Meridian's analytics team currently produces dashboards that report last quarter's on-time performance (descriptive analytics). Leadership now wants to know which upcoming routes are likely to see demand spikes. Which shift in the role of data science does this represent?

Data science spans a spectrum: descriptive analytics (summarizing past events, such as on-time performance dashboards), diagnostic analytics (why something happened), predictive analytics (forecasting future outcomes, such as demand spikes), and prescriptive analytics (recommending actions). Meridian's request to forecast route-level demand is a move from descriptive toward predictive analytics, requiring statistical or machine-learning models rather than simple reporting. The choice claiming a shift from prescriptive to descriptive analytics reverses the maturity order, because prescriptive analytics is more advanced than descriptive analytics, not the reverse, and dashboards are not prescriptive. The choice saying there is no shift is false, because predictive analytics requires model training and validation techniques that plain descriptive reporting does not use. The choice claiming a shift from data science to pure business intelligence is also wrong, because it asserts that forecasting needs no modeling, which contradicts the premise of demand forecasting as a modeling use case. For the exam, know the descriptive-diagnostic-predictive-prescriptive spectrum and be able to place a stated business ask correctly on it; this is a recurring way CPMAI tests data-science literacy at PM depth.