An enterprise financial reporting application runs on AWS. The application tier consists of a fleet of Amazon EC2 `c5.xlarge` instances in an Auto Scaling Group behind an Application Load Balancer (ALB). The application queries an Amazon RDS for PostgreSQL database (configured in a Multi-AZ deployment) to retrieve transaction logs, processes the data, and writes intermediate files to attached `gp3` EBS volumes before uploading the consolidated reports to Amazon S3.
During end-of-month processing, users experience severe delays. A solutions architect reviews the Amazon CloudWatch metrics and notes the following:
* The EC2 instances show low CPU and memory utilization, but high I/O wait times.
* The `gp3` EBS volumes show `VolumeThroughput` consistently pegged at and high `VolumeQueueLength`.
* The RDS database experiences high CPU utilization and read latency on the primary instance, while the standby instance CPU utilization remains near .
Which two actions should the solutions architect take to resolve the performance bottlenecks and optimize the system's performance? (Select TWO.)
- Modify the gp3 EBS volume configuration to increase the provisioned throughput beyond the default to match the peak transfer rate of the application.Cevap
- Create an Amazon RDS PostgreSQL read replica and modify the reporting application to route read-only query traffic to the read replica's endpoint.Cevap
- CModify the database connection string in the application to direct read-only query traffic to the RDS Multi-AZ standby instance's DNS endpoint.
- DConfigure the Auto Scaling Group to use Application Load Balancer target tracking scaling policies and submit a support ticket to pre-warm the ALB prior to the end-of-month cycle.
- EPurchase EC2 Instance Savings Plans for the c5 instance family to reduce compute costs and use the savings to scale out the EC2 instances automatically during processing.