PEGACPDS88V1 Pegasystems Certified Pega Data Scientist 8.8 Free Practice Exam Questions (2025 Updated)

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

Customer name is usually not appropriate to be used as a predictor. A predictor is a property that influences the customer behavior and can be derived from various sources such as customer profile, interaction history, proposition details, etc. Customer name is not likely to have any impact on the customer’s preferences or responses, and it may also violate privacy regulations. 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.

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

Pega Process AI™ lets you bring your own predictive models to Pega and use predictions in case types to optimize the way your application processes work and meet your business goals.

To use the outcome of a predictive fraud model in the case type that processes the incoming claim, you need to use the model outcome in the condition of a decision step2. This way, you can route suspicious claims to a fraud expert for closer inspection based on the model’s prediction.

In a decision strategy, the Adaptive Model decision component belongs to the Decision Analytics category. This category contains components that use advanced analytics techniques, such as adaptive models, predictive models, text analytics models, etc., to make predictions or recommendations. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/decision-analytics-components

Pega machine learning supports the creation of two distinct types of predictive models: categorical and binary. Categorical models predict the outcome of a variable that can have multiple values, such as product category or customer segment. Binary models predict the outcome of a variable that can have only two values, such as yes or no, accept or reject, etc. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-predictivemodel/main.htm

When you increase the performance threshold setting of an adaptive model rule, the number of active predictors may decrease. The performance threshold is the minimum performance that a predictor must have to be included in the model. If you increase this value, some predictors may not meet the criteria and be excluded from the model. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

As a data scientist, if you are tasked with configuring two predictions that are driven by an adaptive model, you need to pay extra attention to adaptive model settings.

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

Positive and negative behavior Reference:

To configure an adaptive model, you must identify positive and negative behavior that indicate specific customer behavior.

Customer Decision Hub Reference:

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

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.

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.

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