A municipal permitting backend scales an Auto Scaling group using average CPU only. During filing deadlines the SQS work queue grows for minutes while CPU stays modest, so citizens see long waits. Which scaling-policy improvement best restores elasticity?
Select an answer to reveal the explanation.
Short Explanation
CPU can look sleepy while the inbox is on fire. For queue-driven permitting work, scale on how deep the backlog is—not how warm the CPUs feel. That is the difference between keeping up and watching tickets pile up.
Full Explanation
When work is buffered in SQS, CPU utilization often lags backlog growth. Improving Auto Scaling to use queue depth or backlog-per-instance metrics aligns capacity with demand for event-driven civic backends. Fixed nightly fleets, unrelated S3 metrics, or artificially tiny fleets do not correct the signal mismatch and can worsen citizen wait times.