AWS Certified Machine Learning – Specialty — Question 148

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

Answer options

• A. Autoregressive Integrated Moving Average (AIRMA)
• B. Exponential Smoothing (ETS)
• C. Convolutional Neural Network - Quantile Regression (CNN-QR)
• D. Prophet

Correct answer: C

Explanation

The Convolutional Neural Network - Quantile Regression (CNN-QR) is the most suitable algorithm for this scenario as it can effectively model complex relationships between various features like weather and occupancy, while also providing quantile forecasts. The other options, such as AIRMA and ETS, are more suited for simpler time series data and may not capture the complexity of the inputs as well as CNN-QR. Prophet is also less capable of handling the diverse variables involved in this case.