Salesforce AI Specialist Practice Exam

Which consideration is most important when selecting data for training the model in Einstein's Model Builder for lead qualification?

1. Selecting only the data from the most recent sales quarter to ensure the model's predictions are up to date.

2. Including data points from both converted and not converted leads to train the model.

3. Using data exclusively from leads that have converted in the past, to focus the model on positive outcomes.

The correct answer is: Including data points from both converted and not converted leads to train the model.

The correct choice emphasizes the importance of diversity in the training data, which is critical for developing a robust and effective model in Einstein's Model Builder. By including data points from both converted and not converted leads, the model gains a comprehensive understanding of the full spectrum of lead outcomes. This balanced approach allows the model to learn how different lead characteristics contribute to conversion, thereby improving its ability to make accurate predictions. Incorporating both converted and non-converted leads ensures that the model does not become biased towards only successful outcomes. This is essential for lead qualification, as it enables the model to recognize various signals and characteristics that differentiate leads likely to convert from those that are less likely. Using only recent data or data exclusively from converted leads can lead to limitations. Recent data may not capture long-term trends and patterns, while focusing solely on converted leads can create a model that overlooks important features inherent in unconverted leads. This approach limits the model’s ability to generalize and apply its learning to new, unseen leads effectively. Thus, B highlights a crucial consideration in building a predictive model that is both accurate and reliable.