Amazon Web Services Updated SOA-C03 Exam Questions and Answers by sidra

CloudFront caches content at edge locations to reduce origin requests and improve performance. When a static website is updated by overwriting existing object keys (for example, updating index.html in the same S3 path), CloudFront may continue to serve the cached versions of those objects until the cache’s time-to-live (TTL) expires. This behavior commonly produces a delay where some viewers still receive the old site version, sometimes for many hours, depending on the configured TTLs and any cache headers that CloudFront is honoring.

A CloudFront invalidation (Option D) is designed to address exactly this situation: it removes cached objects from CloudFront edge caches so that the next request for those objects forces CloudFront to retrieve the latest versions from the S3 origin and cache them again. By invalidating key objects such as /index.html (and potentially additional paths if needed), the company can ensure users see the newest deployment quickly without waiting for TTL expiration.

Option A is not the best answer because “adding a Cache-Control header to requests” does not immediately clear content already cached at edge locations; it primarily influences caching behavior for future fetches and can still leave existing cached objects in place until they expire. Option B only enforces HTTPS and does not affect cache freshness or object replacement visibility. Option C (CachingOptimized) is intended to improve caching efficiency and performance for typical workloads; it does not provide immediate propagation of updated objects and can result in CloudFront serving cached responses until TTLs expire.

Therefore, the most direct solution to display the updated website as quickly as possible is to create a CloudFront invalidation.

The AWS Cloud Operations and Governance documentation specifies that AWS Config can be paired with AWS Systems Manager Automation runbooks for automatic remediation of noncompliant resources.

For SSH restrictions, the restricted-ssh managed rule detects any security group allowing inbound traffic on port 22. To automatically remediate these findings, AWS provides the AWS-DisableIncomingSSHOnPort22 runbook. This runbook programmatically removes inbound rules that allow port 22 traffic from affected security groups.

This approach achieves continuous compliance with minimal human intervention. By contrast, sending notifications (Option A) does not enforce remediation, API-based scripts (Option C) add operational overhead, and manual remediation (Option D) violates automation best practices.

Therefore, the most efficient CloudOps solution is Option B, using AWS Config with the AWS-DisableIncomingSSHOnPort22 automation runbook for automatic, scalable enforcement.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

The correct solution is B. Configure RDS Proxy, because RDS Proxy is specifically designed to manage and pool database connections for Amazon Aurora and Amazon RDS. AWS CloudOps documentation states that RDS Proxy reduces database load and prevents connection exhaustion by reusing existing connections and managing spikes in application demand.

In this scenario, the ecommerce application maintains many idle connections, which consume database connection slots even when not actively used. During peak traffic, new connections cannot be established, resulting in the “Too many connections” error. RDS Proxy sits between the application and the Aurora DB cluster, maintaining a smaller, efficient pool of database connections and multiplexing application requests over those connections.

Option A is incorrect because RCUs and WCUs apply to DynamoDB, not Aurora. Option C is incorrect because enhanced networking improves network throughput and latency but does not manage database connections. Option D is incorrect because changing instance types does not address idle connection buildup and can still result in connection exhaustion.

AWS CloudOps best practices recommend RDS Proxy for applications with connection-heavy workloads, unpredictable traffic patterns, or serverless components.

High availability for an EC2-based web application behind an ALB is primarily achieved by eliminating single points of failure at the infrastructure layer. A single Availability Zone (AZ) deployment is vulnerable to AZ-level impairment (power/network issues, facility events, or service disruptions). The most direct and standard AWS approach is to distribute the Auto Scaling group across multiple AZs within the same Region.

Updating the Auto Scaling group to use subnets in at least two AZs allows instances to be launched across AZs automatically. The ALB is built to route traffic across targets in multiple AZs, and Auto Scaling can replace unhealthy instances in any enabled AZ. This provides resilience without the complexity of multi-Region architectures.

Options A and B address capacity for peak load but do not address the core availability requirement. Even with more instances, a full AZ outage would still take the entire application down if all instances are in the same AZ.

Option D (multi-Region) can improve resilience further, but it introduces significantly more operational overhead: cross-Region traffic routing, data replication, DNS failover strategy, application state handling, and potentially active-active or active-passive designs. For “make the application highly available” from a single-AZ baseline, multi-AZ in the same Region is the standard, least-overhead improvement.