A company stores revenue data in Amazon Redshift. A data analyst needs to create a dashboard so that the company's sales team can visualize historical revenue and accurately forecast revenue for the upcoming months.
Which solution will MOST cost-effectively meet these requirements?
A. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.
B. Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into Amazon Redshift. Add the new forecast information to the dashboard.
C. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget. Publish the analysis as a dashboard.
D. Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.
Which solution will MOST cost-effectively meet these requirements?
A. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.
B. Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into Amazon Redshift. Add the new forecast information to the dashboard.
C. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget. Publish the analysis as a dashboard.
D. Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.