You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
GOOGLE CLOUDGoogle Cloud Professional Machine Learning Engineer
The Google Cloud Professional Machine Learning Engineer certification validates your ability to design, build, and productionize machine learning models to solve business problems. It is for ML engineers and data scientists who architect scalable, reliable, and responsible ML solutions on Google Cloud. Earning it demonstrates you can turn ML experiments into secure, cost-effective, and maintainable production systems.
Exam formatMultiple choice and multiple select
DeliveryGoogle Cloud
Free questions381
Content last reviewed 30 July 2026 · Up to date
The certification
What Google Cloud Professional Machine Learning Engineer proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
6domains
14objectives
113concepts
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The Google Cloud Professional Machine Learning Engineer certification validates that you can design, build, and productionize machine learning models to solve business challenges. It covers the full ML lifecycle on Google Cloud, from framing the problem and architecting data pipelines to training, evaluating, deploying, and monitoring models in production.
Earning this certification demonstrates that you can apply ML engineering best practices: selecting appropriate Google Cloud services, building scalable and reproducible training pipelines, optimizing models for performance and cost, and ensuring responsible AI principles are embedded throughout. The credential signals that you can operate ML systems reliably and securely in an enterprise environment.
Who it’s for
This certification is for machine learning engineers, data scientists, and ML practitioners who design, build, and productionize ML models on Google Cloud. It is also relevant for professionals who architect ML solutions and collaborate with data engineering and software engineering teams. You are comfortable with Python, have hands-on experience with Google Cloud ML services like Vertex AI, and understand the end-to-end ML lifecycle, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
Recommended experience
Google Cloud recommends in-depth experience setting up cloud environments for an organization and deploying services and solutions based on business requirements, with a focus on ML workloads. Designing and building scalable ML models and training pipelines on Google Cloud; Applying ML engineering best practices for feature engineering, model evaluation, and hyperparameter tuning; Deploying and serving models in production with Vertex AI and related services; Monitoring ML models for performance, drift, and operational health; Implementing responsible AI practices, including fairness, explainability, and privacy
The syllabus
What you’ll learn
Every domain and objective Google Cloud measures, with the weight they carry on the exam.
The official Google Cloud exam outline · checked 30 July 2026 · See the source
Architecting low-code AI solutions
- Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform
- Building AI solutions using Google Cloud AI APIs or foundational models
Collaborating within and across teams to manage data and models
- Exploring and preprocessing data for ML
- Model prototyping using notebooks (e.g., Gemini Enterprise Agent Platform Workbench and Colab Enterprise)
- Tracking and running ML experiments
Scaling prototypes into ML models
- Building models given the task considering cost, complexity, latency, and scalability
- Training models
- Choosing appropriate hardware for training
Serving and scaling models
- Serving models
- Scaling online model serving
Automating and orchestrating ML pipelines
- Developing end-to-end ML pipelines
- Automating model retraining
Monitoring AI solutions
- Identifying risks to AI solutions
- Monitoring, testing, and troubleshooting AI solutions
On the day
The exam itself
Everything Google Cloud publishes about sitting it, and nothing we inferred.
Prerequisites
No mandatory prerequisites — this certification has no required predecessor exam or credential.
CertificationGoogle Cloud Professional Machine Learning Engineer
Exam formatMultiple choice and multiple select
DeliveryGoogle Cloud
LanguagesEnglish, Japanese
Certification levelProfessional
After you pass
Where this credential goes next
The path Google Cloud lays out, how the credential is kept, and where to book.
Step-by-step path to Google Cloud Professional Machine Learning Engineer
Credential earnedGoogle Cloud Professional Machine Learning Engineer Professional level certification Renewal and maintenance
Google Cloud certifications are valid for two years. To maintain certification status, you must complete the Google Cloud certification renewal assessment before the expiration date. Stay current with the latest technologies and maintain your certification.
Learn more about renewal requirements Lifecycle status
This certification is currently active and available. Google Cloud maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by Google Cloud
Exam registration
Register for the exam through Google Cloud, Google Cloud’s authorized testing partner.
Schedule your examVisit the official Google Cloud certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How does the Professional Machine Learning Engineer certification relate to other Google Cloud certifications?
It is a Professional-level certification that validates advanced skills in designing, building, and productionizing ML models. It is part of the Professional tier, which also includes Cloud Architect, Data Engineer, and Cloud DevOps Engineer, among others. There is no prerequisite certification required to take this exam.
Is there a hands-on or lab component in the Professional Machine Learning Engineer exam?
The exam is primarily multiple choice and multiple select. Google Cloud does not publish details about a hands-on lab component for this exam.
What is the retake policy for the Professional Machine Learning Engineer exam?
Google Cloud's retake policy requires a waiting period of 14 days after a failed attempt before you can retake the exam. There is no limit on the number of attempts, but each attempt requires a new exam registration.
Can I reschedule or cancel my exam appointment?
Yes, you can reschedule or cancel your exam appointment through the Google Cloud certification portal, subject to the provider's policies. It is recommended to do so well in advance of your scheduled time.
What job roles does the Professional Machine Learning Engineer certification map to?
It maps to roles such as Machine Learning Engineer, ML Engineer, Data Scientist, and AI Engineer, who are responsible for designing, building, and maintaining ML systems in production.
How can I recertify for the Professional Machine Learning Engineer certification?
You can recertify by completing the Google Cloud certification renewal assessment, which is available online. The assessment covers new product updates and best practices.
Are there testing accommodations available for candidates with disabilities?
Yes, Google Cloud provides reasonable accommodations for candidates with disabilities. You can request an accommodation through the certification portal, and the request will be reviewed by the certification team.
How soon will I receive my exam results?
For online proctored exams, you typically receive your score report immediately after completing the exam. For onsite exams, results may be available within a few days.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 381 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept Google Cloud measures — each one written out.
6domains
14objectives
113concepts
81free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official Google Cloud exam outline · checked 30 July 2026 · See the source
1Architecting low-code AI solutions42 questions
2Collaborating within and across teams to manage data and models68 questions
3Scaling prototypes into ML models76 questions
4Serving and scaling models77 questions
5Automating and orchestrating ML pipelines58 questions
6Monitoring AI solutions60 questions
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform6 pagesOpen →
Free in front of it
26 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
381 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
381questions
81pages
14objectives
Freealways
Start anywhereDeveloping ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform 26 questions · 6 pages
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Architecting low-code AI solutions42 questions
2Collaborating within and across teams to manage data and models68 questions
3Scaling prototypes into ML models76 questions
4Serving and scaling models77 questions
5Automating and orchestrating ML pipelines58 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
113concepts
381free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from Google Cloud
Where the exam is defined, scheduled and scored.
2official links
113concepts mapped
The source
Google Cloud’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official Google Cloud outline already has questions here
We build from the official skills outline, not from a summary of it — 14 objectives, 113 concepts written under them, and free questions against every one. When Google Cloud changes the outline, this page changes with it.
14objectives on the outline→113concepts written→381free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.