A consultant is setting up predictive lead scoring for the first time in an environment with dozens of custom lead fields, several of which capture internal notes that would not meaningfully predict conversion. During the scoring model configuration wizard, what should the consultant do before publishing the model?
Select an answer to reveal the explanation.
Short Explanation
Dozens of fields on a lead record does not mean dozens of useful predictors, some of those custom fields are internal notes with no real signal about who's going to convert. Throwing every field into the model regardless is tempting but counterproductive, extra noise from irrelevant fields tends to blur the pattern rather than sharpen it. Leaving the defaults untouched and assuming the system will quietly weed out the bad ones later is also a mistake, that curation step doesn't happen automatically after publishing, it has to happen during setup. And writing off custom fields entirely, as if only the original out-of-box fields on a lead can ever be scored, isn't accurate either, custom fields are just as eligible for inclusion as anything else on the form. The right move is a deliberate pass through the field list, keeping what plausibly predicts conversion and leaving out what doesn't.
Full Explanation
The correct answer is D. The predictive lead scoring configuration wizard asks the consultant to choose which fields the model should analyze, and deliberately selecting fields likely to correlate with conversion, such as lead source, industry, or engagement history, while excluding fields like internal notes that carry no predictive signal, produces a cleaner and more accurate model. Option A is incorrect because including every field, especially free-text or internal-use fields, introduces noise that can dilute the model's accuracy rather than improve it, more fields is not automatically better. Option B is incorrect because the wizard does not automatically discard low-value fields after publishing, field selection is a manual step the consultant must complete during setup, leaving the defaults unreviewed can leave irrelevant fields in the model indefinitely. Option C is incorrect because predictive lead scoring is not restricted to out-of-box fields only, custom fields on the Lead entity can be selected for the model just like standard fields, so excluding them entirely is unnecessary and would waste useful predictive signal.