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You work for a retail company.

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You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:

• Customer_id

• Product_id

• Date

• Days_since_last_purchase (measured in days)

• Average_purchase_frequency (measured in 1/days)

• Purchase (binary class, if customer purchased product on the Date)

You need to interpret your models results for each individual prediction. What should you do?

A.

Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.

B.

Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model

to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.

C.

Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.

D.

Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.

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