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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

  • Demonstrate your expertise and practical experience in cloud-based ML solutions.
  • Enhance your job prospects and earning potential in the field of machine learning engineering.
  • Gain recognition and credibility among peers and potential employers.
  • Stay current and competitive in the rapidly evolving ML landscape.

  • Google Cloud Professional Machine Learning Engineer Exam Guide
  • Google Cloud training and documentation
  • Practice exams and study materials

The exam fee can vary depending on your location and testing provider, but typically falls around USD $200.


  • Delivery method: Online proctored exam.
  • Question format: Multiple choice and scenario-based questions.
  • Number of questions: 50-60 questions.
  • Duration: 2 hours).

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%)

The exam assesses your ability to design, build, deploy, and manage machine learning models in production environments using Google Cloud Platform (GCP). It validates your expertise and practical experience in applying ML solutions to real-world problems.


This exam is ideal for individuals with 3+ years of experience in ML, including 1+ year specifically with Google Cloud and strong skills in:

  • ML fundamentals and algorithms.
  • Building and deploying production ML models.
  • GCP services like Cloud Storage, BigQuery, Cloud AI Platform, and AI Platform Pipelines.
  • Designing and implementing ML pipelines for data, training, evaluation, and deployment.
  • MLOps principles like model monitoring, version control, and CI/CD.
  • Collaboration and communication with cross-functional teams.