Topic 1 Question 167
You are working with a dataset that contains customer transactions. 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. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?
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.
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.
Use the CREATE MODEL statement and select the categorical and non-categorical features.
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.
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- 正解だと思う選択肢: B
Given the goal of quickly deploying the model for predicting customer purchase behavior while handling categorical features, option 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" seems to be the most appropriate. This approach directly handles the encoding of categorical features using one-hot encoding and selects the necessary features for model creation, ensuring efficient utilization of categorical data in the BigQuery ML model.
👍 1pikachu0072024/01/10 - 正解だと思う選択肢: B
When the TRANSFORM clause is present, only output columns from the TRANSFORM clause are used in training. Any results from query_statement that don't appear in the TRANSFORM clause are ignored. https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create#transform so if you want TRANSFORM then use TRANSFORM for both categorical and non-categorical features
👍 1BlehMaks2024/01/12
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