Production Incident Response and Diagnostics Questions
Covers structured practices, techniques, tooling, and decision making for detecting, triaging, mitigating, and learning from failures in live systems. Core skills include rapid incident triage, establishing normal baselines, gathering telemetry from logs, metrics, traces, and profilers, forming and testing hypotheses, reproducing or simulating failures, isolating root causes, and validating fixes. Candidates should know how to choose appropriate mitigations such as rolling back, applying patches, throttling traffic, or scaling resources and when to pursue each option. The topic also includes coordination and communication during incidents, including incident command, stakeholder updates, escalation, handoffs, and blameless postmortems. Emphasis is also placed on building institutional knowledge through runbooks, automated diagnostics, improved monitoring and alerting, capacity planning, and systemic fixes to prevent recurrence. Familiarity with common infrastructure failure modes and complex multi system interactions is expected, for example cascading failures, resource exhaustion, networking and deployment issues, and configuration drift. Tooling and methods include log analysis, distributed tracing, profiling and debugging tools, cross system correlation, and practices to reduce mean time to detection and mean time to resolution.
EasyTechnical
0 practiced
You're ending an on-call shift and need to hand off an ongoing low-severity incident involving a stuck retraining job: write the content for the handoff note and describe the verbal briefing you would give. The handoff must include current hypothesis, actions taken, commands to reproduce the current state, metrics links, and explicit next steps. Be specific about what privileges or access the next engineer will need.
EasyTechnical
0 practiced
A fleet of inference nodes has started failing with GPU out-of-memory (OOM) errors, causing pods to crash and reducing capacity. List immediate mitigations to restore service quickly (both conservative and riskier), and describe the diagnostics and telemetry you would collect to determine whether the root cause is memory fragmentation, a new model regression, or a memory leak in native code.
MediumTechnical
0 practiced
Design a streaming/approximate quantile solution (e.g., t-digest or GK algorithm) to compute p50/p95/p99 latencies over a high-volume log stream where storing all raw latencies is infeasible. Describe the algorithm choice, memory bounds, error guarantees, how to merge sketches from multiple collectors, and how to expose alerts when tail latency exceeds thresholds.
HardTechnical
0 practiced
You suspect silent data corruption in a feature pipeline introducing subtle bias into model predictions. Design a forensic investigation plan that includes scoping the time window, validating checksums or hashes, tracing lineage back to raw sources, comparing backups or snapshots, replaying historical data to reproduce the bias, and a remediation plan (backfill, retraining, and validation). Mention specific tools or SQL patterns you would use.
HardSystem Design
0 practiced
Design a multi-region, highly available model serving architecture capable of 1M QPS with end-to-end median latency under 100ms. Requirements: zero single-region outage impact for read-only traffic, consistent access to feature data within 500ms, automatic failover, and minimal client-perceived divergence across regions. Describe how you would replicate features, handle model artifacts, route traffic, maintain consistency, and perform failover and failback.
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