Exam Professional Machine Learning Engineer topic 1 question 167 discussion

ML.ONE_HOT_ENCODER transforms the categorical features into one-hot encoded values. You then select these encoded categorical features along with the non-categorical features to create your model. This is the most common approach for handling categorical features in BigQuery ML for fast deployment.

Using the CREATE MODEL statement with the categorical and non-categorical features directly (Option C) is the simplest, fastest, and most effective way to build and deploy your model in BigQuery ML

The create_model statement automatically one-hot encodes categorical features. https://cloud.google.com/bigquery/docs/auto-preprocessing This may not be the best solution in terms of transparency, but the question asked for the "fastest" solution

BigQuery ML automatically handles categorical features. When you use the CREATE MODEL statement, it recognizes categorical columns and applies appropriate encoding (e.g., one-hot encoding or embeddings) under the hood.

TRANSFORM is used to transform the input for both learning and inference. ONE_HOT_ENCODER can also be used within TRANSFORM. The other options require conversion on the input in prediction. A is correct.

CREATE OR REPLACE MODEL `project.dataset.model_name` OPTIONS(model_type='logistic_reg') AS SELECT *, TRANSFORM( product_category, payment_method USING ML.ONE_HOT_ENCODER(product_category) AS encoded_product_category, ML.ONE_HOT_ENCODER(payment_method) AS encoded_payment_method ) FROM `project.dataset.table_name`;

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

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.

Only B and D make sense. Between the two, after reading the use case of multi-hot encoding (https://cloud.google.com/bigquery/docs/auto-preprocessing#feature-transform), I would tend towards B, since one-hot encoding is preferred over in case of using non-numerical, non-array features (product category and payment methods are often respresented as such); multi-hot encoding is preferred in case of non-numerical, array features, which is not the case here.