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Exam Details
The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Design architecture that complies with regulatory and security concerns
- Serving
- Identifying potential regulatory issues
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- A variety of component types - data collection; data management
- Automation
- Exploration/analysis
- Selection of quotas and compute/accelerators with components
- Choose appropriate Google Cloud hardware components
- Privacy implications of data usage
- Automation of data preparation and model training/deployment
- Choose appropriate Google Cloud software components
- Feature engineering
- Logging/management
- Data connections
- SDLC best practices
- Optimizing data use and storage
- Monitoring
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
Design pipeline. Considerations include:
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- Decoupling components with Cloud Build
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- Model binary options
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- Implement serving pipeline
- Track and audit metadata
- Identification of components, parameters, triggers, and compute needs
- Implement training pipeline
- Tuning compute performance
- Storing data and generated artifacts
- Organization and tracking experiments and pipeline runs
- Use CI/CD to test and deploy models
- A/B and canary testing
- Setup of trigger and pipeline schedule
- Hybrid or multi-cloud strategies
- Google Cloud serving options
- Orchestration framework
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The 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
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- 87% of Google Cloud certified individuals are more confident about their cloud skills
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Designing ML solutions | - ML architecture design
|
| Data preparation and processing | - Feature engineering
|
| Deployment and operations | - Model deployment
|
| ML pipeline automation and orchestration | - Pipeline design
|
| ML model development | - Model training and tuning
|
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