A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.
The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.
How should the developer verify the suitability of an ARIMA approach?

A. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.

B. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

C. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.

D. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.








 

Suggested Answer: C

Community Answer: C




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


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