For a MySQL database experiencing high write operations, using Amazon RDS with Provisioned IOPS (io1 or io2) SSD storage is recommended to achieve consistent and low-latency performance. Provisioned IOPS allows you to specify a desired IOPS rate, which is crucial for write-intensive workloads.
Monitoring write operation metrics through Amazon CloudWatch enables you to observe performance and adjust the provisioned IOPS as needed to meet application demands.
References:
Modifying settings for Provisioned IOPS SSD storageAWS Documentation
Amazon CloudWatch metrics for Amazon RDSAWS Documentation

Spot Instances can be interrupted when AWS needs the capacity back. To reduce interruptions and improve availability, AWS provides the capacity-optimized allocation strategy.
* Capacity-optimized strategy launches Spot Instances from the most available Spot capacity pools instead of the lowest-priced ones, reducing interruption rates.
* By adding multiple instance types (e.g., using Instance Type Flexibility), the Auto Scaling group can launch instances in a broader set of pools, improving the chance that capacity is available.
* Scheduled scaling actions combined with a diverse set of instances under the capacity-optimized strategy ensure higher resilience and better timing for instance launches.
This approach directly supports the Resiliency design principle in the AWS Well-Architected Framework.
# References:
* Best Practices for EC2 Spot Instances
* Capacity-Optimized Allocation Strategy

The key requirements are shared file access, NFS protocol compatibility, and no application changes. Amazon Elastic File System (EFS) is a fully managed, scalable file system that natively supports the NFS protocol, making it an ideal drop-in replacement for on-premises shared file systems used by Linux applications.
Option C allows the existing web servers to mount the EFS file system using standard NFS mount commands, preserving application behavior and avoiding code changes. EFS is designed to be accessed concurrently by multiple EC2 instances across Availability Zones, providing high availability and elasticity without manual capacity management. This aligns well with typical web server architectures that rely on shared content or assets.
Option A and B use Amazon S3, which is an object storage service and does not support NFS semantics.
Migrating to S3 would require application changes to use object-based APIs instead of file system operations.
Option D uses Amazon FSx for Windows File Server, which supports SMB, not NFS, and is intended for Windows-based workloads.
Therefore, C is the correct solution because Amazon EFS provides NFS compatibility, shared access, high availability, and minimal operational overhead while requiring no changes to the existing Linux-based web server applications.

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.
Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.
Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.
By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.
References:
Organizing and tracking costs using AWS cost allocation tags
Activating user-defined cost allocation tags

A Network Load Balancer operates at Layer 4 (TCP/UDP/TLS) and is optimized for high performance and static IP use cases. While NLB target groups can perform health checks, they are typically oriented around basic reachability and do not provide the same application-layer (Layer 7) visibility as an Application Load Balancer (ALB). The problem statement says the NLB is "not detecting HTTP errors," which indicates the health signal needs to be based on an HTTP endpoint that can reflect application correctness (for example, returning specific HTTP status codes).
Replacing the NLB with an ALB enables true HTTP/HTTPS health checks against a URL path, including interpretation of HTTP response codes. This is the cleanest managed approach to detect application-layer failure modes that still allow TCP connections but produce bad HTTP responses. Once the ALB detects targets as unhealthy, the target group health status can be used by an Auto Scaling group to take action. With appropriate health check configuration (and, commonly, using ELB health checks as a signal), Auto Scaling can replace unhealthy instances automatically, improving availability without custom scripts.
Option A is misleading: NLB does not provide the same HTTP-aware request routing and rich L7 features; even if an NLB health check is configured, it does not address the broader need for application-layer detection and remediation as directly as ALB. Option B violates the "no custom scripts" requirement. Option D reacts to UnhealthyHostCount, but if the NLB isn't marking hosts unhealthy for HTTP error cases, the metric won't reliably trigger replacement; it also still depends on the NLB's limited visibility into HTTP failures.
Therefore, C best meets the requirement by shifting to ALB for application-layer health checks and using Auto Scaling to replace unhealthy instances automatically.