Practice Free PEGACPDS88V1 Certified Pega Data Scientist 8.8 Exam Questions Answers With Explanation

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

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

When building a model using Pega machine learning, the validation hold-out set is used to train the models and to check for robustness of candidate models.

Pega AI is primarily based on self-learning models Reference:

Pega AI is primarily based on self-learning models.

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

As a data scientist, a valid reason to adjust the default response timeout in a prediction is to suit the use case.

Adaptive model components can output the customer’s propensity to accept an 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

To associate an adaptive model with each action in a decision strategy created from scratch, you must define the adaptive model instances in the Adaptive Decision Manager.

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

Adaptive models perform a binary model calculation. This means that adaptive models predict the likelihood of a positive or negative response for each action and customer profile. Adaptive models do not require underlying predictive models or historical data sets to start learning. They learn from customer responses in real time and continuously update their predictions. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

The purpose of predictions is to build adaptive models.

Balance customer needs with business objectives Reference:

The goal of Next-Best-Action is to balance customer needs with business objectives.

A taxonomy is a list of valid categories that can be used to classify text documents or entities. A taxonomy can be hierarchical or flat, depending on the level of detail required. References: https://academy.pega.com/module/text-analytics/topic/creating-taxonomy

A scoring model allows you to differentiate between Good, Better, and Best outcomes for a given proposition or action. A scoring model assigns a numerical value to each outcome based on its desirability or profitability for the business. References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models

The principle of parsimony states that from two models with similar performance, we choose the one with the fewest number of predictors. This is because a simpler model is easier to understand, maintain, and deploy. References: https://academy.pega.com/module/predictive-analytics/topic/evaluating-predictive-models

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

Customer properties are used to map predictors to customer data that is available in the system. They can be either scalar or aggregate properties. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/mapping-predictors-customer

Propensity is the adaptive model output that is automatically mapped to a strategy property. 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

Predictions combine predictive analytics and best practices in data science. Two best practices included in Pega Customer Decision Hub™ predictions are using a control group and selecting the model with the highest performance.

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

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

Pega Customer Decision Hub™ uses the PCV*L arbitration formula to select the next best action for each customer. The purpose of the formula is to balance customer needs with business objectives.

 The implementation of Next-Best-Action must involve defining business issue and group hierarchy, which are used to organize and categorize propositions based on business objectives and customer needs. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

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

When developing a predictive model, the outcome value of a continuous model type can represent the purchase value of an offer.

 Through analysis of customer lifecycles, Next-Best-Action anticipates retention issues and takes proactive actions to prevent customer churn. 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

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

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.

The Monte Carlo method is a way to create a data set that includes mock-up customer data. You can use this method to generate random values for customer properties, such as age, gender, income, etc., based on predefined probabilities and distributions. References: https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

Customer Decision Hub Reference:

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

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

You can use one Set Property component to calculate the average margin of four actions by using an expression that sums up the margin values of each action and divides by four. You can then use this property in other components, such as Filter or Prioritize. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting-properties