Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You are working with a dataset that contains customer transactions and you need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML and export it to Cloud Storage for online prediction. The input data includes several categorical features, such as product category and payment method. Considering that you want to handle these categorical features effectively and deploy the model as quickly as possible, what should you do?
A
Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.
B
Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.
C
Use the CREATE MODEL statement and select the categorical and non-categorical features.
D
Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.