Topic 1 Question 367
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?
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.
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.
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.
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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- 正解だと思う選択肢: 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.
👍 2MultiCloudIronMan2024/10/30 - 正解だと思う選択肢: D
A. No, un modelo lineal requiere calificar manualmente coeficientes y puede no captar relaciones complejas. B. No, random forest da importancia Gini pero implica más configuración y ajuste. C. No, la visualización rápida en Data Wrangler es útil pero no automatiza la selección de 2000 atributos. D. Sí, Autopilot con un informe de Clarify automatiza el entrenamiento y extrae la importancia de las características con mínimo esfuerzo.
👍 1italiancloud20252025/02/18
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