PAS-C01 Technology Practice Question
A company is using SAP Landscape Management (LaMa) to automate the provisioning and decommissioning of SAP application servers in an AWS environment. The SAP systems are deployed on EC2 instances using Amazon EBS volumes. The operations team has observed that when LaMa triggers a scale-in operation to remove an application server, the instance is terminated, but the associated EBS volumes (including the root volume) are not deleted, resulting in orphaned volumes and increasing storage costs. The team wants to ensure that EBS volumes are automatically deleted when an instance is terminated, especially during LaMa-driven operations. Which solution should the team implement?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set the 'Delete on Termination' attribute to 'True' on all EBS volumes attached to the instance when it is launched by LaMa.
The correct solution is to set the 'Delete on Termination' attribute to 'True' on all EBS volumes attached to the instance when it is launched by LaMa. This ensures that when the instance is terminated (e.g., during a LaMa scale-in operation), the EBS volumes are automatically deleted, preventing orphaned volumes. Option A is incorrect because Amazon Data Lifecycle Manager (DLM) is used for managing snapshot schedules, not for immediate deletion on termination. Option C is incorrect because while a Lambda function could delete volumes after termination, it adds complexity and may have timing issues; the simpler and more reliable method is to set the flag at launch. Option D is incorrect because CloudTrail logs API calls but does not directly perform actions; setting up Step Functions for this purpose is overly complex compared to the native 'Delete on Termination' attribute.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure an Amazon Data Lifecycle Manager (DLM) policy to delete volumes that are not attached to a running instance.
Why it's wrong here
DLM is for managing snapshots, not for deleting volumes based on attachment state.
- ✓
Set the 'Delete on Termination' attribute to 'True' on all EBS volumes attached to the instance when it is launched by LaMa.
Why this is correct
With DeleteOnTermination enabled, when LaMa terminates the instance, the volumes are automatically deleted, preventing orphaned volumes.
- ✗
Create an AWS Lambda function that is triggered by EC2 instance termination events to delete unattached EBS volumes.
Why it's wrong here
A Lambda function reacting to EC2 termination events would delete volumes only after the instance is already terminated, but LaMa’s scale-in operation terminates the instance without setting the `DeleteOnTermination` flag on the EBS volumes. The correct solution must modify the volume attribute at launch or via a lifecycle hook so that deletion occurs atomically with termination, not as a separate asynchronous cleanup. This option is tempting because it is a common pattern for cleaning up orphaned resources in general AWS environments, and it would work correctly if the volumes were simply left unattached after a manual termination, but it does not address the root cause of LaMa not setting the flag during automated provisioning.
- ✗
Use AWS CloudTrail to monitor TerminateInstances events and trigger an AWS Step Function workflow to delete the volumes.
Why it's wrong here
This is complex and reactive; enabling DeleteOnTermination is a proactive and reliable method.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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