Cross Functional Influence and Leadership Questions
This topic covers a candidate's ability to influence, align, and lead across organizational boundaries without formal authority. Candidates should demonstrate how they build and sustain credibility and trusted relationships with product, engineering, design, business, analytics, and executive partners to shape decisions, drive initiatives, and change culture. Assessment focuses on stakeholder mapping and prioritization, coalition building, negotiation and persuasion, tailoring communication and storytelling for different audiences, managing up and sideways, facilitating meetings and escalations, and aligning competing incentives. Evaluators will look for concrete tactics such as relationship building, data driven persuasion, compelling business cases, governance and accountability mechanisms, trade off negotiation, creation of scalable practices, and ways to measure and communicate organizational impact. The scope also includes executive presence, emotional intelligence, handling resistance and skepticism, recovering trust after setbacks, and sustaining cultural or operational changes across teams.
MediumSystem Design
0 practiced
Design a concise SLA between data engineering and ML teams for production data delivery used in model training and serving. Include 4 key SLA items (e.g., freshness, completeness), owners, monitoring approach, and an escalation path when SLAs are breached. Provide a small SLA table example with thresholds.
HardSystem Design
0 practiced
Design an internal model marketplace and adoption workflow that enables product teams to discover, request, and adopt models produced by a central ML team. Describe required metadata (e.g., model card fields), access and versioning controls, incentive mechanisms to encourage reuse, and the review/onboarding steps to adopt a model safely into product.
HardSystem Design
0 practiced
Design an organizational model for enterprise-level model risk classification and review that supports autonomous product squads while enforcing company-wide controls. Describe classification criteria, triggers for automated versus manual reviews, tooling integration (CI/CD, ticketing, audit logs), and the escalation path when high-risk models are detected.
EasyTechnical
0 practiced
Provide a concise example of a data-driven argument you would use to convince the design team to change the UI to surface model recommendations. Include the data sources you would gather (instrumentation, experiments, user research), the key analysis you'd run, and the expected business metric uplift you'd communicate.
MediumTechnical
0 practiced
Product wants to maximize engagement while legal demands stricter privacy constraints and engineering warns about tight latency budgets. As the ML Engineer responsible, propose a negotiated technical and roadmap solution that balances engagement, privacy, and latency. Include at least three concrete technical options and a phased rollout plan.
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