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This exam was updated to reflect recent branding changes. Refer to the exam guide to review product names used on the exam. Back to Google Cloud Certification Professional Machine Learning Engineer A Professional Machine Learning (ML) Engineer builds, evaluates, productionizes, and optimizes AI solutions 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 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, MLOps, and metrics interpretation. The ML Engineer is familiar with the foundational concepts of prompt and context engineering, 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, tuning, monitoring, and improving traditional and generative AI (gen AI) 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 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 Register View FAQs Register View FAQs This exam was updated to 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. Quick links Train for the exam Review sample questions View exam guide Partner training for the exam Join the learning community About this certification exam Length: Two hours Registration fee: $200 (plus tax where applicable) Languages: English, Japanese 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 - search for Google Cloud. Prerequisites: None Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud. Certification renewal: Candidates may renew their certification within the renewal eligibility period. For more information about the renewal process, eligibility period, and certification validity timeline, please refer to the Renewal FAQs below. Renewal FAQs 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. Start preparing Prepare for the exam with Googlers and certified experts. Get valuable exam tips and tricks, as well as insights from industry experts. Sign up 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. Take the next step Follow the learning path Start Learning Take the next step Follow the learning path Start Learning Earn a skill badge in machine learning Start now New to Google Cloud? Get started Take a cert prep webinar Watch Cloud OnAir Earn a skill badge in machine learning Start now New to Google Cloud? 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