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Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer

Free Practice Test

FREE
  • No. of Questions5
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice
  • TypeExam Format

Practice Exam

$15.99
  • No. of Questions170
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice, Exam
  • Last UpdatedMay 2024

Online Course

-
  • Content TypeVideo
  • DeliveryOnline
  • AccessImmediate
  • Access DurationLife Long Access
  • No of videos-
  • No of hours-
Not Available

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

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.

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.

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 Duration: 2 hours
  • Registration fee: $200 (plus tax where applicable)
  • Language: English
  • Exam format: 50-60 multiple choice and multiple select questions
  • Prerequisites: None
  • Online-proctored exam from a remote location
  • Onsite-proctored exam at a testing center

Candidate is required to have more than 3 years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

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

 

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