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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.

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:

• India: 8,580,000 INR
• England: 87,200 POUND
• United States: 114,000 USD
• Europe: 97,000 EURO

Prerequisites

The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.

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:

• Choose appropriate Google Cloud software components
• Privacy implications of data usage
• Selection of quotas and compute/accelerators with components
• Monitoring
• Choose appropriate Google Cloud hardware components
• Logging/management
• Design reliable, scalable, highly available ML solution
• Identifying potential regulatory issues
• A variety of component types - data collection; data management
• Automation
• Design architecture that complies with regulatory and security concerns
• Automation of data preparation and model training/deployment
• Optimizing data use and storage
• Data connections
• Serving
• Building secure ML systems
• Feature engineering
• SDLC best practices
• Exploration/analysis