Google Professional Machine Learning Engineer Free Practice Test

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Quick answer

The Google Cloud Professional Machine Learning Engineer exam has 50-60 multiple-choice and multiple-select questions in 2 hours, costs USD 200 plus tax, and has no required prerequisites (checked November 2026). See all the exam facts.

About the exam

Professional Machine Learning Engineer

The Google Professional Machine Learning Engineer certification is for people who build, deploy and maintain machine learning solutions on Google Cloud (GCP). Google recommends 3 or more years of industry experience, including at least 1 year designing and managing solutions on Google Cloud. If that describes your work, this practice test shows how well you know the topics the exam guide covers. The exam guide groups the content into six sections: architecting low-code AI solutions; collaborating within and across teams to manage data and models; scaling prototypes into ML models; serving and scaling models; automating and orchestrating ML pipelines; and monitoring AI solutions. Our questions are short, self-contained scenarios with exactly one correct answer, so they do not match the real format, which also includes multiple-select questions.

Our questions are original, written from the Google Professional Machine Learning Engineer exam guide and Google Cloud documentation, and each answer is explained. Studying nearby topics? Try the Google Cloud Professional Data Engineer, Google Cloud Generative AI Leader and Databricks Data Engineer Associate free practice tests.

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Exam facts

Professional Machine Learning Engineer: format, time and fee Last checked November 2026

Exam name: Professional Machine Learning EngineerGoogle Cloud certification page
Questions: 50 to 60 questions, multiple choice and multiple selectGoogle Cloud certification page
Time: 2 hoursGoogle Cloud certification page
Fee: USD 200, plus tax where applicableGoogle Cloud certification page
Question types: Multiple choice and multiple select. No labs, hands-on tasks or case studies are mentioned on the certification page or in the guide.Google Cloud certification page
Languages: English, JapaneseGoogle Cloud certification page
Delivery: Online-proctored from a remote location, or onsite-proctored at a testing centerGoogle Cloud certification page
Prerequisites: None required; 3 or more years of industry experience, including 1 or more years designing and managing solutions using Google Cloud, is recommendedGoogle Cloud certification page
Validity: Not published on the certification page, which points to the renewal FAQs.Not published by Google Cloud
Exam guide version: The guide shows no version date. The certification page links two guide editions, so confirm which one applies before you study.Exam guide
Sections: Architecting low-code AI solutions; collaborating within and across teams to manage data and models; scaling prototypes into ML models; serving and scaling models; automating and orchestrating ML pipelines; monitoring AI solutionsExam guide
Passing score: Not published.Not published by Google Cloud
Exam code: Not published.Not published by Google Cloud
Our practice test: Single-answer scenario questions only, with our own question count, pass mark and timer. These are not Google's settings.This site

Every question here has exactly one correct answer. If the real exam also uses multiple-select or other types, the facts above say so. Confirm current details with Google Cloud before you register.

What to focus on for the Professional Machine Learning Engineer

Choosing a service by name recognition instead of weighing the constraints in the scenario, such as data size, latency, cost and operational effort.
Mixing up look-alike options, such as AutoML, BigQuery ML and custom training, which fit different data and skill needs.
Skipping the monitoring, governance and reliability details, which can decide between two plausible answers.
Reading too quickly and missing the one requirement that rules out an option.
Confirm which guide edition the certification page links before you start, since it links two.
Take this free practice test one section at a time, read every explanation, then retake your weakest section.

Sample Professional Machine Learning Engineer practice questions

Three questions pulled straight from the bank, with the answer shown. For the timed, no-peeking version, use the quiz above.

Question 1 · Architecting low-code AI solutions

Every night a team must score 300 million rows that already sit in BigQuery using a BigQuery ML model. They want the simplest setup and prefer not to deploy or maintain a serving endpoint. What should they do?

Register the model in Model Registry and require an online endpoint to be running first
Export the table to Cloud Storage and score it with a custom service on Compute Engine
Deploy the model to an Agent Platform endpoint and call it for every row from a script
Run batch prediction directly in BigQuery ML with ML.PREDICT

Why: BigQuery ML has built-in batch prediction that does not need Agent Platform. The other options add an endpoint, an export, or a service to maintain, which conflicts with the wish for the simplest setup.

Source: BigQuery ML inference overview

Question 2 · Architecting low-code AI solutions

A team asks Gemini to classify support tickets into a strict internal format. After many prompt rewrites the output is still inconsistent, and they have hundreds of labeled examples in BigQuery. What is the sensible next step?

Move the examples into an object table and expect the model to learn from them
Tune the model on their labeled examples
Replace Gemini with a k-means model so the labeled examples no longer matter
Keep lengthening the prompt, since tuning cannot help when prompts are inconsistent

Why: Tuning helps when expected behavior is hard to define in a prompt or prompts do not produce consistent results. More prompt text already failed, k-means ignores labels, and an object table does not train anything.

Source: BigQuery, Tune a model using your data

Question 3 · Scaling prototypes into ML models

A team needs a Gemini-based classifier for internal documents. They have only a small set of labeled examples and cannot afford a lengthy fine-tuning process. What approach fits?

Reserving a training cluster for long tuning
Training a new model from scratch on documents
Full fine-tuning of the model on those few examples
Prompting alone, refined with the examples

Why: Prompting is preferred when labeled data is limited or a lengthy fine-tuning process is not affordable. Full fine-tuning, training from scratch or reserving a cluster all need more data or cost than the stem allows.

Source: Google Cloud documentation, Tune models overview

FAQ

Professional Machine Learning Engineer questions, answered

How much does the exam cost and how do I register?

Google Cloud lists the fee as USD 200, plus tax where applicable. Registration and the current price are on the Google Cloud certification page (checked November 2026), so confirm there before you book. Google Cloud certification page.

Is this the real exam?

No. This is a free mock test made of original questions we wrote from Google Cloud's public documentation. It is not the real exam, it is not affiliated with or endorsed by Google Cloud, and it cannot promise a pass.

Does the real exam have questions with more than one correct answer?

Yes. Google Cloud describes the exam as multiple choice and multiple select. Every question here has exactly one correct answer, so the format is not identical to the real exam.

Is the timed test the same as the real Professional Machine Learning Engineer exam?

No. Ours has 50 questions in 120 minutes, all single-answer, with a 70% practice pass mark we chose ourselves. Google Cloud's own format is in the exam facts.

How should I use this Professional Machine Learning Engineer practice test?

Start with quick practice and read every explanation, including for the answers you got right. Then check Where you stand to find your weakest section, practice it again, and finish with a timed test.