How many questions are on the Google Professional Machine Learning Engineer exam?

The Google Professional Machine Learning Engineer exam has 60 questions, and the vendor's published description is: 50-60 multiple choice and multiple select questions; two hours (official page states format and length separately).

How long do you have to complete the Google Professional Machine Learning Engineer exam?

You get 120 minutes, which is about 120 seconds per question. The published format is: 50-60 multiple choice and multiple select questions; two hours (official page states format and length separately).

What score do you need to pass the Google Professional Machine Learning Engineer exam?

No numeric cut score is published for this certification: Not published by the provider (Google provides pass/fail results only; no numeric passing score is published).

What are the domains on the Google Professional Machine Learning Engineer exam?

There are 4 domains: Model development and evaluation, Data engineering and preprocessing, Problem framing and solution design, MLOps (pipeline automation, deployment, monitoring). The official weights are Model development and evaluation 28%, Data engineering and preprocessing 18%, Problem framing and solution design 18%, MLOps (pipeline automation, deployment, monitoring) 36%.

How should I use the domain weights when revising?

Weight by percentage, not by domain. 28% is the one where lost points cost you the most, and Certilum scores you per domain so you can see exactly where that is.

Does Certilum have practice questions for the Google Professional Machine Learning Engineer exam?

Yes: 60 questions in our bank for this certification, each tagged to the exam domain it belongs to, with the official domain weights applied.

Exam format

Google Professional Machine Learning Engineer Exam Format

GCP-PMLE

Everything the official exam blueprint specifies: question count, time limit, scoring and the weight each domain carries. The Google Professional Machine Learning Engineer certification validates the ability to build, deploy, and scale machine learning models using Google Cloud technologies. This exam is highly focused on the end-to-end lifecycle of an ML project, moving beyond simple algorithm selection to the operationalization of models (MLOps). Candidates must demonstrate proficiency in using Vertex AI, BigQuery ML, and various data processing tools to create robust, scalable, and production-ready machine learning systems. The core emphasis lies in automation, monitoring, and ensuring that models provide continuous value in a production environment.

60 questions

120 min

4 domains

certilum

Exam facts at a glance

The published specifications for this certification.

Specification	Value
Questions	60
Time limit	120 minutes (120s per question)
Passing score	Not published by the vendor — Not published by the provider (Google provides pass/fail results only; no numeric passing score is published)
Domains	4
Level	Professional
Question bank here	60 questions
Published format	50-60 multiple choice and multiple select questions; two hours (official page states format and length separately)
Source	Official exam specification, published by the vendor

Official domain weights

The percentage of the exam each domain carries. Weight your revision by these numbers, not by what you find hardest.

Domain	Weight	Topics	Practice Qs
Model development and evaluation	28%	Model selection and training techniques Hyperparameter tuning Evaluation metrics (Precision, Recall, AUC, F1) +2 more	17
Data engineering and preprocessing	18%	Data ingestion and storage (BigQuery, Cloud Storage) Data pipeline orchestration Feature engineering and feature stores +1 more	11
Problem framing and solution design	18%	Translating business problems into ML tasks Defining KPIs and success metrics Selecting appropriate ML approaches +1 more	11
MLOps (pipeline automation, deployment, monitoring)	36%	CI/CD/CT pipelines Vertex AI Pipelines Model deployment (Online vs. Batch) +3 more	21

The heaviest domain is Model development and evaluation at 28%, and that is where lost points cost you the most. Certilum scores you per domain so you can see the difference between a knowledge gap and a formatting problem.

Note from the vendor record: Active ('Professional Machine Learning Engineer'); the official page carries two banners stating the exam 'was updated to reflect recent branding changes' and 'was updated to reflect the transition from Vertex AI to Gemini Enterprise Agent Platform... and Google Cloud's data and analytics stack', and the official exam guide PDF is dated 'as of June 1, 2026' - the only exam-update date Google publishes here. Format: two hours, 50-60 multiple choice/multiple select questions, English/Japanese; no case-study percentage is published for this exam. Retake policy per official exam terms: maximum 4 attempts in a 2-year period with 14-, 60-, then 365-day waits after successive failures; validity is 2 years (Professional) with renewal window opening 60 days before expiration, and the official renewal FAQ lists shorter renewal exam and Google Skills continuing-education renewal as 'Not Available - Coming soon' for other professional certifications (i.e., standard exam is the published option for this one). Recommended experience: 3+ years industry experience including 1+ year designing and managing solutions on Google Cloud; caveat: exam does not directly assess coding skill, but code snippets appear, and prep materials predating the June 1, 2026 guide use superseded Vertex AI product names. Official exam page