Practice Free SOA-C03 AWS Certified CloudOps Engineer - Associate Exam Questions Answers With Explanation

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 permissions.

Per the AWS Cloud Operations and EventBridge documentation, when events are sent across AWS accounts — particularly from multiple accounts in an AWS Organization — the target event bus in the receiver account must include a resource-based policy that explicitly allows events:PutEvents API calls from the sender accounts or the organization ID.

Even if the sender accounts have IAM permissions to call PutEvents, the receiving event bus must trust those accounts via a resource policy. Without this configuration, EventBridge automatically rejects incoming cross-account events, and those events never reach the target Lambda function for processing.

AWS guidance states that “Cross-account event delivery requires a resource-based policy on the event bus that grants permissions to the source accounts or organization.” The policy can include either individual AWS account IDs or the organization’s root ID.

In this scenario, because the events originate from multiple accounts and there is no resource policy on the target event bus to authorize those sender accounts, the events are not delivered.

Therefore, the correct cause is C – the resource-based policy on the target event bus must be modified to allow PutEvents API calls from the sender accounts.

Per the AWS Cloud Operations, Monitoring, and Automation documentation, the correct workflow for automated operational remediation is:

Amazon CloudWatch Agent is installed on each EC2 instance (Option A) to collect local log data and push it to Amazon CloudWatch Logs.

A CloudWatch Metric Filter (Option C) is then defined to identify specific error strings or patterns within those logs (e.g., “HTTP 5xx” or “Service Unavailable”). When such an event occurs, CloudWatch Alarms are triggered.

Upon alarm activation, Amazon EventBridge rules (Option E) are configured to respond automatically by invoking an AWS Systems Manager Automation runbook, which executes an action to restart the web server process on the affected instance via SSM Agent.

This approach aligns directly with AWS’s recommended CloudOps remediation pattern, known as event-driven automation, which ensures minimal downtime and eliminates manual intervention.

Options involving CloudTrail (B) or SES notifications (D) are incorrect because they are unrelated to log-based application monitoring and automated remediation workflows.

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

AWS CloudFormation supports the DeletionPolicy attribute to control what happens to a resource when a stack is deleted. For Amazon RDS DB instances, setting DeletionPolicy: Snapshot instructs CloudFormation to retain a final DB snapshot automatically at stack deletion. CloudOps best practice recommends using this native mechanism for data retention and auditability, avoiding manual scripts or out-of-band processes. Options A, B, and D introduce operational overhead and potential human error. With DeletionPolicy set to Snapshot, the environment can be repeatedly created and torn down while preserving data states for later restoration with minimal manual steps. This aligns with IaC principles—declarative, repeatable, and reliable—and supports efficient lifecycle management of ephemeral development stacks.

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.

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

To secure inbound web traffic over HTTPS when using an Application Load Balancer, AWS CloudOps best practices recommend terminating TLS at the load balancer. This is achieved by attaching an SSL/TLS certificate directly to the ALB listener. Therefore, option B is required. By terminating HTTPS at the ALB, traffic between clients and the load balancer is encrypted, and the ALB can then forward traffic to backend EC2 instances using HTTP or HTTPS based on design requirements.

The certificate used for a public-facing retail website must be trusted by internet browsers. AWS Certificate Manager (ACM) public certificates are specifically designed for this purpose and are trusted by common browsers and operating systems. Therefore, option D is required. ACM manages certificate provisioning, renewal, and deployment automatically, which significantly reduces operational overhead.

Option A is incorrect because attaching certificates to each EC2 instance is unnecessary and does not scale well. Option C is incorrect because private certificates are intended for internal use cases and are not trusted by public browsers. Option E is incorrect because ACM-managed public certificates cannot be exported; they must be attached directly to supported AWS services such as ALBs.

This approach aligns with AWS CloudOps guidance for secure, scalable, and highly available HTTPS architectures.

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.

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

AWS Lambda automatically publishes operational metrics to Amazon CloudWatch, including Duration, which represents the time a function invocation takes to run. To alert when processing time exceeds 10 seconds, the most direct and operationally efficient solution is to create a CloudWatch alarm on the Lambda Duration metric and configure the alarm action to publish to an SNS topic. This meets the monitoring requirement without requiring log parsing or additional query mechanisms.

