery ML and AutoML
Use Google Cloud AI APIs
Tune Gemini models
Optimize Gemini applications for cost, latency, and availability

Collaborating to manage data and models

16%

Explore and preprocess data
Manage features
Handle sensitive data
Track experiments, artifacts, versions, and lineage

Scaling prototypes into ML models

21%

Choose model type and product fit
Train models with SDKs and pipelines
Tune hyperparameters
Fine-tune foundation models

Serving and scaling models

20%

Deploy batch and online inference
Serve custom containers
Manage model registry and versions
Use rollout strategies

Automating and orchestrating ML pipelines

18%

Build end-to-end ML pipelines
Validate data and models
Automate retraining
Apply CI/CD/CT

Monitoring AI solutions

13%

Protect against AI risks
Apply safety filters and Model Armor
Monitor drift and skew
Apply responsible AI practices

03·Key topics

What you actually study

Service families and concept clusters that show up across questions.

Google Cloud AI Stack

Gemini Enterprise Agent Platform
Model Garden
BigQuery ML
AI APIs
AutoML

MLOps

Pipelines
model registry
experiment tracking
CI/CD/CT
monitoring

Serving and Scaling

Online inference
batch inference
custom containers
canary releases
GPU and TPU choices

Responsible AI

Model Armor
safety filters
bias monitoring
drift detection
secure model operation

04·Study tips

How to actually pass it

Practical strategies for the weeks before, and the morning of.

Preparation strategy

Review the current Google Cloud exam guide before scheduling because the PMLE guide has changed for Gemini Enterprise Agent Platform.
Practice choosing between low-code AI, custom training, fine-tuning, batch serving, and online serving.
Study pipeline orchestration, model registry, monitoring, data lineage, and feature management.
Connect responsible AI, data privacy, and security requirements to concrete Google Cloud controls.

Exam day

Identify the product family first, then match the answer to the service or platform named in the scenario.
Watch for requirements around cost, latency, governance, and operational ownership.
Use elimination on answers that ignore security, monitoring, or responsible AI requirements.
Flag long architecture questions and return after completing faster recall questions.
Choose managed services when the question emphasizes speed, governance, and reduced operations.

Practice the full Google Cloud ML lifecycle.

Build readiness across architecture, MLOps, serving, monitoring, and responsible AI. Start free, no card required.

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