A company uses an on-premises data analytics platform. The system is highly available in a fully redundant configuration across 12 servers in the company’s data center. The system runs scheduled jobs, both hourly and daily, in addition to one-time requests from users. Scheduled jobs can take between 20 minutes and 2 hours to finish running and have tight SLAs. The scheduled jobs account for 65% of the system usage. User jobs typically finish running in less than 5 minutes and have no SLA. The user jobs account for 35% of system usage. During system failures, scheduled jobs must continue to meet SLAs. However, user jobs can be delayed. A solutions architect needs to move the system to Amazon EC2 instances and adopt a consumption-based model to reduce costs with no long-term commitments. The solution must maintain high availability and must not affect the SLAs. Which solution will meet these requirements MOST cost-effectively? A. Split the 12 instances across two Availability Zones in the chosen AWS Region. Run two instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run four instances in each Availability Zone as Spot Instances. B. Split the 12 instances across three Availability Zones in the chosen AWS Region. In one of the Availability Zones, run all four instances as On-Demand Instances with Capacity Reservations. Run the remaining instances as Spot Instances. C. Split the 12 instances across three Availability Zones in the chosen AWS Region. Run two instances in each Availability Zone as On-Demand Instances with a Savings Plan. Run two instances in each Availability Zone as Spot Instances. D. Split the 12 instances across three Availability Zones in the chosen AWS Region. Run three instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run one instance in each Availability Zone as a Spot Instance.