Google Professional Machine Learning Engineer
Google Professional Machine Learning Engineer
Google Professional Machine Learning Engineer
The Google Professional Machine Learning Engineer exam has been developed to evaluate the candidates ability to design, build and productionize ML models for solving business challenges. Together with the ability to use Google Cloud technologies and knowledge and skills of proven ML models and techniques.
Knowledge Required
The ML Engineer should have -
- Proficiency in all aspects of model architecture, data pipeline interaction, and metrics interpretation.
- Familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.
- Thorough understanding of training, retraining, deploying, scheduling, monitoring, and improving models
- Skills to design and create scalable solutions for optimal performance.
Exam Evaluates
The exam assesses your ability to -
- Frame ML problems
- Develop ML models
- Architect ML solutions
- Automate and orchestrate ML pipelines
- Design data preparation and processing systems
- Monitor, optimize, and maintain ML solutions
Exam Course Outline
The Google Professional Machine Learning Engineer Practice Exam covers the following topics -
- Domain 1: Overview of Framing ML problems
- Domain 2: Overview of Architecting ML solutions
- Domain 3: Overview of Designing data preparation and processing systems
- Domain 4: Overview of Developing ML models
- Domain 5: Overview of Automating and orchestrating ML pipelines
- Domain 6: Overview of Monitoring, optimizing, and maintaining ML solutions
Google Professional Machine Learning Engineer FAQs
What is the Google Professional Machine Learning Engineer Exam?
The Google Professional Machine Learning Engineer exam has been developed to evaluate the candidates ability to design, build and productionize ML models for solving business challenges. Together with the ability to use Google Cloud technologies and knowledge and skills of proven ML models and techniques.
What is the Knowledge required for Google Professional Machine Learning Engineer Exam?
The ML Engineer should have -
- Proficiency in all aspects of model architecture, data pipeline interaction, and metrics interpretation.
- Familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.
- Thorough understanding of training, retraining, deploying, scheduling, monitoring, and improving models
- Skills to design and create scalable solutions for optimal performance.
What does the Google Professional Machine Learning Engineer Exam Evaluates?
The exam assesses your ability to -
- Frame ML problems
- Develop ML models
- Architect ML solutions
- Automate and orchestrate ML pipelines
- Design data preparation and processing systems
- Monitor, optimize, and maintain ML solutions
What is the exam details for the Google Professional Machine Learning Engineer?
- Exam Duration: 2 hours
- Registration fee: $200 (plus tax where applicable)
- Language: English
- Exam format: 50-60 multiple choice and multiple select questions
- Prerequisites: None
What is the delivery method for Google Professional Machine Learning Engineer Exam?
- Online-proctored exam from a remote location
- Onsite-proctored exam at a testing center
What is the recommended experience for the Google Professional Machine Learning Engineer Exam?
Candidate is required to have more than 3 years of industry experience including 1 or more years designing and managing solutions using Google Cloud.
What are the topics covered in the Google Professional Machine Learning Engineer Exam?
The Google Professional Machine Learning Engineer Practice Exam covers the following topics -
- Domain 1: Overview of Framing ML problems
- Domain 2: Overview of Architecting ML solutions
- Domain 3: Overview of Designing data preparation and processing systems
- Domain 4: Overview of Developing ML models
- Domain 5: Overview of Automating and orchestrating ML pipelines
- Domain 6: Overview of Monitoring, optimizing, and maintaining ML solutions