Option B fits best because it uses the built-in metric stream for Lambda observability: CloudWatch metrics are near real-time, require minimal configuration, and alarms are a standard CloudOps practice for proactive notification. The alarm can be configured with the appropriate statistic (for example, Maximum or p99 via metric math where applicable) and a threshold of 10,000 milliseconds, ensuring the operations team is notified before performance degrades further.

Option A is incorrect because PostRuntimeExtensionsDuration is not the primary runtime metric for function execution time, and extracting runtime from logs is unnecessary. Option C changes the function timeout to 10 seconds, which would cause failures rather than simply notifying on slow executions. Option D is more operationally complex because it relies on log queries; CloudWatch alarms are more straightforward when a native metric exists.

According to the AWS Cloud Operations and Database Reliability documentation, Amazon RDS Multi-AZ deployments provide high availability and automatic failover by maintaining a synchronous standby replica in a different Availability Zone.

In the event of instance failure, planned maintenance, or Availability Zone outage, Amazon RDS automatically promotes the standby to primary with minimal downtime (typically less than 60 seconds). The failover is transparent to applications because the DB endpoint remains the same.

By contrast, read replicas (Option B) are asynchronous and do not provide automated failover. Auto Scaling (Option C) applies to EC2, not RDS. RDS Proxy (Option D) improves connection management but does not add redundancy.

Thus, Option A — converting the RDS instance into a Multi-AZ deployment — delivers the required high availability and business continuity with minimal operational effort.

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

The requirement is to fix performance degradation from predictable peak CPU load without service disruptions. The most reliable and operationally standard approach is horizontal scaling with an Auto Scaling group driven by CloudWatch metrics/alarms (or target tracking). Launching additional instances and distributing traffic (typically behind a load balancer) increases capacity while keeping existing instances serving requests—no reboot or stop/start required.

Option D meets the requirement because Auto Scaling can add capacity when CPU exceeds a threshold and remove capacity when demand falls. This improves performance during peak periods and maintains availability. It is also operationally efficient: scaling actions are automated, consistent, and can be tuned with cooldowns/health checks.

Options A and C describe vertical scaling (instance resize). Resizing an EC2 instance type generally requires stopping the instance, changing the type, and starting it again—this is disruptive for a single-instance web server and often causes downtime. Option B (restarting the application) directly introduces disruption and does not address underlying capacity constraints; it can also worsen customer impact during peaks.

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 CloudOps automation and monitoring best practices, CloudWatch dashboards should be provisioned as infrastructure-as-code (IaC) resources using AWS CloudFormation to ensure consistency, repeatability, and version control. AWS CloudFormation supports the AWS::CloudWatch::Dashboard resource, where the DashboardBody property accepts a JSON object describing widgets, metrics, and layout.

By exporting the existing dashboard configuration as JSON and embedding it into the CloudFormation template, every deployment of the application automatically creates its corresponding dashboard. This method aligns with the CloudOps requirement for automated deployment and operational visibility within the same stack lifecycle.

AWS documentation explicitly states:

“Use the AWS::CloudWatch::Dashboard resource to create a dashboard from your template. You can include the same JSON you use to define a dashboard in the console.”

Option A requires manual execution. Options C and D incorrectly reference or reuse existing dashboards, failing to produce unique, deployment-specific dashboards.

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.

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

The correct answer is A because AWS KMS symmetric customer managed keys with automatic key rotation provide encryption, access control, and compliance with minimal operational effort. AWS CloudOps documentation states that Amazon EBS encryption supports AWS KMS symmetric keys only, and customer managed keys allow administrators to define custom key policies to control access.

Automatic key rotation is supported for symmetric customer managed keys and rotates the backing key material once every year, fully satisfying the company’s rotation requirement without manual intervention. This approach minimizes operational overhead while maintaining strong security controls and auditability.

Option B is incorrect because AWS owned keys do not allow custom key policies and therefore cannot meet the access control requirement. Option C is incorrect because asymmetric KMS keys are not supported for EBS encryption. Option D is incorrect because imported key material requires manual rotation and re-import, increasing operational complexity and risk.

AWS CloudOps security best practices strongly recommend customer managed symmetric keys with automatic rotation when organizations need fine-grained access control, regulatory compliance, and low maintenance overhead.

