MLS-C01 Web TestEngine Demo

Q23 Single choice

A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features.

How should the ML engineer predict the contribution of each feature?

A
Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.
B
Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.
C
Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.
D
Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline.
Manually add the feature scores.

MLS-C01 Web TestEngine demo