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Exam Code: Professional-Machine-Learning-Engineer
Exam Name: Google Professional Machine Learning Engineer
Updated: Aug 09, 2026
Q & A: 350 Questions and Answers
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The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.
The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.
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| Section | Objectives |
|---|---|
| Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance |
| Serving and scaling models | - Online prediction (Vertex AI Prediction) - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Batch prediction |
| Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Model retraining strategies - Performance monitoring and drift detection |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) |
| Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
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