SOA-C03 Practice Exam Questions with Answers AWS Certified CloudOps Engineer - Associate Certification

The AWS Cloud Operations and Security documentation confirms that AWS WAF (Web Application Firewall) is designed to protect web applications from application-layer threats, including SQL injection, cross-site scripting (XSS), and other OWASP Top 10 vulnerabilities.

When integrated with an Application Load Balancer, AWS WAF inspects incoming traffic using rule groups. The AWS Managed Rules for SQL Injection Protection provide preconfigured, continuously updated filters that detect and block malicious SQL patterns.

AWS Shield (Standard or Advanced) defends against DDoS attacks, not application-layer SQL attacks, and vulnerability scanners (Option C) only detect, not prevent, exploitation.

Thus, Option D provides the correct, managed, and automated protection aligned with AWS best practices.

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.

The AWS Cloud Operations and Security documentation specifies that for Amazon CloudFront, automatic certificate renewal is only supported for certificates issued by AWS Certificate Manager (ACM). When a certificate is managed by ACM and validated through DNS validation, ACM automatically renews the certificate before expiration without requiring manual intervention.

Option A ensures that the certificate is issued and managed by ACM, enabling full integration with CloudFront. Option E (DNS validation) is essential for automation; AWS performs revalidation automatically as long as the DNS validation record remains in place.

By contrast, email validation (Option D) requires manual user confirmation upon renewal, which prevents automatic renewals. Certificates issued by third-party certificate authorities (Option B) are manually managed and must be reimported into ACM after renewal. CloudFront does not have a direct feature (Option C) to renew certificates; it relies on ACM’s lifecycle management.

Thus, combining ACM-issued certificates (A) with DNS validation (E) ensures continuous, automated renewal with no downtime or human action required.

The combination of geoproximity routing and DNS failover health checks provides global low-latency routing with high availability.

Geoproximity routing in Route 53 routes users to the AWS Region closest to their geographic location, optimizing latency. For automatic failover, Route 53 health checks can monitor CloudWatch alarms tied to the health of the ALB in each Region. When a Region becomes unhealthy, Route 53 reroutes traffic to the next available Region automatically.

AWS documentation states:

“Use geoproximity routing to direct users to resources based on geographic location, and configure health checks to provide DNS failover for high availability.”

Option B incorrectly monitors EC2 instances directly, which is not efficient at scale. Option C uses private IPs, which cannot be globally health-checked. Option E (simple routing) does not support geographic or failover routing. Hence, A and D together meet both the proximity and failover requirements.

References (AWS CloudOps Documents / Study Guide):

• AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide – Domain 5: Networking and Content Delivery

• Amazon Route 53 Developer Guide – Geoproximity Routing and DNS Failover

• AWS Well-Architected Framework – Reliability Pillar

• Amazon CloudWatch Alarms – Integration with Route 53 Health Checks

The AWS Cloud Operations and Monitoring documentation states that Amazon CloudWatch Logs Insights provides a purpose-built query engine for analyzing and visualizing log data directly within CloudWatch. For Lambda, all invocation results (including errors) are automatically logged to CloudWatch Logs.

By querying these logs with CloudWatch Logs Insights, the CloudOps engineer can efficiently count the number of “ERROR” or “Exception” occurrences over the past 7 days using simple SQL-like commands. This method is serverless, cost-efficient, and real-time.

Athena (Options A and B) would require exporting data to Amazon S3, and OpenSearch (Option D) adds unnecessary operational complexity.

Thus, Option C provides the most efficient and native AWS CloudOps approach for rapid Lambda error analysis.

The AWS Cloud Operations and Content Delivery documentation explains that Amazon CloudFront caches objects in edge locations for a defined time based on TTL settings or origin headers. When new content is deployed to the S3 origin, previously cached versions remain in edge caches until they expire.

To immediately serve the new version, CloudOps engineers must initiate a CloudFront invalidation, which removes cached objects from all edge locations. This forces CloudFront to fetch the latest version from the origin (S3).

Invalidations can target individual objects (e.g., /index.html) or wildcard paths (e.g., /*) and are the AWS-recommended approach for dynamic content refresh after static site updates.

Changing headers (Option A), enforcing HTTPS (Option B), or applying caching policies (Option C) do not directly refresh outdated cache content.

Thus, Option D — issuing a CloudFront invalidation — ensures users receive the latest website content immediately after deployment.

According to the AWS Cloud Operations and ElastiCache documentation, cache evictions occur when the cache runs out of memory and must remove items to make space for new data.

To reduce evictions and retain frequently accessed items, AWS recommends increasing the total available memory — either by scaling up to larger node types or scaling out by adding shards/nodes. Migrating to a cluster with larger nodes is the simplest and most efficient solution because it immediately expands capacity without architectural changes.

Adjusting TTL (Options B and C) controls expiration timing, not memory allocation. Adding a single node (Option A) may help, but redistributing data requires resharding, introducing more complexity.

Thus, Option D provides the lowest operational overhead and ensures high cache hit rates by increasing total cache memory.

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.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:

EC2 Instance Connect Endpoint (EIC Endpoint) enables SSH to instances in private subnets without public IPs and without needing to traverse the public internet. CloudOps guidance explains that you deploy the endpoint in the same VPC/subnet as the targets, then allow inbound SSH on the instance security group from the endpoint’s security group. Access is governed by IAM—administrators must have Instance Connect permissions; while the example uses a broad policy, the key mechanism is EIC in the private subnet plus SG rules scoped to the endpoint. Systems Manager Session Manager can provide shell access without SSH, but the requirement explicitly states “connect through SSH,” making EIC the purpose-built solution. Options B and D misuse Systems Manager for SSH and propose unnecessary SG changes or incorrect endpoint placement; Option C places the endpoint in a public subnet, which is not required for private SSH access. Therefore, creating an EC2 Instance Connect endpoint in the private subnet and updating SGs accordingly meets the requirement while keeping the instance non-internet-exposed.

