Exam AWS Certified Machine Learning - Specialty All Questions

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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.

Show Suggested Answer

Suggested Answer: B 🗳️

by delfoxete at Feb. 6, 2024, 7:55 p.m.

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Comments

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AMEJack

9 months, 3 weeks ago

Selected Answer: C

Standards calling for PCA. https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-data-wrangler-for-dimensionality-reduction/

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MultiCloudIronMan

10 months ago

Selected Answer: B

PCA Requires minmax scaling.

upvoted 1 times

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Peter_Hsieh

1 year, 2 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 2 times

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vkbajoria

1 year, 4 months ago

Selected Answer: B

PCA requires scaling => use min-max scaler

upvoted 1 times

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AIWave

1 year, 4 months 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, 4 months ago

Selected Answer: C

Standard scaler is better for PCA

upvoted 2 times

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delfoxete

1 year, 5 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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