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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 367 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 367
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A finance company has collected stock return data for 5,000 publicly traded companies. A financial analyst has a dataset that contains 2,000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns.

Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use the linear leaner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.
  • B. Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.
  • C. Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick mode's feature importance scores.
  • D. Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.
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Suggested Answer: D 🗳️
by MultiCloudIronMan at Oct. 30, 2024, 11:21 p.m.
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MultiCloudIronMan
2 weeks, 1 day ago
Selected Answer: D
This approach leverages the automation capabilities of SageMaker Autopilot and the detailed analysis provided by SageMaker Clarify, ensuring an efficient and effective solution for identifying the most valuable attributes for predicting stock returns.
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