What should the database specialist do to resolve the performance issues?
In AWS SCT, turn on the balance speed with memory consumption performance option with the optimal memory settings on local desktop.
Provision the target Aurora PostgreSQL database with a higher instance class. In AWS SCT. turn on the balance speed with memory consumption performance option.
In AWS SCT, turn on the fast conversion with large memory consumption performance option and set the JavaOptions section to the maximum memory available.
Provision a client Amazon EC2 machine with more CPU and memory resources in the same AWS Region as the Aurora PostgreSQL database.
Explanations:
While turning on the balance speed with memory consumption can help, it may not be sufficient to resolve performance issues if the local desktop lacks adequate resources for processing such a large schema. Therefore, this option is not the most effective solution.
Provisioning a higher instance class for the Aurora PostgreSQL database could enhance performance for database operations, but it does not address the conversion performance in AWS SCT, which is primarily dependent on the client machine where SCT is running. Thus, this option does not fully resolve the issue.
Turning on the fast conversion with large memory consumption can optimize the AWS SCT performance by allowing it to utilize more resources for the conversion process. Setting the JavaOptions to maximum memory ensures that SCT can leverage available system resources effectively, leading to improved conversion speeds.
While provisioning a more powerful EC2 instance could help improve SCT’s performance, it does not directly enhance the conversion process unless it is explicitly used to run the SCT. The conversion process can still be bottlenecked by other factors, making this option less optimal than option C.
I infer that the answer is:
In AWS SCT, turn on the fast conversion with large memory consumption performance option and set the JavaOptions section to the maximum memory available.