Google Professional Machine Learning Engineer Exam

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Google Professional Machine Learning Engineer Exam

 

A Professional Machine Learning Engineer utilizes Google Cloud technologies and expertise in established models and techniques to construct, assess, deploy, and enhance ML models. This individual manages intricate, extensive datasets and develops code that is both repeatable and reusable. Throughout the ML model development process, considerations of responsible AI and fairness are integral, with close collaboration with other roles to ensure the sustained success of ML-driven applications. The Professional Machine Learning Engineer exam evaluates your proficiency in:

  • Designing low-code ML solutions
  • Cooperating within and between teams to oversee data and models
  • Expanding prototypes into ML models
  • Deploying and expanding the reach of models
  • Automating and coordinating ML pipelines
  • Supervising ML solutions

 

Who should take the exam?

Google Professional Machine Learning Engineer is best for those with 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

 

Google Professional Machine Learning Engineer Exam Course Outline 

The Exam covers the given topics  - 

  • Section 1: Learn Architecting low-code ML solutions (12%)
  • Section 2: Understand about Collaborating within and across teams to manage data and models (16%)
  • Section 3: Learn about Scaling prototypes into ML models (18%)
  • Section 4: Understand about Serving and scaling models (19%)
  • Section 5: Learn Automating and orchestrating ML pipelines (21%)
  • Section 6: Learn about Monitoring ML solutions (14%)

Google Professional Machine Learning Engineer Exam FAQs

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

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

  • 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

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

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 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.

  • Online-proctored exam from a remote location
  • Onsite-proctored exam at a testing center