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.

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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:

• Identification of components, parameters, triggers, and compute needs
• Model/dataset lineage
• Tuning compute performance
• Testing for target performance
• Constructing and testing of parameterized pipeline definition in SDK
• Use CI/CD to test and deploy models
• A/B and canary testing
• Hooking into model and dataset versioning
• Hybrid or multi-cloud strategies
• Implement serving pipeline
• Hooking models into existing CI/CD deployment system
• Google Cloud serving options
• Decoupling components with Cloud Build
• Orchestration framework
• Implement training pipeline
• Model binary options
• Storing data and generated artifacts
• Organization and tracking experiments and pipeline runs
• Setup of trigger and pipeline schedule
• Performing data validation
• Track and audit metadata

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

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Topics 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:

• Data Preparation and Processing
• ML Problem Framing
• ML Model Development
• ML Solution Architecture
• ML Solution Monitoring, Optimization, and Maintenance
• ML Pipeline Automation & Orchestration

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