I started with metrics and logs which felt right, but I rambled a bit trying to cover everything at once.
Start by clarifying the symptoms and scope (latency vs throughput, which services, when it started), then systematically narrow down using metrics, logs, and traces from the top down. Propose a fix with trade-offs, and emphasize validation and prevention (e.g., canary, rollback, monitoring).
Pro tip: Anchor your answer in Amazon's leadership principles: demonstrate 'Dive Deep' by asking about specific metrics and 'Customer Obsession' by tying impact to user experience. Also, mention using AWS services like CloudWatch, X-Ray, and CodeDeploy for observability and safe deployments.
Ask questions to understand the exact symptoms: is it latency or throughput? Which services/regions? When did it start? What changed recently (deployments, config, traffic)?
Use monitoring tools (CloudWatch, X-Ray, logs) to check key metrics: CPU, memory, network, disk I/O, request rates, error rates, and latency percentiles. Identify if the issue is widespread or isolated.
Based on data, hypothesize root causes (e.g., resource saturation, code bug, dependency slowdown, network issue). Test by isolating components, checking recent changes, and using canary or A/B tests.
Apply the fix (e.g., rollback, scale up, optimize code, add caching). Validate with metrics and canary deployments, and monitor for recurrence.
Add alerts, improve observability, conduct a post-mortem, and share learnings. Consider long-term improvements like auto-scaling, circuit breakers, or architectural changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.