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.
EasyTechnical
0 practiced
For a production ML model, what minimum elements should a runbook contain so a first responder can contain incidents quickly? Provide a concise template that includes: key dashboards and metrics, immediate diagnosis steps, mitigation and rollback instructions, escalation contacts and SLAs, and post-incident actions.
HardTechnical
0 practiced
Discuss how to design backups and a data retention policy that support forensic investigations for ML incidents while balancing storage cost, user privacy, and legal requirements such as GDPR. Include strategies for snapshot cadence, anonymization, encryption at rest, access controls, and how long raw vs derived artifacts should be retained.
HardSystem Design
0 practiced
Design a multi-region ML inference platform that provides low-latency predictions to global users and supports automated incident detection and cross-region failover without significant model inconsistency. Describe architecture components (model registry, feature store, telemetry, failover controller), data replication strategy, consistency model, and trade-offs between latency, availability, and prediction reproducibility.
HardTechnical
0 practiced
As a staff data scientist charged with reducing recurring ML incidents across teams, describe a plan to change the organization's incident response process: identify stakeholders, propose process and tooling changes, pilot the changes, measure adoption and effectiveness, and scale the new process company-wide. Include a stakeholder map and metrics for success.
MediumTechnical
0 practiced
Describe how to implement a canary deployment strategy for ML models. Include traffic-splitting strategies, metrics to evaluate canary success (leading and lagging), statistical thresholds for automated promotion or rollback, rollback triggers, and how to automate the process in CI/CD pipelines.
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