Google Professional Machine Learning Engineer
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You work for a magazine distributor and need to build a machine learning model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical subscription and customer data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You are now required to determine which customer attribute (e.g., age, gender, last purchase date) has the most predictive power for each prediction served by the model. What approach should you take to achieve this?
A
Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
B
Use Vertex Explainable AI. Submit each prediction request with the explain keyword to retrieve feature attributions using the sampled Shapley method.
C
Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
D
Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.