A data scientist is developing a model to predict the outcome of a vote for...

→ Extrapolated data refers to making predictions about data points that fall outside the observed range or distribution. Since the sample data (80%) is heavily skewed toward a small subset of professions and locations, predicting results for the remaining, unrepresented professions and regions involves extrapolation.

Why the other options are incorrect:

A: Interpolation occurs within the bounds of observed data — not the issue here.

C: In-sample data refers to training data, which is overrepresented in this case.

D: Out-of-sample data is a concern in generalization but extrapolation is more specific here.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Extrapolation introduces risk when models are used outside the range of data they were trained on, especially if certain subgroups are underrepresented.”

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