MLS-C01 · Question #390
MLS-C01 Question #390: Real Exam Question with Answer & Explanation
The correct answer is C: Use the SageMaker Data Wrangler multicollinearity measurement features with the principal. Principal Component Analysis (PCA) is a powerful dimensionality reduction technique that helps in understanding variance in the data by transforming the feature space. It reduces the number of variables while retaining the most important information (variance). In this case, sinc
Question
A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables. The data scientist wants to understand the variance in the data along various directions in the feature space. Which solution will meet these requirements?
Options
- AUse the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation
- BUse the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to
- CUse the SageMaker Data Wrangler multicollinearity measurement features with the principal
- DUse the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by
Explanation
Principal Component Analysis (PCA) is a powerful dimensionality reduction technique that helps in understanding variance in the data by transforming the feature space. It reduces the number of variables while retaining the most important information (variance). In this case, since the data scientist wants to explore variance along different directions in the feature space, PCA is a suitable approach. PCA will create a new set of features (principal components) that explain the variance in the dataset, making it easier to identify the most relevant features for the predictive
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