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Professional Machine Learning Engineer

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.

The Professional Machine Learning Engineer 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

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 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 how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on Google Cloud with hands-on guides for developers entering the data science field: Data Science on Google Cloud Platform.

Step 5: Schedule an exam

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