The development of measurement methods in the era of Industry 4.0 and 5.0 has made 3D scanning one of the most universal methods for acquiring geometric data. In the literature, the term resolution is used ambiguously, referring to detector resolution, structural resolution, or sampling resolution. In this paper, sampling resolution is defined as the distance between adjacent measurement points, which determines the density of the acquired data. In industrial practice, this parameter is often selected intuitively, which can lead either to redundant data generation or to a loss of geometric detail. The aim of this work is to evaluate the impact of the specified sampling resolution and the resulting data density on the accuracy, repeatability, and quality of geometric feature mapping. The tests were conducted on a ball-bar standard using a mobile metrological scanner based on laser triangulation. The geometric deviation results were compared with reference values obtained from a coordinate measuring machine. The analysis revealed a nonlinear relationship between sampling density, scanning time, and reconstruction accuracy, as well as the presence of an information-saturation threshold beyond which increasing the resolution no longer improves the results. The research was supplemented with tests aimed at assessing the system’s ability to map small-diameter holes. The work addresses the problem of terminological ambiguity. The results contribute to the development of an Expert system with machine-learning components that enables rapid estimation of measurement uncertainty based on archival data. The tools being developed support advanced decision-making in selecting measurement systems for specific industrial applications.