References (AWS CloudOps Documents / Study Guide):

• AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide – Security and Compliance

• Amazon EC2 – Instance Connect Endpoint (Private SSH Access)

• AWS Well-Architected Framework – Security Pillar (Least Privilege Network Access)

Per the AWS Cloud Operations and Data Protection documentation, S3 Object Lock enforces write-once-read-many (WORM) protection on objects for a defined retention period.

There are two modes:

Compliance mode: Even the root user cannot delete or modify objects during the retention period.

Governance mode: Privileged users with special permissions can override lock settings.

For regulatory or audit requirements that prohibit deletion, Compliance mode is the correct choice. When configured with a 3-month retention period, all backup objects are protected from deletion until expiration, ensuring compliance with data retention mandates.

Versioning (Option C) alone does not prevent deletion. IAM-based restrictions (Option A) lack time-based enforcement and require manual intervention. Governance mode (Option D) is less strict and unsuitable for regulatory retention.

Thus, Option B is the correct CloudOps solution for immutable S3 backups.

According to the AWS Cloud Operations, Governance, and Compliance documentation, AWS Config provides managed rules that automatically evaluate resource configurations for compliance. The “required-tags” managed rule allows CloudOps teams to specify mandatory tags (e.g., Environment, Owner, CostCenter) and automatically detect non-compliant resources such as DynamoDB tables.

Furthermore, AWS Config supports automatic remediation through AWS Systems Manager Automation runbooks, enabling correction actions (for example, adding missing tags) without manual intervention. This automation minimizes operational overhead and ensures continuous compliance across multiple accounts.

Using a custom Lambda function (Options A or B) introduces unnecessary management complexity, while EventBridge rules alone (Option D) do not provide resource compliance tracking or historical visibility.

Therefore, Option C provides the most efficient, fully managed, and compliant CloudOps solution.

AWS CloudOps governance best practices emphasize centralized account management and preventive guardrails. AWS Control Tower integrates directly with AWS Organizations and provides “Region deny controls” and “Service Control Policies (SCPs)” that apply automatically to all existing and newly created member accounts. SCPs are organization-wide guardrails that define the maximum permissions for accounts. They can explicitly deny actions such as launching EC2 instances in a specific Region, or block root user access.

To prevent CloudTrail log deletion, SCPs can also include denies on cloudtrail:DeleteTrail and s3:DeleteObject actions targeting the CloudTrail log S3 bucket. These SCPs ensure that no user, including administrators, can violate the compliance requirements.

AWS documentation under the Security and Compliance domain for CloudOps states:

“Use AWS Control Tower to establish a secure, compliant, multi-account environment with preventive guardrails through service control policies and detective controls through AWS Config.”

This approach meets all stated needs: centralized enforcement, automatic propagation to new accounts, region-based restrictions, and immutable audit logs. Options A, B, and D either detect violations reactively or lack complete enforcement and automation across future accounts.

References (AWS CloudOps Documents / Study Guide):

• AWS Certified CloudOps Engineer – Associate (SOA-C03) Exam Guide – Domain 4: Security and Compliance

• AWS Control Tower – Preventive and Detective Guardrails

• AWS Organizations – Service Control Policies (SCPs)

• AWS Well-Architected Framework – Security Pillar (Governance and Centralized Controls)

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:

With simple scaling policies, an Auto Scaling group performs one scaling activity when the alarm condition is met, then observes a default cooldown period (300 seconds) before another scaling activity of the same type can begin. CloudOps guidance explains that cooldown prevents rapid successive scale-outs by allowing time for the newly launched instance(s) to register with the load balancer and impact the metric. Even if the alarm breaches multiple times during the cooldown window, the group waits until the cooldown completes before evaluating and acting again. In this case, although RequestCountPerTarget exceeded the threshold twice within 180 seconds, the group will launch a single instance and then wait for cooldown before any additional scale-out can occur. Options A, C, and D do not reflect the behavior of simple scaling with cooldowns; A describes step/target-tracking-like behavior, and C/D are not Auto Scaling mechanics.

References (AWS CloudOps Documents / Study Guide):

• Amazon EC2 Auto Scaling – Simple Scaling Policies and Cooldown (User Guide)

• Elastic Load Balancing Metrics – ALB RequestCountPerTarget (CloudWatch Metrics)

• AWS Well-Architected Framework – Performance Efficiency & Operational Excellence

According to the AWS Cloud Operations and Auto Scaling documentation, scaling applications that consume Amazon SQS messages should be driven by queue backlog per instance, not by general system metrics such as network traffic or CPU.

The correct approach is to calculate a custom metric using CloudWatch metric math that divides the SQS metric ApproximateNumberOfMessagesVisible by the number of active EC2 instances in the Auto Scaling group. This “backlog per instance” value represents the average number of messages waiting to be processed by each instance.

Then, the CloudOps engineer can create a target tracking policy that automatically scales out or in based on maintaining a desired backlog threshold. This approach ensures dynamic, workload-driven scaling behavior that reacts in near real time to message volume.

Step and simple scaling (Options C and D) require manual thresholds and do not automatically balance the load per instance.

Thus, Option B—using CloudWatch metric math to define queue backlog per instance for target tracking—is the most effective and AWS-recommended CloudOps practice.