Google : Professional-Machine-Learning-Engineer Questions & Answers
Last Updated: Sep 08, 2026
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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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- Professional Cloud Architect was the highest paying certification of 2020 and 2019
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 2: Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Optimize model performance and generalization - Design and run experiments |
| Topic 3: Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift |
| Topic 4: Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Topic 5: Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Topic 6: Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems - Design end-to-end ML workflows |
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