Amazon Web Services SCS-C03 Exam With Confidence Using Practice Dumps

To enforce encryption in transit to Aurora, you must require clients to useTLS/SSLwhen connecting to the database. Aurora (depending on engine flavor) supports a parameter that enforces secure transport by rejecting non-TLS connections. Setting the DB (cluster/instance) parameter such asrequire_secure_transporttoONforces clients to negotiate SSL/TLS; otherwise the server refuses the connection, ensuring all data in transit is encrypted. This directly enforces the compliance requirement at the database endpoint itself, independent of client behavior.

Kerberos authentication (Option B) provides centralized authentication and can be useful for IAM/AD integration, but it does not by itself guarantee that the network session is encrypted. Option C is incorrect because Aurora/RDS usesRDS-provided certificatesfor TLS; you don’t attach ACM certificates to Aurora the way you would for ALB/CloudFront. Option D is not the right enforcement mechanism: RDS Proxy can help with connection pooling and IAM auth patterns, but it does not inherently force all client-to-proxy or proxy-to-db connections to be encrypted in the way a DB parameter enforcement does (and you’d still need to ensure clients use TLS). Therefore, enabling the parameter that requires secure transport is the correct solution.

AWS CloudTrail provides a record of all API calls made in an AWS account, including calls initiated by AWS CloudFormation. According to the AWS Certified Security – Specialty Study Guide, CloudTrail is the primary source for troubleshooting authorization failures because it records denied actions and the policy type that caused the denial, including service control policies.

Reviewing CloudTrail logs allows a security engineer to identify which specific API calls failed during the CloudFormation deployment and whether the denial was caused by an SCP, an IAM policy, or a permission boundary. This evidence-based approach is the recommended first step before making any configuration changes.

Option B is unsafe and violates governance best practices by removing SCPs in production. Option C may be necessary later, but it does not identify whether SCPs are the root cause. Option D introduces unnecessary risk and bypasses the purpose of differentiated controls across OUs.

AWS documentation emphasizes observing and validating before modifying security controls, making CloudTrail log analysis the correct initial troubleshooting step.

Referenced AWS Specialty Documents:

AWS Certified Security – Specialty Official Study Guide

AWS Organizations Service Control Policies

AWS CloudTrail Authorization Failure Analysis

To analyze API access patterns with minimal effort andwithout parsing raw log files, the best approach is to rely onmetricsand built-in query tooling. EnablingDetailed CloudWatch Metricsfor an API Gateway stage (Option E) provides near-real-time, aggregated visibility into usage and performance patterns (such as request counts, latency, error rates like 4XX/5XX) that are ideal for identifying trends and spikes without handling logs.

For deeper pattern exploration when needed,CloudWatch Logs Insights(Option D) provides an interactive query experience over logs that are already in CloudWatch Logs, allowing quick filtering and aggregation. In practice, developers use metrics to understand access patterns at a high level and Logs Insights to slice and dice request data without building a separate parsing pipeline.

Options A and C still rely on enabling access logs and shipping them to S3/Athena, which is more setup and operational overhead (and still involves managing log storage/format). CloudTrail (Option B) records control-plane API calls to AWS services, not end-user access requests to your API methods, so it won’t provide the desired access pattern view for API consumers. Therefore, Detailed CloudWatch Metrics plus CloudWatch Logs Insights is the least-effort combination for access pattern analysis.