PDF Version DemoThe benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
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Professional Machine Learning Engineer - Google Certification Path
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
What is the duration, language, and format of Professional Machine Learning Engineer - Google
- No negative marking for wrong answers
- Type of Questions: Multiple choice (MCQs), multiple answers
- Language of Exam: English, Japanese, Korean
- Duration of Exam: 120 minutes
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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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems |
| Topic 2: Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 3: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments |
| Topic 4: 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 |
| Topic 5: Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs |
| Topic 6: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques |
