Exam AWS Certified Machine Learning - Specialty topic 1 question 347 discussion

Data Wrangler also provides a third option to detect multicollinearity in your dataset facilitated via Principal Component Analysis (PCA). PCA measures the variance of the data along different directions in the feature space. The ordered list of variances, also known as the singular values, can inform about multicollinearity in your data. https://aws.amazon.com/blogs/machine-learning/detect-multicollinearity-target-leakage-and-feature-correlation-with-amazon-sagemaker-data-wrangler/

"evaluate the contribution each feature of a training dataset makes to the prediction of the target" -> B.

The question is asking for contribution of each feature not to view only the features that make the highest contribution which .5 and 1 scores shows.

Should be B

B is the correct answer. The most effective way for ML engineers to assess the contribution that each feature in the training dataset makes to the prediction of the target variable is to use the B. Amazon SageMaker Data Wrangler Quick Model Visualization to find feature importance scores between 0.5 and 1. This method provides a quick visualization of the importance of each feature and can be used to make selection decisions. Other selections A uses Principal Component Analysis (PCA) to calculate variance, but does not directly assess the contribution of a feature. C identifies bias and is not suitable for assessing contribution. D is related to data preparation but is not a method to evaluate the contribution of a feature.

This approach directly addresses the need to evaluate the contribution of each feature to the prediction of the target variable by providing feature importance scores, which are crucial for understanding and selecting the most impactful features.

Should be D.