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Exam AWS Certified Machine Learning - Specialty topic 1 question 308 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 308
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

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?

  • A. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data.
  • B. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
  • C. Reduce the dimensionality of the dataset by removing the features that have the highest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Standard Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
  • D. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
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Suggested Answer: B 🗳️
by delfoxete at Feb. 6, 2024, 7:55 p.m.
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AMEJack
7 months ago
Selected Answer: C
Standards calling for PCA. https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-data-wrangler-for-dimensionality-reduction/
upvoted 1 times
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MultiCloudIronMan
7 months, 1 week ago
Selected Answer: B
PCA Requires minmax scaling.
upvoted 1 times
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Peter_Hsieh
12 months ago
Selected Answer: B
With support for PCA in Data Wrangler, you can now easily reduce the dimensionality of a high dimensional data set in only a few clicks. You can access PCA by selecting Dimensionality Reduction from the “Add step” workflow. https://aws.amazon.com/about-aws/whats-new/2022/10/amazon-sagemaker-data-wrangler-reduce-dimensionality-pca/
upvoted 1 times
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vkbajoria
1 year, 1 month ago
Selected Answer: B
PCA requires scaling => use min-max scaler
upvoted 1 times
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AIWave
1 year, 1 month ago
Selected Answer: B
A: No - PCA requires feature scaling to remove dominance of high value variables B: Yes - Scaling addresses the issue of features with different ranges + PCA does feature reduction C: No - manual removal may lead to removal of important features D: No - manual removal may lead to removal of important features
upvoted 2 times
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vkbajoria
1 year, 1 month ago
Selected Answer: C
Standard scaler is better for PCA
upvoted 2 times
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delfoxete
1 year, 2 months ago
Selected Answer: B
C performs a standard transformation and D removes variables with low correlations which will delete important features
upvoted 2 times
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