PEGACPDS88V1 Certified Pega Data Scientist 8.8 Question and Answers

An adaptive model is different from a predictive model as it can use strategy properties as predictors. Strategy properties are dynamic values that are calculated or derived during the execution of a decision strategy. They can capture customer context, such as channel, location, time, etc. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview

If you are debugging the Next-Best-Action decision strategy and want to see if the result of the Arbitration is correct, you should use the Prioritize strategy component.

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

The adaptive model output that is automatically mapped to a strategy property is propensity, which indicates the likelihood that the customer will accept or respond to an offer. Propensity is also known as behavior or probability in decision strategies. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/using-adaptive-models-decision

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

The use of an imported third-party model in a decision strategy is similar to the use of a model built with Pega machine learning. You can use a predictive model component in a decision strategy to reference an imported third-party model and pass the input parameters and receive the output score. You do not need to convert the third-party model into a Pega machine learning model or Pega markup language. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-predictivemodel/main.htm

You are a company with a new and unique product, and you want to offer it to the right customer. Given the scenario, you should use an adaptive model rule type. An adaptive model rule type allows you to define the predictors and the outcome of the model and associate it with an action. An adaptive model learns from customer responses in real time and predicts the propensity of each customer to accept the action. An adaptive model is suitable for new products or markets where there is no historical data available. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

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

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

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

Predictor selection and The update frequency of the models Reference:

As a highly experienced data scientist, you have access to advanced settings such as predictor selection and the update frequency of the models.

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 Customer Lifetime Value Threshold is typically used to prioritize customers based on their expected long-term value to the business. Customers who have a higher lifetime value than the threshold are considered high-value customers and receive more personalized and relevant offers. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

Text prediction is a type of natural language processing that uses AI and statistics to predict the next word in a sentence based on the preceding words1. Text analysis is a process of extracting information from text data using various methods and tools. One of the types of text analysis that a text prediction performs is entity extraction23, which is the identification and classification of named entities such as people, places, organizations, dates, etc. in a text.

Lift is a key metric for the performance of the adaptive models. To measure lift, you need a control group with random actions. Lift is the ratio of the performance of the adaptive model to the performance of the control group. A control group is a subset of customers who receive random actions instead of the ones suggested by the model. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-adm-/data-adm-model/main.htm

When you approve the updated model in the standardized machine learning process (MLOps), a change request is automatically generated in the production environment.

One of the types of analysis that occurs during natural language processing is Semantic analysis.