Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer

Measure your ability to design, develop, and deploy scalable and efficient machine learning systems using Google Cloud technologies.

Focus

The Google Professional Machine Learning Engineer certification focuses on Machine Learning Engineering on Google Cloud Platform

Exam Duration

The Google Professional Machine Learning Engineer exam duration is 180 minutes (3 hours)

Num of Question

The exam contains a total of 90 questions

Exam Fee

The exam fee is $300.

Exam Validity

The CCRN-Pediatric certification is valid for 2 years.

Prior Certification

• Google Cloud Professional Engineer - Professional Data Engineer or Professional Cloud Developer certifications are recommended

Exam Format

The Google Professional Machine Learning Engineer exam is a Multiple choice questions and hands-on assessments

Passing Conditions

To pass Google Professional Machine Learning Engineer exam, applicants must achieve a minimum score of Pass all components of the exam with a score of 720 (out of 1000)

Who Should Take

Machine learning engineers, data scientists, and developers who design, develop, and deploy scalable and efficient machine learning systems using Google Cloud technologies

• Machine Learning Engineer
• Data Scientist
• Cloud Developer

Prerequisites

2 years of experience in machine learning engineering or a related field

Or can substitute with certifications:

Certification Process

• Sign up for the exam on the Google Cloud Certification website
• Study for the exam using official study guides and training courses
• Schedule and take the exam at a Pearson VUE testing center

Domains with Weights

• Machine Learning (40%): Design, develop, and deploy machine learning models using Google Cloud AI Platform
• Cloud Services (30%): Design and deploy scalable and efficient cloud-based machine learning systems using Google Cloud services
• Data Engineering (20%): Design and develop data pipelines and data warehousing architecture for machine learning systems
• Security and Privacy (10%): Design and implement secure and compliant machine learning systems using Google Cloud security and privacy controls