How should the data engineer make sure that the larger customer workloads do NOT interfere with the smaller customer workloads?

A data engineer is running a DWH on a 25-node Redshift cluster of a SaaS service. The data engineer needs to build a dashboard that will be used by customers. Five big customers represent 80% of usage, and there is a long tail of dozens of smaller customers. The data engineer has selected the dashboarding tool.

How should the data engineer make sure that the larger customer workloads do NOT interfere with the smaller customer workloads?
A . Apply query filters based on customer-id that can NOT be changed by the user and apply distribution keys on customer-id.
B . Place the largest customers into a single user group with a dedicated query queue and place the rest of the customers into a different query queue.
C . Push aggregations into an RDS for Aurora instance. Connect the dashboard application to Aurora rather than Redshift for faster queries.
D . Route the largest customers to a dedicated Redshift cluster. Raise the concurrency of the multi-tenant Redshift cluster to accommodate the remaining customers.

Answer: D