PDF Version DemoTopics of Professional Machine Learning Engineer - Google
Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:
- ML Model Development
- ML Problem Framing
- ML Pipeline Automation & Orchestration
- ML Solution Architecture
- ML Solution Monitoring, Optimization, and Maintenance
- Data Preparation and Processing
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- England: 87,200 POUND
- United States: 114,000 USD
- Europe: 97,000 EURO
- India: 8,580,000 INR
What is the duration, language, and format of Professional Machine Learning Engineer - Google
- No negative marking for wrong answers
- Duration of Exam: 120 minutes
- Type of Questions: Multiple choice (MCQs), multiple answers
- Language of Exam: English, Japanese, Korean
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Manage datasets and features in Vertex AI |
| 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 |
| Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Design and run experiments - Work with foundation models and generative AI techniques |
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction |
| 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 |
| Monitor and optimize AI solutions | 16% | - Troubleshoot and maintain production systems - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage |
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