|Exam Format||Multiple-choice and scenario-based questions|
|Exam Duration||Four hours|
|Exam Fee||$200 USD|
|Prerequisites||Strong understanding of machine learning algorithms, programming languages such as Python, and GCP services such as Compute Engine and Kubernetes Engine|
|Recommended Experience||At least three years of experience in machine learning, including one year of experience designing and implementing ML models on GCP|
|Exam Topics||Data Preprocessing and Feature Engineering, Model Selection and Hyperparameter Tuning, Distributed Training and Serving, Model Deployment and Serving, Monitoring, Logging, and Debugging, Security and Privacy|
|Recommended Training Courses||Google Cloud Machine Learning Engineer, Machine Learning with TensorFlow on Google Cloud Platform, Production Machine Learning Systems|
The Google Professional Machine Learning Engineer exam is a certification exam that validates the skills and expertise of professionals in designing, building, and deploying scalable machine learning models on Google Cloud Platform (GCP). The exam tests the candidate’s ability to design and implement scalable and efficient machine learning solutions, as well as their understanding of data preprocessing, model selection, hyperparameter tuning, and model deployment.
Here are some key details about the Google Professional Machine Learning Engineer exam:
The Google Professional Machine Learning Engineer exam covers a wide range of topics related to machine learning and GCP. Here are some of the key topics that candidates should be familiar with:
Google recommends that candidates review the official exam guide, take the relevant training courses, and gain hands-on experience with GCP services before taking the exam. Here are some recommended training courses for preparing for the exam:
In addition to training courses, candidates can also take practice exams and use study materials such as books and online tutorials to prepare for the exam.
The Google Professional Machine Learning Engineer exam is a comprehensive certification that validates the skills and knowledge of professionals in designing, building, and deploying scalable machine learning models on GCP. By passing the exam, candidates demonstrate their proficiency in machine learning and their ability to implement scalable and efficient ML solutions on GCP.
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