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…

Question

Options

• ACreate an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in
• BCreate a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data
• CCreate an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget
• DCreate an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight

• A
  12% (4)
• B
  3% (1)
• C
  79% (27)
• D
  6% (2)

Explanation

Amazon QuickSight has native, built-in ML-powered forecasting as a first-class feature - you can add a forecasting insight directly to a visual with a few clicks, no external model required. Option C leverages this capability with the least operational overhead and cost. Option A mentions a 'custom field,' which is not the right mechanism for forecasting in QuickSight. Option B proposes building a custom D3.js dashboard, which requires significant development and maintenance effort - not cost-effective. Option D involves training and hosting an Amazon SageMaker model solely for forecasting, which is overkill when QuickSight already includes ML forecasting natively, and it introduces unnecessary cost and complexity.

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