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Career Bonuses
The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.
What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Language of Exam: English, Japanese, Korean
- Duration of Exam: 120 minutes
- No negative marking for wrong answers
- Type of Questions: Multiple choice (MCQs), multiple answers
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:
- United States: 114,000 USD
- England: 87,200 POUND
- Europe: 97,000 EURO
- India: 8,580,000 INR
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Transfer learning
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Overfitting
- Unit tests for model training and serving
- Model performance against baselines, simpler models, and across the time dimension
- Model generalization
- Productionizing
- Model explainability on Cloud AI Platform
- Build a model
- Distributed training
- Choice of framework and model
- Hardware accelerators
- Training a model as a job in different environments
- Scale model training and serving
- Modeling techniques given interpretability requirements
- Retraining/redeployment evaluation
- Tracking metrics during training
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage |
| Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates - Implement CI/CD for ML systems |
| Train and deploy models | 20% | - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure |
| 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 - Design and run experiments - Work with foundation models and generative AI techniques - Optimize model performance and generalization |
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