A Dynamics 365 Sales AI Consultant Associate is preparing to turn on predictive opportunity scoring for a mid-size sales organization. The organization has years of opportunity history, but many closed records are missing values for fields such as estimated revenue and actual close date. What should the consultant address first?
Select an answer to reveal the explanation.
Short Explanation
Think of the scoring model like a new hire who learns your sales process entirely by studying old deal files. If half those files are missing the deal size or the date it actually closed, the new hire draws the wrong lessons no matter how smart they are. So before flipping the switch on, the smart move is going back through the historical opportunities and filling in the gaps that matter most, like revenue and close date, so the model has something solid to learn from. Freezing new opportunity creation does not touch the old, messy records at all, so that trap does not actually fix anything. And piling on more pipeline stages just reorganizes how future deals get tracked; it does not repair what already happened in the past. The one move that genuinely prepares the model is cleaning the historical data itself, because a prediction engine is only as good as the history it was trained on.
Full Explanation
The correct answer is B. Predictive opportunity scoring learns from patterns in historical won and lost opportunities, so if key fields like estimated revenue and actual close date are blank or inconsistent across those records, the model has less signal to learn from and its predictions become less reliable. Cleaning and populating this historical data is a prerequisite the consultant should handle before enabling the feature, not an optional nice-to-have. Option A is incorrect because the feature does not require flawless data; it requires enough usable historical data, and a small amount of missing data does not make scoring impossible. Option C is incorrect because pausing new opportunity creation does nothing to fix historical records and would disrupt the business without improving the model. Option D is incorrect because adding pipeline stages changes how future deals are tracked going forward, but it does not repair the missing values in the existing historical records that the model depends on for training, so it does not solve the actual data quality problem the consultant needs to fix first.