PEGACPDS88V1 Questions and Answers

This is because shadow mode allows you to test a new model in parallel with an existing model without affecting the decision outcomes. You can compare the performance of both models and decide whether to replace or keep the existing model. 

https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN-StudentGuide.pdf

Up to 100 fields to limit the impact on model speed Reference:

When configuring an adaptive model, it is recommended to select up to 100 potential predictors to limit the impact on model speed.

When building a predictive model, you compare the performance of predictive models at the Model Comparison stage. This stage allows you to select the best model based on various metrics, such as accuracy, lift, or area under curve (AUC). References: https://academy.pega.com/module/predictive-analytics/topic/comparing-predictive-models

Proactive retention is applicable when a customer is likely to churn. Proactive retention is a strategy that aims to prevent customer attrition by identifying customers who are at risk of leaving and offering them incentives or solutions to retain them. Proactive retention requires predicting the customer’s churn risk and selecting the next best action accordingly. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning-/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm

In an adaptive model, you can use Symbolic predictors.

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.

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.

When implementing a Next-Best-Action project, the recommended first step is to define Issue and Group hierarchy, which are used to organize and categorize propositions based on business objectives and customer needs. This step helps to align the project with the business vision and goals. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

When selecting the list of predictors for an adaptive model you should consider properties from a wide range of sources. Predictors are properties that influence the customer behavior and can be derived from various sources such as customer profile, interaction history, proposition details, etc. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

The outcome of a scoring model indicates the likely response to an offer that is presented to a customer. For example, a scoring model can predict if a customer will accept, reject, or defer an offer for a credit card upgrade. References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models

Pega adaptive models learn about customer behavior in real time by analyzing the responses to each offer and updating their predictions accordingly. They do not require historical data, human effort, or inbound channels to function. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview

Two tasks that a system architect needs to perform to export historical data are export the data set and create a data set.

The difference between predictive and adaptive analytics is that adaptive models use the customer data as predictors, while predictive models use the customer data as outcomes. Adaptive models learn from real-time customer interactions and update their predictions accordingly. Predictive models use historical customer data to train and validate their predictions. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview

The key component of a Next-Best-Action strategy is a strategy, which is a graphical representation of the business logic that determines which actions to offer to each customer and in what order. A strategy can use various components, such as business rules, predictive models, filters, prioritizers, etc., to achieve this goal. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

In Prediction Studio, model transparency thresholds can be set for a model.

The prioritize component is used to order actions based on a numerical value, such as priority, propensity, or custom expression. You can choose to sort the actions in descending or ascending order. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/prioritizing-actions