Google Professional Machine Learning Engineer Certification
Length: Two hours
Language: English
Exam format:Â 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:Â 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.
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Google Professional Machine Learning Engineer Certification details
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
