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…

While imputing missing values is important, option C is more comprehensive by explicitly including 'Resample data by hour' first, which is critical for establishing a consistent hourly frequency for time series analysis like ARIMA.

Amazon SageMaker Autopilot automates model building and tuning, but it doesn't primarily focus on the explicit data preparation steps like resampling and imputation needed to *verify suitability of an ARIMA approach* based on data regularity issues.

Amazon SageMaker Data Wrangler is designed for data preparation and can be used to import data, resample it by hour to create a consistent time series index, and then impute missing values. This transformation is crucial for ensuring the data meets the regularity requirements for ARIMA modeling, thereby verifying its suitability.

Although SageMaker Autopilot has time series forecasting capabilities, it's not the primary tool for manually performing specific data preparation steps (resampling, imputation) required to explicitly *verify* the underlying data's suitability for a traditional ARIMA model.