Ace Your PEGACPDS88V1 Pegasystems Certification Exam

To detect the sentiment of the incoming emails, you need to configure a text analytics prediction1234 in Prediction Studio. A text analytics prediction is a type of prediction that uses natural language processing (NLP) to analyze text data and extract insights, such as topics, entities, and sentiments. You can use a text analytics prediction to detect the sentiment of an email based on its content and assign a score ranging from -1 (negative) to 1 (positive). This can help you improve the customer experience by identifying dissatisfied customers and taking appropriate actions.

In Customer Decision Hub™, the factor in the arbitration formula that is calculated by using AI is Propensity.

Evidence is the number of customers who exhibited statistically similar behavior to the current customer and responded to the modeled offer. It indicates how reliable the propensity score is based on the available data. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview

• Import a third party PMML model1: PMML stands for Predictive Model Markup Language, which is an XML-based standard for representing predictive models. You can import a PMML model that was created by another tool or platform into Pega and use it in your strategies.
• Create an adaptive self-learning model1: An adaptive model is a type of predictive model that learns from customer responses and adapts its predictions over time. You can create an adaptive model in Pega and configure its parameters, such as learning rate, decay rate, and performance goal.
• Use Pega machine learning to build a model1: Pega machine learning is a feature that allows you to build predictive models using various algorithms, such as decision trees, logistic regression, neural networks, and random forests. You can use Pega machine learning to build a model from your data and evaluate its performance.