Demo practice questions for guest users.
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 company hosts a critical legacy application on two Amazon EC2 instances that are in one Availability Zone. The instances run behind an Application Load Balancer (ALB). The company uses Amazon CloudWatch alarms to send Amazon Simple Notification Service (Amazon SNS) notifications when the ALB health checks detect an unhealthy instance. After a notification, the company's engineers manually restart the unhealthy instance. A CloudOps engineer must configure the application to be highly available and more resilient to failures. Which solution will meet these requirements?
High availability requires removing single-AZ risk and eliminating manual recovery. The AWS Reliability best practices state to design for multi-AZ and automatic healing: Auto Scaling “helps maintain application availability and allows you to automatically add or remove EC2 instances” (AWS Auto Scaling User Guide). The Reliability Pillar recommends to “distribute workloads across multiple Availability Zones” and to “automate recovery from failure” (AWS Well-Architected Framework Reliability Pillar). Attaching the Auto Scaling group to an ALB target group enables health-based replacement: instances failing load balancer health checks are replaced and traffic is routed only to healthy targets. Using an AMI in a launch template ensures consistent, repeatable instance configuration (AWS EC2 Launch Templates). Options A and C keep all instances in a single Availability Zone and rely on manual or ad-hoc restarts, which do not meet high-availability or resiliency goals. Option B only scales vertically and adds a restart rule; it neither removes the single-AZ failure domain nor provides automated replacement. Therefore, creating a multi-AZ EC2 Auto Scaling group with a launch template and attaching it to the ALB target group (Option D) is the CloudOps-aligned solution for resilience and business continuity.
References (AWS CloudOps Documents / Study Guide):
• AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide: Domain 2 – Reliability and Business Continuity
• AWS Well-Architected Framework – Reliability Pillar • Amazon EC2 Auto Scaling User Guide – Health checks and replacement
• Elastic Load Balancing User Guide – Target group health checks and ALB integration
• Amazon EC2 Launch Templates – Reproducible instance configuration
An Amazon EC2 instance is running an application that uses Amazon Simple Queue Service (Amazon SQS) queues. A CloudOps engineer must ensure that the application can read, write, and delete messages from the SQS queues.
Which solution will meet these requirements in the MOST secure manner?
The most secure pattern is to use an IAM role for Amazon EC2 with the minimum required permissions. AWS guidance states: “Use roles for applications that run on Amazon EC2 instances” and “grant least privilege by allowing only the actions required to perform a task.” By attaching a role to the instance, short-lived credentials are automatically provided through the instance metadata service; this removes the need to create long-term access keys or embed secrets. Granting only sqs:SendMessage, sqs:ReceiveMessage, and sqs:DeleteMessage against the specific SQS queues enforces least privilege and aligns with CloudOps security controls. Options A and B rely on IAM user access keys, which contravene best practices for workloads on EC2 and increase credential management risk. Option C uses a role but grants sqs:*, violating least-privilege principles. Therefore, Option D meets the security requirement with scoped, temporary credentials and precise pe rm i ssi ons.
References (AWS CloudOps Documents / Study Guide):
• AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide – Security & Compliance
• IAM Best Practices – “Use roles instead of long-term access keys,” “Grant least privilege”
• IAM Roles for Amazon EC2 – Temporary credentials for applications on EC2
• Amazon SQS – Identity and access management for Amazon SQS
A company runs an application that logs user data to an Amazon CloudWatch Logs log group. The company discovers that personal information the application has logged is visible in plain text in the CloudWatch logs.
The company needs a solution to redact personal information in the logs by default. Unredacted information must be available only to the company's security team. Which solution will meet these requirements?
CloudWatch Logs data protection provides native redaction/masking of sensitive data at ingestion and query. AWS documentation states it can “detect and protect sensitive data in logs” using data identifiers, and that authorized users can “use the unmask action to view the original data.” Creating a data protection policy on the log group masks PII by default for all viewers, satisfying the requirement to redact personal information. Granting only the security team permission to invoke the unmask API operation ensures that unredacted content is restricted. Option B (KMS) encrypts at rest but does not redact fields; encryption alone does not prevent plaintext visibility to authorized readers. Options A and D add complexity and latency, move data out of CloudWatch, and do not provide default inline redaction/unmask controls in CloudWatch itself. Therefore, the CloudOps aligned, managed solution is to use CloudWatch Logs data protection with appropriate data identifiers and unmask permissions limited to the security team.
References (AWS CloudOps Documents / Study Guide):
• AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide – Monitoring & Logging
• Amazon CloudWatch Logs – Data Protection (masking/redaction with data identifiers)
• CloudWatch Logs – Permissions for masking and unmasking sensitive data
• AWS Well-Architected Framework – Security and Operational Excellence (sensitive data handling)