A Data 360 Consultant needs to build a model on Einstein Studio to predict the time- to- close of an opportunity brought into Data 360 from CRM. Based on the supported model types, which statement is true?
A.
The consultant should use a Multiclass Classification model to bucket the time-to-close into specifications.
B.
The consultant should use a Binary Classification model to determine if the time-to-close is long or short.
C.
The consultant should use a Multiclass Classification model because the outcome is represented as text data.
D.
The consultant should use a Regression model because the target outcome is a numeric measure.
The AI pattern works when the model is grounded in appropriate Data 360 data and its outputs can be operationalized safely. The consultant should use a Regression model because the target outcome is a numeric measure. fits because predictions or generative experiences are only useful when the data is representative, governed, and connected to Salesforce execution patterns such as scoring jobs, Flow, or grounded retrieval. The distractors fall short because they either move the problem into the wrong system, add needless duplication, ignore Data 360 object relationships, or rely on a feature built for a different lifecycle stage. In a real implementation, those choices usually create brittle pipelines, stale data, security exposure, or segments that look correct on paper but fail when activated. Thinking like an architect, the selected option places the logic where Data 360 can govern it and reuse it reliably. This is the nuance exam questions often test: the platform capability must match both the technical layer and the business timing requirement, not just sound related to data.
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