Crucial Exams

GCP Professional Data Engineer Practice Question

You are building a churn-propensity model in BigQuery ML. The training table contains a numeric column named total_spend that ranges from a few cents to several thousand US dollars, and its distribution is extremely skewed. Business analysts want the model to treat spend as four ordered categories-"< 25", "25-100", "100-500", and ">= 500"-so that coefficients are learned per range and the same transformation is applied when the model is used for prediction. Inside the CREATE MODEL statement you plan to express this logic in a TRANSFORM clause. Which BigQuery ML manual preprocessing function should you use to implement the required transformation?

  • Apply ML.ROBUST_SCALER() to normalize total_spend using its interquartile range.

  • Apply ML.BUCKETIZE() with the split points in the TRANSFORM clause.

  • Apply ML.MAX_ABS_SCALER() to rescale total_spend between -1 and 1 before training.

  • Apply ML.FEATURE_CROSS() to create four spend category indicators from total_spend.

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