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 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
- Scale prototypes into ML models
- Automate and orchestrate ML pipelines
- Collaborate within and across teams to manage data and models
- Serve and scale models
- Monitor AI solutions
The new Professional Machine Learning Engineer exam in English will be live on June 1. If you plan to take the exam in English on or after June 1, review the new exam guide.
What's new?
The upcoming version of the Professional Machine Learning Engineer exam will reflect the transition from Vertex AI to Gemini Enterprise Agent Platform, updates to Google Cloud's data and analytics stack, and prioritizes Google Cloud native solutions. Please refer to the new exam guide for products covered in the new exam.
Prerequisites
Before attempting the Machine Learning Engineer exam, it's recommended that you have 3+ years of industry experience, including 1 or more years designing and managing solutions using Google Cloud.
Recommended training for this certification
Exams
- Length: Two hours
- Language: English, Japanese
- Exam format: 50-60 multiple choice and multiple select questions
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.
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.
If you plan to take the exam in English on or after June 1, review the new 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.
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
Step 5: Schedule an exam
Register and select the option to take the exam remotely or at a nearby testing center.
Recertification
Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. This exam is valid for two years. 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.