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The root cause of the problem is that the RDS PostgreSQL instance is exceeding its database connection limit due to high volumes of concurrent GET requests. The most efficient and lowest-effort remediation is to reduce the number of connections that reach the database and serve as many reads as possible from a cache layer. Option B accomplishes both goals with minimal changes to the existing architecture.

First, RDS Proxy manages database connections efficiently by pooling and reusing them. This reduces connection churn and prevents the Lambda function from opening excessive concurrent connections during traffic spikes. RDS Proxy is natively integrated with RDS and requires only minor configuration changes without altering application logic.

Second, enabling API Gateway caching allows caching GET responses directly at the API layer. Because over 90% of requests are GET operations, enabling caching drastically reduces traffic hitting both Lambda and RDS, significantly improving performance and lowering connection pressure.

Option C also improves read scaling, but adding ElastiCache introduces additional infrastructure and requires modifying application code to query Redis before querying the database. This is more complex than enabling API Gateway caching. Options A and D require full database migration, which is far more labor-intensive.

Option B is the simplest, most effective, and most aligned with AWS best practices for reducing RDS connection saturation.

Amazon GuardDuty provides built-in, fully managed threat detection for AWS accounts and workloads, including automatic detection of cryptocurrency mining activity, compromised EC2 instances, command-and-control traffic patterns, and anomalous behaviors. GuardDuty includes multiple threat identifiers such as EC2:CryptoCurrencyActivity.BitcoinToolDetected and EC2:UnauthorizedAccess.CryptoMining. Because this capability is native and requires no custom detection logic, it provides the lowest development and operational overhead compared to alternatives.

When GuardDuty generates a finding, it publishes the event automatically to Amazon EventBridge. The DevOps engineer can create an EventBridge rule that matches the specific mining-related finding types. The target of the rule is an AWS Lambda function that parses instance details from the finding and calls TerminateInstances on the affected EC2 instance.

This enables real-time automated remediation without needing recurring scans, log parsing, or complex data queries.

Options A and B require building custom detection pipelines and maintaining periodic Lambda polling, increasing long-term complexity. Option D (Security Hub) aggregates findings but still relies on GuardDuty for detection, adding unnecessary overhead.

Therefore, GuardDuty + EventBridge + Lambda termination is the easiest, quickest, and most AWS-recommended solution following incident-response best practices.

" The firewall appliance sends logs to Amazon CloudWatch Logs and includes event severities of CRITICAL, HIGH, MEDIUM, LOW, and INFO "

Amazon FSx for NetApp ONTAP provides NetApp ONTAP features in AWS, including SnapMirror replication and storage efficiencies like deduplication and compression. Create FSx for ONTAP in each Region and use SnapMirror from on-prem ONTAP to each Region for efficient, incremental replication. Regions can serve data independently of on-prem availability once replicated.

Amazon S3 can generate server access logs that record detailed information about each request, including requester, bucket, key, operation, time, and status. These logs are written as objects to an S3 bucket. To analyze access patterns, the simplest and most serverless approach is to use Amazon Athena directly on those logs without building ingestion pipelines or databases.

Option B enables S3 server access logging and then creates an Athena external table over the log bucket. AWS provides standard log formats and even example schemas for S3 access logs. The analytics team can run ad hoc SQL queries to count the number of accesses per object per time period, filter by user, and perform aggregations, all without provisioning compute or managing databases.

Option A requires ingesting logs into Aurora, which adds ETL complexity and ongoing database management. Option C requires a Lambda function for every access event plus DB writes, which is more complex and potentially expensive at scale. Option D uses CloudWatch Logs and Managed Flink, which is more suited for streaming analytics and is significantly more complex than necessary for monthly summary reports.

Therefore, Option B provides the required analysis with the least development and operational effort.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The correct answer is D because AWS Secrets Manager supports automatic secret rotation and provides built-in rotation templates for supported services, including Amazon ElastiCache for Redis OSS. This gives the least operational overhead for rotating the secret every 60 days.

The requirement also states that employees and applications must access secrets and parameters through AWS Systems Manager Parameter Store. Parameter Store can reference Secrets Manager secrets by using a reserved path, which allows the application to retrieve the secret through Parameter Store while the secret is actually stored and rotated in Secrets Manager.

Why the other options are incorrect:

A. This option correctly uses Secrets Manager rotation, but it does not specifically use the reserved Parameter Store reference path approach that satisfies the access requirement through Parameter Store as clearly as option D.

B. Parameter Store does not provide native automatic rotation for secrets in the same way that Secrets Manager does.

C. This option is possible, but it requires custom Lambda and EventBridge automation, which creates more operational overhead than using native Secrets Manager rotation with the provided template.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#LambdaFunctionExample

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

The correct answer is C.

Option A is incorrect because creating a new SCP that has two statements, one that allows access to the new range of IP addresses for all the S3 buckets and one that denies access to the old range of IP addresses for all the S3 buckets, is not a valid solution. SCPs are not resource-based policies, and they cannot specify the S3 buckets or the IP addresses as resources or conditions. SCPs can only control the actions that can be performed by the principals in the organization, not the access to specific resources. Moreover, setting a permissions boundary for the OrganizationAccountAccessRole role in the two OUs to deny access to the S3 buckets is not sufficient to revoke the permissions of the two OUs, as there might be other roles or users in those OUs that can still access the S3 buckets.

Option B is incorrect because creating a new SCP that has a statement that allows only the new range of IP addresses to access the S3 buckets is not a valid solution, for the same reason as option A. SCPs are not resource-based policies, and they cannot specify the S3 buckets or the IP addresses as resources or conditions. Creating another SCP that denies access to the S3 buckets and attaching it to the two OUs is also not a valid solution, as SCPs cannot specify the S3 buckets as resources either.

Option C is correct because it meets both requirements of changing the range of IP addresses that have permission to access the contents of the S3 buckets and revoking the permissions of two OUs in the company. On all the S3 buckets, configuring resource-based policies that allow only the new range of IP addresses to access the S3 buckets is a valid way to update the IP address ranges, as resource-based policies can specify both resources and conditions. Creating a new SCP that denies access to the S3 buckets and attaching it to the two OUs is also a valid way to revoke the permissions of those OUs, as SCPs can deny actions such as s3:PutObject or s3:GetObject on any resource.

Option D is incorrect because setting a permissions boundary for the OrganizationAccountAccessRole role in the two OUs to deny access to the S3 buckets is not sufficient to revoke the permissions of the two OUs, as there might be other roles or users in those OUs that can still access the S3 buckets. A permissions boundary is a policy that defines the maximum permissions that an IAM entity can have. However, it does not revoke any existing permissions that are granted by other policies.

AWS X-Ray provides distributed tracing, which allows visualization of latencies and errors within microservices, pinpointing bottlenecks or delays.

Instrumenting application code with the X-Ray SDK and running the X-Ray daemon as a DaemonSet in EKS ensures tracing data is collected cluster-wide.

Container Insights (Option B) provides resource-level metrics but not detailed request tracing.

CloudWatch alarms and alerts (Option C) detect symptoms but don ' t provide root cause tracing.

Increasing timeouts (Option D) only masks the issue and does not diagnose it.

https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-groups.html