PEGACPDS88V1 Exam Dumps - Pegasystems PCDS Questions and Answers

These factors indicate how accurate and explainable the models are, which are key measures of model health. The number of responses and decisions are related more to model usage rather than health.

The mapping of the input fields of a third-party predictive model is done in the Predictive Model rule. The Predictive Model rule defines how to invoke and interpret the results of a third-party predictive model that is imported in PMML format. References: https://academy.pega.com/module/predictive-analytics/topic/using-pmml-models

The Pega Customer Decision Hub™ delivers Next-Best-Actions consistently through multiple channels. This applies to Mobile channels.

Propensity Reference:

The PCV*L arbitration formula used by the Customer Decision Hub to select the Next-Best-Action for each customer calculates propensity using AI.

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.

Adaptive models can start to learn without historical evidence. The starting propensity of every action is 0.5.