Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 432 discussion

https://docs.aws.amazon.com/quicksight/latest/user/sagemaker-integration.html

Machine Learning = Sage Maker so B for least operational overhead A and D are not right technologies. C is possible but with more overhead of using AMI even if you can get OpenSearch to visualize the data somehow which I don't think is possible without massive overhead

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

Question keyword "machine learning", answer keyword "Amazon SageMaker". Choose B. Use Amazon QuickSight for visualization. See "Gaining insights with machine learning (ML) in Amazon QuickSight" at https://docs.aws.amazon.com/quicksight/latest/user/making-data-driven-decisions-with-ml-in-quicksight.html

Sagemaker.

Business intelligence, visualiations = AmazonQuicksight ML = Amazon SageMaker

Most likely B.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly.

Amazon SageMaker is a fully managed service that provides a complete set of tools and capabilities for building, training, and deploying ML models. It simplifies the end-to-end ML workflow and reduces operational overhead by handling infrastructure provisioning, model training, and deployment. To visualize the data and integrate it into business intelligence dashboards, Amazon QuickSight can be used. QuickSight is a cloud-native business intelligence service that allows users to easily create interactive visualizations, reports, and dashboards from various data sources, including the augmented data generated by the ML models.

ML== SageMaker

B sagemaker provide deploy ml models