Google Machine Learning Engineer Professional Machine Learning Engineer

A Professiona Machine Learning Engineer builds, evaluates, productionizes, and optimizes Mmodels by using Google Cloud technologies and knowledge of proven models and techniques. The MEngineer handles large, complex datasets and creates repeatable, reusable code. The MEngineer considers responsible AI and fairness throughout the Mmodedevelopment process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The MEngineer has strong programming skills and experience with data platforms and distributed data processing tools. The MEngineer is proficient in the areas of modearchitecture, data and Mpipeline creation, and metrics interpretation. The MEngineer is familiar with foundationaconcepts of MLOps, application development, infrastructure management, data engineering, and data governance. The MEngineer makes Maccessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the MEngineer 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 Professiona Machine Learning Engineer exam assesses your ability to:

Architect low-code Msolutions

Collaborate within and across teams to manage data and models

Scale prototypes into Mmodels

Serve and scale models

Automate and orchestrate Mpipelines

Monitor Msolutions

Length: Two hours

Registration fee: $200 (plus tax where applicable)

Language: English

Exam format: 50-60 multiple choice and multiple select questions

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

Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

Certification Renewa/ Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, alGoogle 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.

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.

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.

Section 1: Architecting low-code Msolutions (~12% of the exam)

Section 2: Collaborating within and across teams to manage data and models (~16% of the exam)

Section 3: Scaling prototypes into Mmodels (~18% of the exam)

Section 4: Serving and scaling models (~19% of the exam)

Section 5: Automating and orchestrating Mpipelines (~21% of the exam)

Section 6: Monitoring Msolutions (~14% of the exam)

Familiarize yourself with the format of questions and example content that may be covered on the Machine Learning Engineer exam.

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