Professional-Machine-Learning-Engineer Exam Dumps | Your team trained and tested a DNN regression model with good results. Six months after deployment, the

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Question 70/123

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?

A. Create alerts to monitor for skew, and retrain the model.

B. Perform feature selection on the model, and retrain the model with fewer features

C. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

D. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

Correct Answer: A

Data drift doesn't necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew

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