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Question 69

A large consumer goods manufacturer has the following products on sale:

* 34 different toothpaste variants

* 48 different toothbrush variants

* 43 different mouthwash variants

The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average

(ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.

Which solution should a Machine Learning Specialist apply?

    Correct Answer: B

    To forecast demand for the new product, using the Amazon SageMaker DeepAR algorithm is the most suitable solution. DeepAR is a supervised learning algorithm designed specifically for time series forecasting using recurrent neural networks (RNN). It excels in scenarios where there are multiple related time series, such as the different variants of toothpaste, toothbrushes, and mouthwash. This algorithm can utilize the entire sales history available in Amazon S3 to generate accurate forecasts, even for new products. Classical models like ARIMA may not perform as well with the large amount of diverse time series data due to their limitations in handling scalability and complexity.

Discussion
HaiHN

B https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html "...When your dataset contains hundreds of related time series, DeepAR outperforms the standard ARIMA and ETS methods. You can also use the trained model to generate forecasts for new time series that are similar to the ones it has been trained on."

ValcilioOption: B

DeepAr for new products forever!

hans1234

It is B

AjoseOOption: B

The DeepAR algorithm is a powerful time series forecasting algorithm that is designed to handle multiple time series data and can handle irregularly spaced time series data and missing values, making it a good fit for this task. Additionally, the large amount of sales history data available in Amazon S3 makes the use of a deep learning algorithm like DeepAR more appropriate.

ninomfr64Option: B

"You can also use the trained model to generate forecasts for new time series that are similar to the ones it has been trained on" https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

james2033Option: B

'autoregressive integrated moving average (ARIMA)' <--> DeepAR. https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

loictOption: B

B - DeepAR is based on GluonTS, and can use multiple time series for learning

Mickey321Option: B

Option B

Shailendraa

B https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

ac427

This is the same question as Topic 2 Q4