AWS Certified Machine Learning – Specialty — Question 141

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them?
(Choose two.)

Answer options

• A. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
• B. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
• C. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
• D. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
• E. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Correct answer: A, C

Explanation

Option A is correct because new stores lack historical data for seasonality detection, which is crucial for accurate forecasting. Option C is also correct as daily data would provide more granularity for the model, addressing the challenge of using aggregated weekly data. The other options do not directly address the main issues affecting the model's performance, such as the lack of historical context or data granularity.