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Exam AWS Certified Machine Learning - Specialty topic 1 question 316 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 316
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.

Which solution will meet this requirement with the LEAST development effort?

  • A. Use SageMaker Data Wrangler to perform a Gini importance score analysis.
  • B. Use a SageMaker notebook instance to perform principal component analysis (PCA).
  • C. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
  • D. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
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Suggested Answer: A 🗳️
by F1Fan at March 10, 2024, 4:47 p.m.
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Peter_Hsieh
5 months, 4 weeks ago
Selected Answer: A
The Quick model of Data Wrangler calculates feature importance for each feature using the Gini importance method. https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-analyses.html
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AIWave
7 months, 2 weeks ago
Selected Answer: A
Wrangler allows to calculate feature importance scores using Gini importance
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F1Fan
7 months, 2 weeks ago
A. Use SageMaker Data Wrangler to perform a Gini importance score analysis. By using the Gini importance score analysis in Data Wrangler, the ML developer can obtain importance scores for each feature of the sales dataset with minimal development effort, as it is a built-in functionality with a visual interface. This approach requires no coding or additional setup, making it the least effort-intensive solution compared to the other options involving custom coding or separate analyses.
upvoted 1 times
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