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MLS-C01 · Question #394

MLS-C01 Question #394: Real Exam Question with Answer & Explanation

The correct answer is D: Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify. Amazon SageMaker Autopilot automates the entire machine learning process, from data preprocessing to model selection and tuning. It builds multiple models and provides feature importance scores based on the selected model's performance. Additionally, SageMaker Clarify is integrat

ML Implementation and Operations

Question

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?

Options

  • AUse the linear leaner algorithm in SageMaker to train a linear regression model to predict the stock
  • BUse random forest regression in SageMaker to train a model to predict the stock returns. Identify
  • CUse an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns.
  • DUse Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify

Explanation

Amazon SageMaker Autopilot automates the entire machine learning process, from data preprocessing to model selection and tuning. It builds multiple models and provides feature importance scores based on the selected model's performance. Additionally, SageMaker Clarify is integrated with Autopilot to provide feature importance explanations, helping the financial analyst identify the top 15 most valuable attributes with minimal operational overhead. This approach reduces the need for manual model selection and tuning, making it highly efficient.

Topics

#SageMaker Autopilot#AutoML#Feature Importance#Operational Overhead

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