Google Cloud Certified
Machine Learning Engineer - Professional
Demonstrate your expertise in designing, building, and deploying scalable machine learning models on Google Cloud for real-world business solutions.
Exam overview
| Category | Professional |
|---|---|
| Exam duration | 2 hours |
| Cost | $200 (plus tax where applicable) |
| Exam format | 50-60 multiple choice and multiple select questions |
| Recommended experience | 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud. |
| Testing Options | Online (proctored) or in-person at a test center |
| Language | English |
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:
*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
Key FAQs to help you get started
There are no formal prerequisites, but prior experience with machine learning, TensorFlow, and Google Cloud AI tools is recommended.
Yes, our training includes practical hands-on labs and mock exams to help you prepare for real-world scenarios and the certification exam.
Yes, candidates should be proficient in Python and have experience with frameworks like TensorFlow and Scikit-learn.
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