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MLS-C01 · Question #355

MLS-C01 Question #355: Real Exam Question with Answer & Explanation

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Data Engineering

Question

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training dataset is missing values for the target variable. The company stores the training dataset as JSON files. The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset. Which approach will meet these requirements with the LEAST development effort?

Options

  • AImpute the missing values by using the linear regression method. Use the entire dataset and the
  • BReplace the missing values with not a number (NaN). Use the entire dataset and the encoded
  • CImpute the missing values by using a forward fill. Use the entire dataset and the imputed values
  • DImpute the missing values by using the mean value. Use the entire dataset and the imputed

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Topics

#Data Imputation#Missing Values#SageMaker DeepAR#Data Preprocessing
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