Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?
You work for a retail company that is using a regression model built with BigQuery ML to predict product
sales. This model is being used to serve online predictions Recently you developed a new version of the model
that uses a different architecture (custom model) Initial analysis revealed that both models are performing as
expected You want to deploy the new version of the model to production and monitor the performance over
the next two months You need to minimize the impact to the existing and future model users How should you
deploy the model?
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