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Professional Machine Learning Engineer

A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.

*Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.

The Professional Machine Learning Engineer exam assesses your ability to:

  • Architect low-code ML solutions
  • Collaborate within and across teams to manage data and models
  • Scale prototypes into ML models
  • Serve and scale models
  • Automate and orchestrate ML pipelines
  • Monitor ML solutions

The Professional Machine Learning Engineer exam does not cover generative AI, as the tools used to develop generative AI-based solutions are evolving quickly. If you are interested in generative AI, please refer to the Introduction to Generative AI Learning Path (all audiences) or the Generative AI for Developers Learning Path (technical audience). If you are a partner, please refer to the Gen AI partner courses: Introduction to Generative AI Learning Path, Generative AI for ML Engineers, and Generative AI for Developers.

About this certification exam

Length: Two hours

Registration fee: $200 (plus tax where applicable)

Language: English

Exam format: 50-60 multiple choice and multiple select questions

Exam delivery method:

a. Take the online-proctored exam from a remote location, review the online testing requirements.

b. Take the onsite-proctored exam at a testing center, locate a test center near you

Prerequisites: None

Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.

Exam overview

Step 1: Get real world experience

Before attempting the Machine Learning Engineer exam, it's recommended that you have 3+ years of hands-on experience with Google Cloud products and solutions. Ready to start building? Explore the Google Cloud Free Tier for free usage (up to monthly limits) of select products.

Try the Google Cloud Free Tier

Step 2: Understand what's on the exam

The exam guide contains a complete list of topics that may be included on the exam. Review the exam guide to determine if your skills align with the topics on the exam.

See current exam guide

Step 3: Review the sample questions

Familiarize yourself with the format of questions and example content that may be covered on the Machine Learning Engineer exam.

Review sample questions

Step 4: Round out your skills with training

Prepare for the exam by following the Machine Learning Engineer learning path. Explore online training, in-person classes, hands-on labs, and other resources from Google Cloud.

Prepare for the exam with Googlers and certified experts. Get valuable exam tips and tricks, as well as insights from industry experts.

Explore Google Cloud documentation for in-depth discussions on the concepts and critical components of Google Cloud.

Learn about designing, training, building, deploying, and operationalizing secure ML applications on Google Cloud using the Official Google Cloud Certified Professional Machine Learning Engineer Study Guide. This guide uses real-world scenarios to demonstrate how to use the Vertex AI platform and technologies such as TensorFlow, Kubeflow, and AutoML, as well as best practices on when to choose a pretrained or a custom model.

Step 5: Schedule an exam

Register and select the option to take the exam remotely or at a nearby testing center.