MLS-C01 · Question #335
MLS-C01 Question #335: Real Exam Question with Answer & Explanation
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Question
A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model. Which preprocessing step will meet these requirements?
Options
- AUse the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data.
- BLoad the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler
- CReduce the dimensionality of the dataset by removing the features that have the highest
- DReduce the dimensionality of the dataset by removing the features that have the lowest
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