Question 550
At Granite SaaS, the tenant database platform team maintains a multi-tenant business application backed by Azure SQL with requirements for tenant isolation and consistent provisioning. The operations owner asks for a validation result for: An Azure SQL maintenance workflow sends all task notifications to a terminated employee's personal mailbox and incidents go unnoticed. Which observed result BEST proves the intended problem is corrected?
Explanation
Correct Answer
A forced task failure reaches the active on-call channel and leaves a retrievable notification and acknowledgement record. That observable result supports resolution of: An Azure SQL maintenance workflow sends all task notifications to a terminated employee's personal mailbox and incidents go unnoticed. The corresponding corrective change is: Configure monitored team-owned alert channels and escalation for task successes, failures and skipped schedules. The Granite SaaS reviewer can distinguish demonstrated database behavior from an untested change claim.
Incorrect Answers
The task runs on schedule after the engineer's account is disabled and the operations team receives completion evidence. That result is evidence for another corrective change: Move the work to a managed scheduled automation mechanism with a service identity, ownership metadata and alerting. The alternative problem is: A database task's schedule is stored as a local engineer's calendar reminder, so it stops running when that employee leaves. The current Granite SaaS issue is instead: An Azure SQL maintenance workflow sends all task notifications to a terminated employee's personal mailbox and incidents go unnoticed. Its decisive acceptance evidence would be: A forced task failure reaches the active on-call channel and leaves a retrievable notification and acknowledgement record.
A simulated invalid schema change is rejected before data mutation and a valid change passes the recovery and verification gates. That result is evidence for another corrective change: Add approved dry-run checks, a backup or restore checkpoint and stop-on-failure conditions before the database task proceeds. The alternative problem is: A patch automation workflow runs destructive schema changes without a preflight validation, health gate or recovery checkpoint. The current Granite SaaS issue is instead: An Azure SQL maintenance workflow sends all task notifications to a terminated employee's personal mailbox and incidents go unnoticed. Its decisive acceptance evidence would be: A forced task failure reaches the active on-call channel and leaves a retrievable notification and acknowledgement record.
The job history lists each approved target exactly once and contains no unexpected production database. That result is evidence for another corrective change: Maintain elastic job target groups through a controlled inventory and verify membership and execution scope before each rollout. The alternative problem is: A database elastic job runs on only one test database because the production target group was never updated after adding new databases. The current Granite SaaS issue is instead: An Azure SQL maintenance workflow sends all task notifications to a terminated employee's personal mailbox and incidents go unnoticed. Its decisive acceptance evidence would be: A forced task failure reaches the active on-call channel and leaves a retrievable notification and acknowledgement record.