Role Specific Job Understanding Questions
Covers familiarity with specific job families and titles and the typical responsibilities and challenges associated with them. Examples include customer success, project management, account management, business intelligence, operations, sales operations, and executive roles such as vice president positions. Candidates should show domain knowledge about daily tasks, common tools, stakeholder interactions, and specific outcomes expected in those named roles, and ask role specific questions about scope and priorities.
HardSystem Design
0 practiced
Create a cross-functional KPI framework that ties AI model metrics (AUC/F1, calibration, latency, concept drift) to business KPIs (conversion rate, retention, revenue). Provide a mapping table, define owners for each KPI, propose a reporting cadence, and describe how you would resolve conflicts when model improvements negatively affect business KPIs.
HardSystem Design
0 practiced
Draft key SLA and contractual clauses for delivering AI-powered services to enterprise customers. Cover measurable definitions for uptime and model performance guarantees (accuracy, latency), data privacy and retention commitments, incident response times and severity levels, liability caps, and terms covering model updates or deprecations.
MediumSystem Design
0 practiced
Describe how you would define and negotiate SLAs for model inference latency and accuracy with business stakeholders. Explain the difference between SLOs and SLAs, propose monitoring and alerting strategies, define an error budget approach, and describe actions to take when an SLA breach occurs.
HardTechnical
0 practiced
You observe a sudden 10% absolute drop in model accuracy after a production deployment. Create a detailed incident response plan: detection and triage steps, immediate mitigations to protect customers (feature flip/rollback), root-cause analysis checklist (data pipeline, feature drift, model artifact), stakeholder communications and timelines, and post-incident retrospective actions to prevent recurrence.
EasyTechnical
0 practiced
You're asked to explain, in plain English to a non-technical executive, the difference between training and inference in machine learning systems. Provide a concise analogy, highlight cost/time/resource implications for each phase, and give one example of why distinguishing them matters for budgeting and deployment decisions.
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