DSA-C03 Exam Dumps | You are building a fraud detection model using Snowflake data'. One of the features is 'transaction

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Question 124/143

You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

A. StandardScaler (Z-score normalization)

B. MinMaxScaler (Min-Max scaling)

C. RobustScaler (using interquartile range)

D. Power Transformer (Yeo-Johnson or Box-Cox)

E. No scaling is needed as tree-based models are robust to skewed data.

Correct Answer: C,D

RobustScaler is suitable for handling outliers as it uses the interquartile range, which is less sensitive to extreme values than the mean and standard deviation used by StandardScaler. PowerTransformer can also be useful for transforming skewed data to a more Gaussian-like distribution, which can improve the performance of some machine learning models. While tree-based models are generally more robust to skewed data than other models, scaling can still improve convergence speed or performance, especially when combined with other preprocessing techniques or models that are sensitive to feature scaling. Therefore, E is not a great choice. Using RobustScaler and PowerTransformer will lead to a better performance of model.

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