Q94 — AWS AIF-C01 Ch.3

Question 94 of 100 | ← Chapter 3

A digital device company wants to forecast customer demand for memory hardware. The company has no coding expertise or knowledge of ML methodologies and therefore needs to develop a data-driven forecasting model. The company needs to analyze both internal and external data. Which solution meets these requirements?

• A. Store data in Amazon S3. Use Amazon SageMaker built-in algorithms with data from Amazon S3 to create an ML model and demand forecast. ✓
• B. Import data into Amazon SageMaker Data Wrangler. Use SageMaker built-in algorithms to create an ML model and demand forecast.
• C. Import data into Amazon SageMaker Data Wrangler. Use the Amazon Personalize Trending-Now recipe to create an ML model and demand forecast.
• D. Import data into Amazon SageMaker Canvas. Build an ML model and demand forecast by selecting values from the data within SageMaker Canvas. ✓

Correct Answer: A. Store data in Amazon S3. Use Amazon SageMaker built-in algorithms with data from Amazon S3 to create an ML model and demand forecast., D. Import data into Amazon SageMaker Canvas. Build an ML model and demand forecast by selecting values from the data within SageMaker Canvas.

Explanation

This question evaluates demand forecasting solutions for a non-technical organization. Amazon SageMaker Canvas enables no-code model building and forecasting by selecting data values directly—ideal for users without coding or ML expertise. While Amazon S3 can store data and SageMaker built-in algorithms can train models, they require technical implementation. SageMaker Data Wrangler focuses on data preparation—not end-to-end modeling—and Amazon Personalize Trending-Now is designed for recommendation use cases, not demand forecasting.