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.
HardTechnical
68 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.
EasySystem Design
86 practiced
For a junior AI Engineer joining an on-call rotation, list the minimal contents that should be present in a runbook for a model-serving incident. Include detection steps, immediate mitigation commands (or playbook steps), how to verify a fix, contacts and escalation path, last-resort rollback steps, and common pitfalls to avoid.
EasyTechnical
68 practiced
You are paged: the prediction service p99 latency jumped from 120ms to 800ms and users are seeing slow responses. You have dashboards for CPU, memory, GC, request rate, error rate, and distributed traces. Describe the immediate triage checklist you would execute in the first 15 minutes: what metrics or traces you check first, what hypothesis you form, and what short-term mitigations you might apply while you investigate.
MediumTechnical
76 practiced
How would you instrument automated detection for covariate shift, prior (label) shift, and concept drift in a production ML pipeline? Specify which telemetry to collect (feature histograms, label lag, model confidence), the statistical tests or algorithms you would use (KS test, PSI, drift detectors), and an alerting strategy that balances sensitivity and false positives.
MediumTechnical
76 practiced
After deploying model v2, you observe an increased error rate for ~2% of users while overall traffic is unchanged. Describe a step-by-step triage: how to identify whether the issue is data-specific (certain user cohorts), model-specific (version bug), or infra-related. Include the use of canary analysis, logs/traces correlation, feature-distribution checks, and mitigations (feature flags, rollback, throttling).
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