Q48 — AWS SAA-C03 Ch.5

Question 48 of 65 | ← Chapter 5

Q348. An ecommerce company wants to use machine learning (ML) algorithms to build and train models.The company will use the models to visualize complex scenarios and to detect trends in customer data. The architecture team wants to integrate its ML models with a reporting platform to analyze the augmented data and use the data directly in its business intelligence dashboards. Which solution will meet these requirements with the LEAST operational overhead?

Correct Answer: B. Use Amazon SageMaker to build and train models. Use Amazon QuickSight to visualize the dat

Explanation

B. Use Amazon SageMaker to build and train models. Use Amazon QuickSight to visualize the data:Amazon SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy ML models quickly and easily. Using Amazon SageMaker, the ecommerce company can create and train their ML models without worrying about operational overhead. After training the models, they can use Amazon QuickSight to visualize complex scenarios and detect trends in customer data. This provides an end-to-end solution that requires minimal operational overhead. Option A involves using AWS Glue to create an ML transform to build and train models, which may require additional configuration and setup time compared to using SageMaker. Furthermore, while Amazon OpenSearch Service can be used for data visualization, it is primarily designed as a search and analytics engine and may not provide all of the functionality needed for complex data visualization. Option C involves using a pre-built ML Amazon Machine Image (AMI) from the AWS Marketplace to build and train models, which may have additional licensing and cost implications compared to using SageMaker.Option D involves using Amazon QuickSight to build and train models by using calculated fields, which may not be as scalable or robust as using dedicated ML tools like SageMaker.