Answer: Use Amazon SageMaker to build and train models. Use Amazon QuickSight to visualize the data.

## Explanation **Correct Answer: B** **Why Option B is correct:** 1. **Amazon SageMaker** is AWS's fully managed machine learning service that provides everything needed to build, train, and deploy ML models with minimal operational overhead. It handles infrastructure management, scaling, and maintenance. 2. **Amazon QuickSight** is AWS's fully managed business intelligence service that integrates seamlessly with SageMaker. It can directly consume ML predictions and augmented data from SageMaker models and visualize them in dashboards. 3. **Least operational overhead**: Both SageMaker and QuickSight are fully managed services, requiring minimal infrastructure management, patching, or scaling operations. **Why other options are incorrect:** **Option A**: AWS Glue ML transforms are limited to specific data preparation tasks and not suitable for building complex ML models. OpenSearch Service is more for search and analytics, not optimized for business intelligence dashboards. **Option C**: Using a pre-built ML AMI requires managing EC2 instances, scaling, patching, and maintenance, which increases operational overhead. OpenSearch Service is not a dedicated BI tool. **Option D**: Amazon QuickSight's calculated fields are for basic data transformations, not for building and training complex ML models. It lacks the comprehensive ML capabilities needed for this scenario. **Key AWS Services:** - **Amazon SageMaker**: Fully managed ML service - **Amazon QuickSight**: Fully managed BI service with ML integration - **Integration**: SageMaker models can be directly consumed by QuickSight for visualization This solution provides an end-to-end managed ML and BI pipeline with minimal operational overhead.

Author: LeetQuiz Editorial Team