Ten minutes after a deploy, 500 errors jump from 0.1% to 8% on the main API. Walk through how you handle the incident.

What the interviewer wants to hear

• Mitigate first: roll back or disable the feature flag
• Communicate status to the team and stakeholders
• Use metrics, logs and traces to find the failing path
• Compare the deploy diff, including config and migrations
• Confirm recovery with metrics before closing
• Run a blameless postmortem with follow-up actions

Model answer

My first goal is to stop the damage, not to find the root cause. Because the spike started right after a deploy, I roll back to the previous version or turn off the feature flag, and I post in the incident channel who is leading and what we are doing. One catch: if the deploy included a database migration, I check that the old code still works with the new schema before rolling back. Once error rates recover, I investigate using the dashboards, the error tracker’s grouped stack traces and distributed traces for failing requests, filtering by endpoint and region. I compare the diff, including environment variables, dependency upgrades and migrations, and reproduce the issue in staging. After the fix ships safely, I write a blameless postmortem covering the timeline, impact, root cause and actions, such as better tests, canary deploys or an alert that would have fired sooner.

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