AWS Certified Solutions Architect - Associate
Get started today
Ultimate access to all questions.
Deep dive into the quiz with AI chat providers.
We prepare a focused prompt with your quiz and certificate details so each AI can offer a more tailored, in-depth explanation.
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?
Other
Community
UAnonymous
A
Use AWS Glue to create an ML transform to build and train models. Use Amazon OpenSearch Service to visualize the data.
B
Use Amazon SageMaker to build and train models. Use Amazon QuickSight to visualize the data.
C
Use a pre-built ML Amazon Machine Image (AMI) from the AWS Marketplace to build and train models. Use Amazon OpenSearch Service to visualize the data.
D
Use Amazon QuickSight to build and train models by using calculated fields. Use Amazon QuickSight to visualize the data.
Explanation:
Explanation
Correct Answer: B
Why Option B is correct:
-
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.
-
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.
-
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.
Comments
Loading comments...