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Google Cloud · AI & Machine Learning · Professional

Google Cloud Professional Machine Learning Engineer exam guide

Exam Professional Machine Learning Engineer

Professional Machine Learning Engineer validates that you can architect, build, scale, serve, automate and monitor ML and AI solutions on Google Cloud, including low-code and generative AI options. Google updated the exam in 2026 for its transition to the Gemini Enterprise Agent Platform.

Questions50-60
Time limit120 minutes
Passing scoreSee official page
Exam fee$200 USD
Valid for2 years
DeliveryOnline proctored or onsite at a Pearson VUE test center

Format: Multiple choice. Fee note: Plus tax where applicable..

Who it's for

ML engineers and data scientists with 3+ years of industry experience, including a year building ML solutions on Google Cloud, plus working Python and SQL.

What the exam covers

Architecting low-code AI solutions13%
Collaborating within and across teams to manage data and models16%
Scaling prototypes into ML models21%
Serving and scaling models20%
Automating and orchestrating ML pipelines18%
Monitoring AI solutions13%

Eligibility & prerequisites

Required: None

Recommended: 3+ years of industry experience, including 1+ years designing and managing solutions on Google Cloud; minimum proficiency in Python and SQL helps interpret code snippets.

Keeping it active

Valid for 2 years. Renew by retaking the exam, or through Google Skills by completing the designated renewal courses and/or skill badges during the final year of validity, which extends the certification by 1 year (link CM Connect and Google Skills accounts with the same email).

Skills & careers

Skills it validates

  • Choosing between pre-trained APIs, AutoML, custom training and foundation models
  • Preparing features and managing datasets and models
  • Training and tuning models at scale on Google Cloud
  • Serving models with online and batch prediction
  • Building ML pipelines and CI/CD for models
  • Monitoring drift, skew, fairness and model performance

Job roles

Machine Learning EngineerMLOps EngineerAI EngineerData ScientistApplied ML Engineer

Where it fits in your path

Planning a degree too? See how this cert earns college credit in the College Credit Navigator.

Build my study plan

  1. Week 1: Machine Learning Engineer learning path; review the current exam guide's product names after the 2026 platform update.
  2. Week 2-3: Build and train a custom model, then compare with AutoML and a foundation model approach on the same problem.
  3. Week 4: Serving and scaling: online vs batch prediction, autoscaling, GPUs/TPUs, model registry.
  4. Week 5: Pipelines and monitoring: build a pipeline, add evaluation gates, configure model monitoring.
  5. Week 6: Sample questions and practice exams; sit the exam.

Make it yours: pick your exam date and weekly hours. We'll split your time by domain weight and give you a printable plan.

Exam-day tips

  • Scaling prototypes and serving together are about 40%; prioritize training at scale and deployment patterns.
  • The 2026 update moved product naming to the Gemini Enterprise Agent Platform; older Vertex AI-only materials need cross-checking.
  • Know when a pre-trained API or low-code option beats custom training on time-to-value.
  • Expect questions distinguishing training-serving skew from data drift and how to detect each.
  • Questions favor Google Cloud native, managed solutions over self-managed ones.

Key terms for this exam

All 148 terms Study with thinQcards™

Commonly confused on this exam

Frequently asked questions

How much does Professional ML Engineer cost?

$200 plus tax.

How long is it valid?

Professional certifications are valid for 2 years. Renew by retaking the standard exam, or through the Google Skills renewal path in the final year (extends validity by 1 year).

Does the exam test coding?

Not directly, but you need enough Python and SQL to interpret code snippets.

What changed in 2026?

Google updated the exam for the transition from Vertex AI to the Gemini Enterprise Agent Platform and prioritizes Google Cloud native solutions.

Is it worth it?

Yes; it is a well-recognized ML engineering credential and pairs well with the new Professional Agentic Architect.

Official page: cloud.google.com. Reviewed November 2026. Vendors change fees and objectives, so confirm with the vendor before you book. Spot something out of date? Tell us.