A consultant preparing to turn on predictive lead scoring for a client for the first time confirms the client already has the required premium licensing in place. (Select TWO.) Which two additional conditions must also be true before the feature can be enabled and produce useful scores?
Select all correct answers, then click Submit.
Short Explanation
You need someone with enough administrative privilege, such as a system administrator, to open the Sales Insights settings area and turn the model on. You also need enough historical closed leads, with both won and lost outcomes, so the model can learn which patterns predicted success. Seller training and moving leads into a separate artificial-intelligence-only environment aren't requirements.
Full Explanation
The correct answers are the administrative-access requirement and the historical-data requirement. Enabling predictive scoring is an administrative configuration task, so it requires someone with the appropriate privilege to open the Sales Insights settings and turn the model on for the relevant table; without that access, the feature simply cannot be activated. It also requires a sufficient body of historical closed leads, including both won and lost outcomes, because the model learns which signal patterns correlate with conversion by studying past results, and a training set skewed toward only wins or too small overall will not produce a reliable model. The Viva Learning training requirement is incorrect because there is no training prerequisite tied to whether scores display; sellers see scores based on configuration and licensing, not completion of external training modules. The separate Dataverse environment requirement is incorrect because predictive scoring runs against the existing Dataverse environment where the Leads table already lives; there is no requirement to migrate records into a separate dedicated environment, and doing so would be unnecessary and disruptive. The two genuine prerequisites are administrative access to configure the feature and adequate historical outcome data for the model to train on.