Try and practice the latest Google : Professional-Machine-Learning-Engineer real questions & answers
Updated: Sep 07, 2026
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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Machine Learning Engineer Certification Exam |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Exam Price: | $200 USD (plus tax where applicable) |
| Real Exam Qty: | 50-60 |
| Exam Duration: | 120 minutes |
| Available Languages: | English, Japanese |
| Passing Score: | Not officially published, approximately 70% |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Professional Data Engineer |
| Exam Format: | Multiple choice, Multiple select |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Official Exam Guide Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online-proctored remote exam or onsite-proctored exam at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
How much Professional Machine Learning Engineer - Google Cost
The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Implement CI/CD for ML systems - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Design and run experiments |
| Train and deploy models | 20% | - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems |
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