Assess candidates' ability to design an interdisciplinary research and implementation plan for an AI-driven, privacy-preserving, equity-centered urban mobility system. The quiz will test knowledge and skills across transportation engineering (modeling, system design, operational planning), data governance and privacy-preserving ML techniques (differential privacy, federated learning, stewardship), socio-economic impact assessment (equity metrics, stakeholder engagement, cost–benefit, displacement risks), and climate resilience metrics (emissions, vulnerability, adaptation). Tasks include defining objectives and research questions, proposing a methodological framework, specifying data requirements and governance models, selecting evaluation and resilience metrics, outlining implementation timeline, budget and stakeholder roles, and identifying ethical, legal, and risk-mitigation strategies. Responses should demonstrate clear interdisciplinary integration, measurable outcomes, and feasible urban-scale implementation strategies suitable for graduate-level research and policy translation.