Assess students' ability to analyze methodological trade-offs when integrating machine learning with ethnographic methods, testing understanding of design choices (sampling, data collection, preprocessing), epistemological tensions (interpretability vs. contextual depth), validity and reliability, bias mitigation and ethics, human-in-the-loop approaches, strategies for triangulation and data integration, and the justification of choices to maintain rigor, transparency, and reproducibility in graduate-level research.