Free reasoning-led diagnostic
GCP Professional Machine Learning Engineer Mock Exam & Readiness Assessment
A free 30-question diagnostic for Google Cloud Professional Machine Learning Engineer. Answer at your own pace, then review a domain-by-domain readiness signal with fully explained reasoning for every question.
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- 30 Questions
- 25 Single-Answer / 5 Multi-Answer
- 6 Domains
- 65 minutes (~2.2 minutes per question based on the official exam)
Who This Diagnostic Is For
This assessment is for learners preparing for Google Cloud Professional Machine Learning Engineer who want an honest, evidence-based signal of where they stand today. Every question is original CertShield content, technically validated against current official documentation (Live version effective June 1, 2026) and peer-reviewed by subject-matter experts before publication.
What It Measures
| Domain | Questions |
|---|---|
| Architecting low-code AI solutions | 4 |
| Automating and orchestrating ML pipelines | 5 |
| Collaborating within and across teams to manage data and models | 5 |
| Monitoring AI solutions | 4 |
| Scaling prototypes into ML models | 6 |
| Serving and scaling models | 6 |
Limitations of this diagnostic: 30 questions cannot cover every exam objective with equal depth. Domains with fewer than three questions are labelled "Limited evidence" in your results and can never cause a readiness downgrade on their own. This is not an official exam, an accredited course, or a pass-rate prediction.
Technically validated: Completed September 6, 2026, against the official Professional Machine Learning Engineer exam guide (effective June 1, 2026) and current Gemini Enterprise Agent Platform / BigQuery ML / Document AI / Model Armor documentation on docs. See the assessment methodology for how scoring, confidence and readiness safeguards work.
About the Official Professional Machine Learning Engineer Exam
Sourced directly from the official Professional Machine Learning Engineer Exam Guide, published by Google Cloud — Live version effective June 1, 2026.
- Questions
- 50‑60 multiple choice and multiple select questions
- Time Limit
- 2 hours
- Passing Score
- Not publicly disclosed by Google Cloud (pass/fail result only)
- Registration Fee
- $200, plus tax where applicable
- Delivery
- Pearson VUE — online-proctored (remote) or onsite-proctored at a test center
- Validity
- 2 years, with a shorter renewal exam available starting 60 days before expiration
Official exam domains this diagnostic mirrors:
- Architecting low-code AI solutions ~13%
- Collaborating within and across teams to manage data and models ~16%
- Scaling prototypes into ML models ~21%
- Serving and scaling models ~20%
- Automating and orchestrating ML pipelines ~18%
- Monitoring AI solutions ~13%
Who this certification is for:
Google Cloud's own certification exam guide describes this credential for practitioners who build, evaluate, productionize, and optimize AI solutions using Google Cloud capabilities and conventional ML approaches — proficient in model architecture, data and ML pipeline creation, MLOps, and metrics interpretation, and familiar with prompt and context engineering, application development, infrastructure management, data engineering, and data governance — typically with 3+ years of industry experience, including 1+ years designing and managing solutions using Google Cloud.
No formal prerequisites; also offered in Japanese in addition to English. The exam does not directly assess coding skills — minimum proficiency in Python and SQL is recommended to interpret questions containing code snippets.
Frequently Asked Questions
How many questions are on the Professional Machine Learning Engineer exam?
50‑60 multiple choice and multiple select questions
How long is the Professional Machine Learning Engineer exam?
2 hours
What is the passing score for Professional Machine Learning Engineer?
Not publicly disclosed by Google Cloud (pass/fail result only)
How much does the Professional Machine Learning Engineer exam cost?
$200, plus tax where applicable
What topics are covered in the Professional Machine Learning Engineer exam?
The exam covers Architecting low-code AI solutions ~13%, Collaborating within and across teams to manage data and models ~16%, Scaling prototypes into ML models ~21%, Serving and scaling models ~20%, Automating and orchestrating ML pipelines ~18%, and Monitoring AI solutions ~13%.
Who is the Professional Machine Learning Engineer exam for?
Google Cloud's own certification exam guide describes this credential for practitioners who build, evaluate, productionize, and optimize AI solutions using Google Cloud capabilities and conventional ML approaches — proficient in model architecture, data and ML pipeline creation, MLOps, and metrics interpretation, and familiar with prompt and context engineering, application development, infrastructure management, data engineering, and data governance — typically with 3+ years of industry experience, including 1+ years designing and managing solutions using Google Cloud.
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