SOA-C03 Exam Dumps - AWS Certified CloudOps Engineer - Associate

The symptoms point to two common database pressure sources: inefficient queries (driving high CPU and latency) and excessive concurrent connections (driving connection management overhead, read contention, and timeouts). The most effective remediation is to address both the SQL workload and the connection surge behavior.

Amazon RDS Performance Insights (Option A) is designed to help identify which SQL statements and wait events contribute most to database load. It provides a database-centric view of performance that highlights top SQL, top waits, and the relative impact of sessions on CPU and other resources. By using Performance Insights to pinpoint the highest-impact SQL and then tuning those queries (for example, improving indexes, reducing full scans, and optimizing query patterns), the company can lower CPU utilization and reduce query latency during peak hours.

Connection surges are best addressed with a managed connection pooling layer. RDS Proxy (Option E) provides connection pooling and multiplexing between application connections and the database, which reduces the overhead of establishing and maintaining a large number of database connections. During spikes, the proxy can smooth bursty connection behavior, improve failover handling, and reduce the risk of overwhelming the database with too many concurrent connections. This directly targets the observed timeouts and read performance degradation tied to surges.

Option B is not the best fit because CloudWatch Logs Insights is a log analytics tool; it is not the primary mechanism for deep SQL performance attribution in RDS and typically requires extensive logging configurations that add overhead. Option C would reduce availability and does not improve performance. Option D would make the problem worse: disabling pooling increases connection churn and overhead, amplifying timeouts and latency during peak traffic.

Therefore, combining Performance Insights query analysis/tuning with RDS Proxy connection pooling best resolves both root causes.

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According to AWS Cloud Operations and VPC Networking documentation, when VPCs are peered, security groups can reference peer account security groups directly to restrict traffic between them.

This feature allows specifying the security group ID (sg-1234abcd) from the source account (111122223333) in the target database’s security group inbound rule. AWS automatically validates that the VPCs are connected through an existing VPC peering connection and that mutual permissions are properly configured.

You do not prefix the security group ID with the account or peering connection (Options A and B), and using the destination account ID (Option D) is incorrect because it represents the database side, not the source.

Hence, the correct configuration is Option C, which references the application servers’ security group directly for precise, least-privilege access control.

When an AWS Lambda function is configured to run inside a VPC, it loses default internet access. All outbound traffic must be explicitly routed. In this scenario, the Lambda function resides in a private subnet and successfully connects to Amazon RDS, but it times out when attempting to publish messages to Amazon SQS. This indicates a lack of network connectivity to the SQS service endpoint.

There are two valid AWS-supported ways to restore connectivity. The first is to deploy a NAT gateway in a public subnet and update the private subnet route table to send outbound internet-bound traffic (0.0.0.0/0) to the NAT gateway. This allows the Lambda function to reach public AWS service endpoints, including SQS.

The second option is to create an interface VPC endpoint (AWS PrivateLink) for Amazon SQS. This enables private, secure connectivity to SQS directly within the AWS network without traversing the internet. This approach is often preferred for security-sensitive workloads and removes dependency on NAT gateways.

Option A would break database connectivity because the Lambda function must remain in the VPC to access the private RDS instance. Option B does not address outbound connectivity to SQS. Option E is incorrect because Amazon SQS does not support gateway endpoints; only interface endpoints are supported.

Therefore, deploying a NAT gateway or creating an SQS interface endpoint resolves the timeout issue.

AWS Config is designed to continuously evaluate AWS resource configurations and detect noncompliance. The s3-bucket-logging-enabled managed rule specifically checks whether server access logging is enabled on S3 buckets. This directly meets the detection requirement for both current and future buckets.

To satisfy the remediation requirement, AWS Config supports automatic remediation actions. Using the AWS-provided AWS-ConfigureS3BucketLogging Systems Manager Automation runbook enables logging without custom code. This reduces operational overhead, avoids Lambda function maintenance, and aligns with AWS best practices.

Option A is incorrect because Trusted Advisor does not support automatic remediation. Option B cannot enforce logging at creation time through bucket policies alone. Option C works but introduces unnecessary Lambda maintenance compared to using an AWS-managed automation runbook.

Thus, combining AWS Config managed rules with Systems Manager Automation provides continuous compliance with minimal operational effort.

The requirement is to produce cost optimization recommendations for EBS volumes based on IOPS and throughput, and to do so with the most operational efficiency across potentially many instances and volumes. AWS Compute Optimizer is the most suitable service because it analyzes historical utilization metrics and configuration characteristics to generate rightsizing recommendations. When enabled, Compute Optimizer evaluates EBS volume performance patterns (including throughput and IOPS consumption) and can recommend more appropriate volume types or settings to reduce cost while meeting performance needs.

Option C is operationally efficient because it centralizes analysis. Instead of manually inspecting per-volume graphs, Compute Optimizer aggregates and evaluates data over time, reducing human effort and improving consistency. After sufficient observation time, the CloudOps engineer can review the findings and translate them into concrete actions such as reducing provisioned IOPS on io1/io2, selecting gp3 with tuned IOPS/throughput, or resizing volumes that are over-provisioned relative to actual demand.

Option A is manual and does not directly address IOPS/throughput rightsizing; it also incorrectly focuses on consumed space versus provisioned space, which is more relevant to capacity than performance provisioning. Option B is unrelated: EBS-optimized is an EC2 feature for dedicated EBS bandwidth and does not rightsize EBS volume performance or reduce over-provisioned IOPS/throughput. Option D introduces significant operational overhead by installing benchmarking tools and running synthetic tests, which is not scalable, can affect production performance, and still may not reflect real workload patterns.

Therefore, enabling AWS Compute Optimizer and using its EBS volume recommendations is the most operationally efficient approach.