A data scientist at a retail company is forecasting sales for a product over the next 3 months. After preliminary analysis, the data scientist identifies that sales are seasonal and that holidays affect sales. The data scientist also determines that sales of the product are correlated with sales of other products in the same category.
The data scientist needs to train a sales forecasting model that incorporates this information.
Which solution will meet this requirement with the LEAST development effort?

A. Use Amazon Forecast with Holidays featurization and the built-in autoregressive integrated moving average (ARIMA) algorithm to train the model.

B. Use Amazon Forecast with Holidays featurization and the built-in DeepAR+ algorithm to train the model.

C. Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the SageMaker DeepAR built-in algorithm.

D. Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the Gluon Time Series (GluonTS) toolkit.








 

Suggested Answer: B

Community Answer: B




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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