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Crisis Management and Decision Making Questions

Evaluates how a candidate responds to urgent, high stakes, or time sensitive incidents such as production outages, security incidents, regulatory investigations, compliance failures, customer escalations, or other critical operational problems. Interviewers assess the candidate's ability to rapidly gather and prioritize incomplete or ambiguous information, perform quick diagnosis and root cause analysis, triage and prioritize multiple competing issues, and make pragmatic decisions under time pressure using clear decision criteria. The scope includes short term containment actions, trade offs between temporary workarounds and longer term fixes, risk identification and mitigation, escalation thresholds, and knowing when to pause for more information or to delegate and call for help. Candidates should demonstrate clear and concise stakeholder communication, documentation of rationale, attention to accuracy and quality under deadlines, stress and resilience strategies, and mechanisms to follow up and prevent recurrence by implementing safeguards and lessons learned. At senior levels this also includes leading teams through incidents, setting priorities under pressure, coordinating cross functional stakeholders, maintaining team morale, and measuring outcomes and impact. Strong answers use concrete examples of specific incidents, the decision criteria used, trade offs made when data was limited, how uncertainty and stress were managed, and what was learned and institutionalized afterward.

MediumTechnical
0 practiced
An attacker has poisoned a public dataset your model used for training, and a deployed model shows performance degradation on specific classes. Describe how you would perform a forensic investigation to confirm poisoning, including data lineage checks, model-influence analysis, and evidence to collect for legal or security teams.
EasyTechnical
0 practiced
You are handed logs showing an increase in 500 errors from the model-serving API, but the model health metrics look fine. List the top five areas you would inspect (e.g., infra, network, auth) and why. Explain how you'd prioritize them under time pressure.
HardSystem Design
0 practiced
Describe how you would harden ML pipelines to reduce silent failures (e.g., missing columns, silent type-casts) that only manifest in production weeks later. Propose concrete automated checks, CI steps, contract tests between services, and runtime guards.
EasyTechnical
0 practiced
Provide a short template for a post-incident retrospective for an ML production incident. The template should include: timeline, root cause, contributing factors, immediate remediation, long-term fixes, action owners, and metrics to track for closure.
HardSystem Design
0 practiced
A complex incident summary: multiple models degraded after a cloud provider network partition. Propose a short recovery plan that addresses immediate service restoration, model consistency (ensuring models and feature stores are in sync), and validation steps before returning traffic to normal. Include rollback and reconciliation steps.

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