AWS Certified Machine Learning – Specialty — Question 315

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?

Answer options

• 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.

Correct answer: A

Explanation

To verify if an ARIMA model is suitable, the developer must analyze the time series characteristics, which requires a continuous dataset without missing values. Amazon SageMaker Data Wrangler provides the capability to import the data, impute the missing hourly values, and perform Seasonal Trend decomposition to evaluate the trend and seasonal components of the time series. Amazon SageMaker Autopilot does not offer ARIMA as a standard selectable ML problem, making options B and D incorrect, while option C loses the valuable hourly granularity of the dataset.