A company has a production Amazon Aurora Db cluster that serves both online transaction processing (OLTP) transactions and compute-intensive reports. The reports run for 10% of the total cluster uptime while the OLTP transactions run all the time. The company has benchmarked its workload and determined that a six-node Aurora DB cluster is appropriate for the peak workload.
The company is now looking at cutting costs for this DB cluster, but needs to have a sufficient number of nodes in the cluster to support the workload at different times. The workload has not changed since the previous benchmarking exercise.
How can a Database Specialist address these requirements with minimal user involvement?

A. Split up the DB cluster into two different clusters: one for OLTP and the other for reporting. Monitor and set up replication between the two clusters to keep data consistent.
B. Review all evaluate the peak combined workload. Ensure that utilization of the DB cluster node is at an acceptable level. Adjust the number of instances, if necessary.
C. Use the stop cluster functionality to stop all the nodes of the DB cluster during times of minimal workload. The cluster can be restarted again depending on the workload at the time.

D. Set up automatic scaling on the DB cluster. This will allow the number of reader nodes to adjust automatically to the reporting workload, when needed.