Google Professional Machine Learning Engineer
Last Update Dec 22, 2024
Total Questions : 285
To help you prepare for the Professional-Machine-Learning-Engineer Google exam, we are offering free Professional-Machine-Learning-Engineer Google exam questions. All you need to do is sign up, provide your details, and prepare with the free Professional-Machine-Learning-Engineer practice questions. Once you have done that, you will have access to the entire pool of Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer test questions which will help you better prepare for the exam. Additionally, you can also find a range of Google Professional Machine Learning Engineer resources online to help you better understand the topics covered on the exam, such as Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer video tutorials, blogs, study guides, and more. Additionally, you can also practice with realistic Google Professional-Machine-Learning-Engineer exam simulations and get feedback on your progress. Finally, you can also share your progress with friends and family and get encouragement and support from them.
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
TESTED 22 Dec 2024
