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Seems that the performance of the critical ETL cluster should not be affected when using data sharing, so the answer is likely A: https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html Supporting different kinds of business-critical workloads – Use a central extract, transform, and load (ETL) cluster that shares data with multiple business intelligence (BI) or analytic clusters. This approach provides read workload isolation and chargeback for individual workloads. You can size and scale your individual workload compute according to the workload-specific requirements of price and performance. https://docs.aws.amazon.com/redshift/latest/dg/considerations.html The performance of the queries on shared data depends on the compute capacity of the consumer clusters.
A as Redshift data sharing allows you to share live data across Redshift clusters without having to duplicate the data. This feature enables the sales team to access the data from the ETL cluster directly without interrupting the critical analysis tasks or overloading the ETL cluster's resources. The sales team can join this shared data with their own data in the BI cluster efficiently.
"The solution must minimize usage of the computing resources of the ETL cluster." That is key. You shouldn't use ETL cluster, so unload data to S3 and run queries in a separate Redshift Spectrum database. ETL cluster do nothing meanwhile.