A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?
A. Create IAM groups and resource tags for each team within the company. Set up 1AM policies that control user access and actions on the Data Catalog resources.
B. Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.
C. Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
D. Create Athena query groups for each team within the company and assign users to the groups.
Which solution meets these requirements?
A. Create IAM groups and resource tags for each team within the company. Set up 1AM policies that control user access and actions on the Data Catalog resources.
B. Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.
C. Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
D. Create Athena query groups for each team within the company and assign users to the groups.