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

The correct answer is D because target tracking scaling policies are designed to automatically maintain a specific metric at a desired target value with the least administrative effort. AWS CloudOps documentation states that target tracking works like a thermostat, continuously adjusting capacity to keep the selected metric close to the defined target.

By specifying a target value of 40% average CPU utilization, the Auto Scaling group automatically scales out or in based on real-time demand without requiring manual thresholds, multiple alarms, or predefined scaling steps. This approach is fully managed by AWS and is the most operationally efficient option.

Option A is incorrect because scheduled scaling is based on predictable traffic patterns and cannot respond dynamically to real-time load changes. Option B is incorrect because simple scaling reacts only after an alarm is triggered and does not continuously maintain a target value. Option C is less efficient because step scaling requires additional configuration and tuning of scaling steps.

AWS CloudOps best practices clearly recommend target tracking scaling policies for maintaining steady performance metrics with minimal operational overhead.

Per the AWS Cloud Operations, Networking, and Security documentation, the best practice for serving private S3 content securely to end users is to use Amazon CloudFront with Origin Access Control (OAC).

OAC enables CloudFront to access S3 buckets privately, even when Block Public Access settings are enabled at the account level. This allows content to be delivered globally and securely without making the S3 bucket public. The bucket policy explicitly allows access only from the CloudFront distribution, ensuring that users can retrieve PDF files only via CloudFront URLs.

This configuration offers:

Automatic scalability through CloudFront caching,

Improved security via private access control,

Minimal administration effort with fully managed services.

Other options require manual handling or make the bucket public, violating AWS security best practices.

Therefore, Option B—using CloudFront with Origin Access Control and a restrictive bucket policy—provides the most secure, efficient, and low-maintenance CloudOps solution.

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

The most operationally efficient approach is A: copy the AMI to each target Region using copy-image and update the CloudFormation template to reference the correct AMI IDs per Region (commonly via Mappings or parameters). AMIs are regional resources, so an AMI built in us-east-1 cannot be launched directly in other Regions without copying. The copy-image operation is the standard, supported method to replicate an AMI across Regions while preserving the image configuration and backing snapshots in the destination Region.

Once AMIs exist in each Region, CloudFormation can be executed in each Region using the same template logic. Adding mappings for AMI IDs keeps the deployment consistent and repeatable, aligning with Infrastructure as Code practices and minimizing manual steps.

Option B is more work than necessary because copying snapshots and re-creating AMIs adds extra steps and increases the chance of inconsistency. Option C is incomplete because the template will fail or launch incorrect resources if it references an AMI ID that does not exist in the target Region. Option D is not feasible because an Auto Scaling group is a regional construct and cannot span multiple Regions from a single stack update in us-east-1.

The AWS Cloud Operations and Infrastructure-as-Code documentation specifies that when using AWS CloudFormation, resources are created in parallel by default unless explicitly ordered using DependsOn.

If a custom resource (Lambda) depends on another resource (like an EC2 instance) to exist before execution, a DependsOn attribute must be added to enforce creation order. This ensures the EC2 instance is launched and available before the custom resource executes its automation logic.

Updating the service token (Option B) doesn’t affect order of execution. The cfn-response module (Option C) handles callback communication but not sequencing. Fn::If (Option D) is for conditional creation, not dependency control.

Therefore, Option A is correct — adding a DependsOn attribute guarantees that CloudFormation provisions the EC2 instance before executing the Lambda custom resource.

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.

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.

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

The correct answer is B because cross-account role assumption requires two explicit permissions. AWS CloudOps documentation states that the target role must trust the principal, and the principal must be allowed to call sts:AssumeRole.

In the member account, the role’s trust policy must list the IAM group ARN (or the identity account) as a trusted principal. In the identity account, the IAM group must have an inline or attached policy that allows the sts:AssumeRole action for the target role ARN. This dual configuration enables secure and controlled cross-account access.

Option A is incorrect because trust policies cannot be attached to IAM groups. Option C is incorrect because sts:PassRole is used for passing roles to AWS services, not for assuming roles. Option D is incorrect because roles do not grant permissions via inline policies to principals.

This approach aligns precisely with AWS CloudOps guidance for multi-account IAM design.

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.