A Company Uses Amazon Redshift for Its Data Warehousing Needs

A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL. Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams. Which solution meets these requirements?

A) Increase the query priority to HIGHEST for the data analysis queue.
B) Configure the data analysis queue to enable concurrency scaling.
C) Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.
D) Use workload management query queue hopping to route the query to the next matching queue.