PEGACPDS88V1 Certified Pega Data Scientist 8.8 Questions and Answers

To predict if a customer is likely to churn, you use a model of type decision tree. A decision tree is a type of predictive model that uses a set of rules to classify customers into different categories based on their attributes and behavior. A decision tree can predict a binary outcome (such as churn or not churn) or a multi-class outcome (such as low risk, medium risk, or high risk). References: https://academy.pega.com/module/predictive-analytics/topic/using-decision-tree-models

 The Monte Carlo method enables the company to generate data that simulates customer behavior and can be used as input for adaptive decisioning. The generated data is based on predefined probabilities and distributions that reflect realistic scenarios. References: https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

The pxResult property is used to access the output of a prediction that is driven by a PMML model. It contains the predicted value or class for each record in the input data set. References: https://academy.pega.com/module/predictive-analytics/topic/using-pmml-models

When capturing historical data in an adaptive model, the outcome of the interaction and the value of all predictors are captured for every customer interaction.

Call center, SMS, and Retail store Reference:

MyCo can use Call center, SMS, and Retail store as real-time channels to present Next-Best-Actions.

To measure the boost in success rate AI generates, you need a control action that is offered to random customers.

Proactive retention is a strategy that anticipates potential customer churn and takes actions to prevent it before it happens. It uses predictive analytics to identify customers who are at risk of leaving and offers them incentives or solutions to retain them. References: https://academy.pega.com/module/one-one-customer-engagement/topic/proactive-retention

The PMML standard is designed to facilitate the exchange of models between applications.

The rank property is automatically recomputed for each decision component. It indicates the order in which the actions are presented to the customer, based on their priority and propensity. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/ranking-actions

Customer Decision Hub Reference:

To optimize the click-through rate of a web banner, you need to create a Customer Decision Hub prediction.

The adaptive model component in a decision strategy computes a propensity value for each action. Propensity is the likelihood of a positive response for a given action and predictor profile. It ranges from 0 to 100. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

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.

A solid arrow from a “Set Property” component to a “Filter” component means that a property from the “Set Property” component is referenced by the “Filter” component. For example, you can use a “Set Property” component to calculate a customer’s age and then use a “Filter” component to remove actions that are not suitable for that age group. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting-properties

To confirm the continuing accuracy of your adaptive models, two tasks that are part of a regular inspection are check the performance and success rate of the models and check the performance of individual predictors.

Sentiment refers to the general attitude of the author towards a subject and can be negative.

Predictive analytics is a branch of data science that uses statistical techniques and machine learning algorithms to find repeatable patterns in data and make predictions about future outcomes or behaviors. References: https://academy.pega.com/module/predictive-analytics/topic/predictive-analytics-overview

Evidence is the number of customers who exhibited statistically similar behavior. It indicates how much data the model has collected for a given predictor profile. The higher the evidence, the more reliable the model is. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm