A company runs applications on Amazon EC2 instances. The company wants to ensure that SSH ports on the EC2 instances are never open. The company has enabled AWS Config and has set up the restricted-ssh AWS managed rule.
A CloudOps engineer must implement a solution to remediate SSH port access for noncompliant security groups.
What should the engineer do to meet this requirement with the MOST operational efficiency?
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.
A company is running an ecommerce application on AWS. The application maintains many open but idle connections to an Amazon Aurora DB cluster. During times of peak usage, the database produces the following error message: "Too many connections." The database clients are also experiencing errors.
Which solution will resolve these errors?
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.
Amazon RDS User Guide -- RDS Proxy concepts and benefits
Amazon Aurora User Guide -- Managing database connections
AWS SysOps Administrator Study Guide -- Database reliability and scaling
A company's ecommerce application is running on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The instances are in an Auto Scaling group. Customers report that the website is occasionally down. When the website is down, it returns an HTTP 500 (server error) status code to customer browsers.
The Auto Scaling group's health check is configured for EC2 status checks, and the instances appear healthy.
Which solution will resolve the problem?
In this scenario, the EC2 instances pass their EC2 status checks, indicating that the operating system is responsive. However, the application hosted on the instance is failing intermittently, returning HTTP 500 errors. This demonstrates a discrepancy between the instance-level health and the application-level health.
According to AWS CloudOps best practices under Monitoring, Logging, Analysis, Remediation and Performance Optimization (SOA-C03 Domain 1), Auto Scaling groups should incorporate Elastic Load Balancing (ELB) health checks instead of relying solely on EC2 status checks. The ELB health check probes the application endpoint (for example, HTTP or HTTPS target group health checks), ensuring that the application itself is functioning correctly.
When an instance fails an ELB health check, Amazon EC2 Auto Scaling will automatically mark the instance as unhealthy and replace it with a new one, ensuring continuous availability and performance optimization.
Extract from AWS CloudOps (SOA-C03) Study Guide -- Domain 1:
''Implement monitoring and health checks using ALB and EC2 Auto Scaling integration. Application Load Balancer health checks allow Auto Scaling to terminate and replace instances that fail application-level health checks, ensuring consistent application performance.''
Extract from AWS Auto Scaling Documentation:
''When you enable the ELB health check type for your Auto Scaling group, Amazon EC2 Auto Scaling considers both EC2 status checks and Elastic Load Balancing health checks to determine instance health. If an instance fails the ELB health check, it is automatically replaced.''
Therefore, the correct answer is B, as it ensures proper application-level monitoring and remediation using ALB-integrated ELB health checks---a core CloudOps operational practice for proactive incident response and availability assurance.
References (AWS CloudOps Verified Source Extracts):
AWS Certified CloudOps Engineer -- Associate (SOA-C03) Exam Guide: Domain 1 -- Monitoring, Logging, and Remediation.
AWS Auto Scaling User Guide: Health checks for Auto Scaling instances (Elastic Load Balancing integration).
AWS Well-Architected Framework -- Operational Excellence and Reliability Pillars.
AWS Elastic Load Balancing Developer Guide -- Target group health checks and monitoring.
A CloudOps engineer needs to track the costs of data transfer between AWS Regions. The CloudOps engineer must implement a solution to send alerts to an email distribution list when transfer costs reach 75% of a specific threshold.
What should the CloudOps engineer do to meet these requirements?
According to the AWS Cloud Operations and Cost Management documentation, AWS Budgets is the recommended service to track and alert on cost thresholds across all AWS accounts and resources. It allows users to define cost, usage, or reservation budgets, and configure notifications to trigger when usage or cost reaches defined percentages of the budgeted value (e.g., 75%, 90%, 100%).
The AWS Budgets system integrates natively with Amazon Simple Notification Service (SNS) to deliver alerts to an email distribution list or SNS topic subscribers. AWS Budgets supports granular cost filters, including specific service categories such as data transfer, regions, or linked accounts, ensuring precise visibility into inter-Region transfer costs.
By contrast, CloudWatch billing alarms (Option B) monitor total account charges only and do not allow detailed service-level filtering, such as data transfer between Regions. Cost and Usage Reports (Option A) are for detailed cost analysis, not real-time alerting, and VPC Flow Logs (Option D) capture traffic data, not billing or cost-based metrics.
Thus, using AWS Budgets with a 75% alert threshold best satisfies the operational and notification requirements.
A company runs an application on Amazon EC2 instances behind an Elastic Load Balancer (ELB) in an Auto Scaling group. The application performs well except during a 2-hour period of daily peak traffic, when performance slows.
A CloudOps engineer must resolve this issue with minimal operational effort.
What should the engineer do?
According to the AWS Cloud Operations and Compute documentation, when workloads exhibit predictable traffic patterns, the best practice is to use scheduled scaling for Amazon EC2 Auto Scaling groups.
With scheduled scaling, administrators can predefine the desired capacity of an Auto Scaling group to increase before anticipated demand (in this case, before the 2-hour peak) and scale back down afterward. This ensures that sufficient compute capacity is provisioned proactively, avoiding performance degradation while maintaining cost efficiency.
AWS notes: ''Scheduled actions enable scaling your Auto Scaling group at predictable times, allowing you to pre-warm instances before demand spikes.''
Manual scaling (Option D) adds operational overhead. Adjusting launch templates (Option B) doesn't affect scaling behavior, and permanently increasing minimum capacity (Option A) wastes resources outside of peak hours.
Thus, Option C provides an automated, cost-effective, and operationally efficient CloudOps solution.
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