Exam Professional Machine Learning Engineer topic 1 question 289 discussion

B is the right solution. Keep in mind that it is asking for a solution where you "minimize storage and computational overhead". You end up storing more data with A and D. While in C you create more computational overhead. All solutions would work perfectly fine, but B matches best with the requirements in the question.

BQ is sufficient

While the TRANSFORM clause can perform preprocessing, it's applied during model creation, not for inference. You'll need to recalculate statistics for each inference request, increasing computational overhead.

The TRANSFORM clause in BigQuery ML allows you to directly define feature preprocessing logic (such as quantile bucketization and MinMax scaling) within the SQL query itself. This approach minimizes storage and computational overhead because: No additional storage: Statistics for preprocessing are calculated on-the-fly during model training and inference, without needing to store preprocessed data or statistics separately. Integrated workflow: The preprocessing logic is tightly coupled with the model creation process, ensuring consistency between training and inference without external dependencies.

B is the best option because: 1) TRANSFORM saves processing, storage and computation by performing feature preprocessing directly within the CREATE MODEL. 2) This method integrates preprocessing with model training, streamlining the entire process.

Docs say BQ is not suitable for full-pass transformations such as Minmax.

A) Preprocessing and staging the data in BigQuery before training and inference, is the most efficient approach because: 1) You can use BQ’s optimized processing by preprocessing data before training 2) Avoiding redundant calculations, by directly using the preprocessed data (already bucketized and scaled) for training and inference; 3) Reducing storage by keeping only preprocessed data, not raw data and statistics separately.