Problem Solving and Learning from Failure Questions
Combines technical or domain problem solving with reflective learning after unsuccessful attempts. Candidates should describe the troubleshooting or investigative approach they used, hypothesis generation and testing, obstacles encountered, mitigation versus long term fixes, and how the failure informed future processes or system designs. This topic often appears in incident or security contexts where the expectation is to explain technical steps, coordination across teams, lessons captured, and concrete improvements implemented to prevent recurrence.
MediumTechnical
0 practiced
Logs were lost due to rotation misconfiguration and disk failure; you must run a forensic investigation. Describe alternative data sources you can use (metrics, traces, CDN logs, client telemetry), how to reconstruct a timeline from partial signals, and what changes you would make to logging, retention, and shipping to prevent recurrence.
HardSystem Design
0 practiced
Design a globally consistent feature-flag system that supports emergency disables (kill-switch), audit trails, gradual rollouts, and safe rollbacks across microservices. Consider replication and caching strategies for low-latency reads, eventual consistency trade-offs, and how to invalidate flags quickly during emergencies.
EasyTechnical
0 practiced
Explain the difference between a short-term mitigation and a long-term root-cause fix using a concrete database outage example. For each, describe technical steps, risks, how you would test them, and how you'd prevent the mitigation from becoming permanent technical debt.
HardTechnical
0 practiced
An intermittent race condition reproduces only under high concurrency in production. Describe in detail how you would capture deterministic evidence (instrumented logging with unique IDs, selective tracing, core dumps, record-replay tools), minimize customer impact while debugging (sampled tracing, canaries), and strategies to deploy a safe fix and validate it at scale.
MediumTechnical
0 practiced
Explain how distributed tracing (e.g., OpenTelemetry) helps identify slow requests that cause SLO breaches. Describe required instrumentation (spans, context propagation), sampling strategies (head vs tail sampling), and how to correlate traces with logs and metrics to build an RCA timeline.
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