Customer and User Obsession Questions

Demonstrating a deep commitment to understanding and advocating for customers and end users. Candidates should show how they prioritize user needs in decision making, even when it conflicts with other priorities, and provide concrete examples of advocating for users internally. Topics include using qualitative and quantitative research to surface user pain points, validating assumptions with user evidence, designing or improving experiences to solve real problems, maintaining ongoing connection to users through feedback loops, and influencing stakeholders to keep the organization user focused. Examples may range from entry level empathy and direct customer learning to strategic changes driven by user insight.

MediumBehavioral

0 practiced

Describe a time when user research changed a model or feature you built. What research method informed the change, what technical work did you perform, how did you roll it out, and what quantitative and qualitative measures did you use to verify improvement?

HardTechnical

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Case study: A content moderation ML model misclassifies posts written in a minority language, causing wrongful suspensions and public backlash. As the AI Engineer and product advocate, design a remediation plan covering immediate customer-facing fixes, root-cause analysis, data strategy to reduce bias, and policy/process changes to prevent recurrence.

HardTechnical

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You have 150 user-reported issues for a model-driven feature. As the AI Engineer responsible for triage, propose a prioritization framework that balances severity, user impact (number of affected users and value), reproducibility, technical effort, and business goals. Describe scoring and an example of top-priority vs low-priority issues.

HardTechnical

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You're leading a cross-functional initiative to change product roadmaps based on user feedback about harmful AI behavior. How would you align engineering, product, legal, and support teams to implement changes with minimal user disruption and acceptable business impact? Describe cadence, decision criteria, and communication strategy.

HardSystem Design

0 practiced

Design a data governance and training-data pipeline that ensures training data represents diverse user populations, tracks consent, preserves privacy, supports deletion requests, and provides lineage/traceability for individual examples used to produce model outputs for auditing. Describe metadata, storage, and processes.

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