Crucial Exams

GCP Professional Data Engineer Practice Question

Your analytics team stores 50 GB of transaction data in BigQuery and wants to train a logistic-regression churn model with BigQuery ML. One input column, customer_segment, contains string values such as "Retail", "SMB", and "Enterprise". The model must serve predictions through ML.PREDICT without any additional preprocessing code. Inside the TRANSFORM clause of the CREATE MODEL statement, which expression should you use to convert customer_segment into a set of binary indicator features that the model can consume at both training and prediction time?

  • ML.FEATURE_CROSS([customer_segment]) AS customer_segment_features

  • SAFE_CAST(customer_segment AS INT64) AS customer_segment_features

  • ML.BUCKETIZE(customer_segment, ['Retail','SMB','Enterprise']) AS customer_segment_features

  • ML.ONE_HOT_ENCODER(customer_segment) AS customer_segment_features

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