Professional Machine Learning Engineer
A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes AI solutions by using Google Cloud capabilities and knowledge of conventional ML approaches. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer designs and operationalizes generative AI solutions based on foundational models. The ML Engineer considers responsible AI practices, and collaborates closely with other job roles to ensure the long-term success of AI-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, generative AI, 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 enables teams across the organization to use AI solutions. 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 AI 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 AI solutions
This version of the Professional Machine Learning Engineer exam covers tasks related to generative AI, including building AI solutions using Model Garden and Vertex AI Agent Builder, and evaluating generative AI solutions.
To learn more about Google Cloud’s generative AI services, go to Google Cloud Skills Boost to see the Introduction to Generative AI Learning Path (all audiences) or the Generative AI for Developers Learning Path (technical audience). If you are a partner, refer to the Gen AI partner courses: Introduction to Generative AI Learning Path, Generative AI for ML Engineers and Generative AI for Developers. For additional learning, refer to product-specific Gen AI learning offerings such as Explore and Evaluate Models using Model Garden, Vertex AI Agent Builder path (partners), Integrate Search in Applications using Vertex AI Agent Builder, and Generative Chat App with Vertex AI Agent Builder and Dialogflow.
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.
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.
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.
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.
Review exam
terms and conditions
and
data sharing policies.