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What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Type of Questions: Multiple choice (MCQs), multiple answers
- No negative marking for wrong answers
- Language of Exam: English, Japanese, Korean
- Duration of Exam: 120 minutes
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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.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Prerequisites
The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure |
| Topic 2: Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools |
| Topic 3: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 4: Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks |
| Topic 5: Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health - Troubleshoot and maintain production systems |
| Topic 6: Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Automate retraining and model updates - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools |




