Exam Name: | Google Professional Machine Learning Engineer | ||
Exam Code: | Professional-Machine-Learning-Engineer Dumps | ||
Vendor: | Certification: | Machine Learning Engineer | |
Questions: | 270 Q&A's | Shared By: | margaux |
You have successfully deployed to production a large and complex TensorFlow model trained on tabular data. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?
You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?
You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?
You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?