Amazon AWS Certified CloudOps Engineer - Associate SOA-C03 Exam Questions

Answer : A

CloudFormation cross-stack references are the correct low-effort method for sharing resource values between related stacks. The first template can define stack outputs and mark them with an Export name. The second template can use Fn::ImportValue to reference those exported values, such as VPC ID, subnet IDs, route table IDs, or security group IDs. AWS documentation states that you use the Export output field and Fn::ImportValue intrinsic function to create cross-stack references. A custom resource is unnecessary and adds code, permissions, and maintenance. Mappings are static lookup tables, not a way to retrieve live resources from another stack. Passing values manually as parameters works, but it increases administrative effort and error risk. Therefore, exports and imports are correct.

Answer : C

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.

Answer : C

AWS Systems Manager Run Command includes a built-in rate control feature that allows administrators to control the maximum number of concurrent executions across target instances. This directly addresses the requirement to limit downloads to 30 at a time without custom orchestration or additional services.

By targeting instances using tags, the solution automatically scales as new instances are added, which aligns with future growth expectations. Rate control ensures controlled concurrency and protects the private repository from overload.

Option A is manual and does not scale operationally. Option B introduces unnecessary complexity with Lambda and concurrency management that does not map cleanly to instance execution concurrency. Option D significantly increases architectural complexity without added value.

Run Command with rate control is the simplest, most native, and most scalable solution.

Answer : D

The traffic pattern described is a linear deployment: shift a fixed percentage of traffic at fixed time intervals until the new version receives 100% of traffic. AWS CodeDeploy for Amazon ECS includes a predefined deployment configuration named CodeDeployDefault.ECSLinear10PercentEvery3Minutes, which shifts 10% of traffic every 3 minutes until all traffic is shifted. A canary deployment sends an initial small percentage to the new version and then shifts the remaining traffic after a bake period, not in equal repeated increments. A rolling deployment replaces tasks in batches but does not express ALB traffic shifting percentages in this way. Blue/green is the broader deployment model used by CodeDeploy, but the specific release strategy requested is linear. Therefore, option D is correct.

Answer : C

The Auto Scaling group cannot scale out because its maximum capacity is already set to 2, the same as the current desired capacity. To automate the resolution, the CloudOps engineer must raise the maximum capacity and configure a scaling policy that adds instances when CPU utilization exceeds the threshold. Since the workload is CPU-bound and peak periods are unpredictable, metric-based scaling is appropriate. Option A adds unmanaged capacity outside the Auto Scaling group, which defeats automated fleet management. Option B correctly increases maximum capacity but incorrectly changes to burstable instances; burstable instances are a poor choice for sustained CPU-heavy workloads. Option D is incomplete because increasing desired capacity alone does not guarantee future automated scale-out unless the maximum capacity also permits it. Therefore, option C is correct.

Answer : A

Because the application workload is 95% reads, the best database scaling approach is to add or remove Aurora Replicas. Aurora Auto Scaling can automatically adjust the number of Aurora Replicas in a DB cluster based on metrics such as average CPU utilization or average connections across replicas. This allows the read tier to scale as campaign traffic increases while the primary writer continues to handle the smaller write workload. Option B is incorrect because Aurora Auto Scaling scales the number of replicas; it does not vertically resize existing replicas. Options C and D are worded around generic AWS Auto Scaling rather than Aurora Auto Scaling and use less appropriate metric relationships. Scaling based on replica CPU utilization directly addresses read pressure. Therefore, configure Aurora Auto Scaling for Aurora Replicas.

Answer : A

The CloudWatch agent procstat plugin is specifically designed to collect per-process metrics such as CPU usage by process ID. It allows continuous, automated monitoring without manual intervention and integrates directly with CloudWatch.

Default CloudWatch metrics provide only instance-level CPU utilization and do not expose process-level details. Manual logins and Lambda-based polling introduce unnecessary operational overhead and are not scalable.

Therefore, configuring the procstat plugin is the most efficient solution.