Pass the Amazon Web Services AWS Certified Associate SOA-C03 Questions and answers with ExamsMirror

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.

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

The correct answer is A because AWS Systems Manager Patch Manager natively supports tag-based targeting, which automatically includes both existing and future instances that match specified tag criteria. AWS CloudOps documentation states that patch policies can target managed nodes by instance tags, allowing administrators to dynamically scope patching operations without additional automation.

By defining the patch policy target as instances with an environment tag value of “Prod,” Patch Manager automatically applies patch baselines to all matching instances. Any new EC2 instance launched with the same tag is included automatically, requiring no manual intervention or additional services. This approach delivers the least operational overhead while remaining fully scalable and compliant.

Options B, C, and D are incorrect because they introduce unnecessary complexity by adding AWS Lambda functions, resource groups, or EventBridge rules. AWS CloudOps best practices emphasize using native Systems Manager capabilities whenever possible to reduce operational burden and failure points.

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

Use Amazon S3 Event Notifications with AWS Lambda to trigger image processing on object creation. S3 natively supports invoking Lambda for events such as s3:ObjectCreated:*, providing a serverless, low-latency pipeline without managing additional services. AWS operational guidance states that “Amazon S3 can directly invoke a Lambda function in response to object-created events,” allowing you to pass event metadata (bucket/key) to the function for resizing and writing results back to S3. This approach minimizes operational overhead, scales automatically with upload volume, and integrates with standard retry semantics. SNS or SQS can be added for fan-out or buffering patterns, but they are not required when the requirement is simply “invoke the Lambda function on upload.” CloudWatch alarms do not detect individual S3 object uploads and cannot directly satisfy per-object triggers. Therefore, configuring S3 → Lambda event notifications meets the requirement most directly and aligns with CloudOps best practices for event-driven, serverless automation.

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

VPC Flow Logs show the request arriving and being ACCEPTed on dstport 8080 and the corresponding response being REJECTed on the return path to the client’s ephemeral port (59003). AWS networking guidance states that security groups are stateful (return traffic is automatically allowed) while network ACLs are stateless and require explicit inbound and outbound rules for both directions. CloudOps operational guidance for VPC networking further notes that when you allow an inbound request (for example, TCP 8080) through a subnet’s network ACL, you must also allow the outbound ephemeral port range (typically 1024–65535) for the response traffic; otherwise, the return packets are dropped and appear as REJECT in flow logs. The observed pattern—request accepted to 8080, response rejected to 59003—matches a missing outbound ephemeral-range allow on the subnet’s NACL. Therefore, the cause is the subnet NACL, not security groups or on-premises ACLs. The remediation is to add an outbound ALLOW rule on the NACL for the appropriate ephemeral TCP port range back to the on-premises CIDR (and the corresponding inbound rule if asymmetric).

AWS Secrets Manager natively supports credential management and automatic rotation for Amazon RDS master user passwords. When a secret is associated with an RDS instance, Secrets Manager automatically updates the password both in the secret and on the database, without downtime or manual scripting.

AWS documentation confirms:

“AWS Secrets Manager can automatically rotate the master user password for Amazon RDS databases. Rotation is fully managed and integrated, requiring no custom code or maintenance.”

Option A introduces unnecessary Lambda automation. Option B and C use Parameter Store, which does not provide direct RDS password rotation. Therefore, Option D achieves secure, automatic credential rotation with least operational effort, fully aligned with CloudOps security automation principles.

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

The requirement is preventative: stop users from launching from unapproved AMIs. The most scalable approach with 75 approved AMIs is to tag all approved AMIs (for example, ApprovedAMI=true) and enforce usage through an IAM policy condition that only allows ec2:RunInstances when the ec2:ImageId resource (the AMI) includes the required tag. This avoids maintaining long allow/deny lists of AMI IDs and supports continuous updates: as new approved AMIs are created, tagging them automatically brings them under the policy without policy rewrites.

Option B is incorrect because service-linked roles are used by AWS services, not assigned to users for interactive authorization enforcement in this way. Option C and D are detective/remedial controls that act after instances are already launched, which does not satisfy “must prevent.” They also increase operational overhead and risk disruptions.

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.

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.

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

The correct answer is C because selective CloudFront invalidation ensures that only updated JavaScript files are refreshed across edge locations. AWS CloudOps documentation explains that invalidations remove cached objects so that CloudFront fetches the latest version from the origin on the next request.

Invalidating only changed files minimizes cost, reduces operational impact, and avoids unnecessary origin requests. This approach ensures users always receive the latest application assets without degrading backend performance.

Option A is incorrect because setting TTLs to 0 forces CloudFront to query the origin for every request, increasing load on the ALB and EC2 instances. Option B is inefficient and costly because invalidating all files is unnecessary. Option D removes the benefits of CloudFront caching and increases latency.

AWS CloudOps best practices recommend targeted invalidations during deployments to balance performance, cost, and correctness.

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.