Import a third party PMML model 1 : PMML stands for Predictive Model Markup Language, which is an XML-based standard for representing predictive models. You can import a PMML model that was created by another tool or platform into Pega and use it in your strategies.

Create an adaptive self-learning model 1 : An adaptive model is a type of predictive model that learns from customer responses and adapts its predictions over time. You can create an adaptive model in Pega and configure its parameters, such as learning rate, decay rate, and performance goal.

Use Pega machine learning to build a model 1 : Pega machine learning is a feature that allows you to build predictive models using various algorithms, such as decision trees, logistic regression, neural networks, and random forests. You can use Pega machine learning to build a model from your data and evaluate its performance.

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 step 2 . This way, you can route suspicious claims to a fraud expert for closer inspection based on the model’s prediction.

When defining outcomes for an adaptive model, you must define one or more positive behavior values, which indicate that the customer accepted or responded to the offer. You can also define negative and neutral behavior values, but they are optional. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/configuring-adaptive-models

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

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

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

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

 When using a text prediction based on machine learning with keywords configured, the Not keywords function as negative features, meaning that they reduce the probability of detecting the topic if they appear in the text. The Must keywords and May keywords do not have any impact on the machine learning model. References: https://academy.pega.com/module/text-analytics/topic/configuring-keywords

to create a prediction that calculates the probability that a claim is fraudulent, you need to create  a case management prediction . This type of prediction allows you to use predictive models built on external platforms such as H2O.ai and apply them to case types in Pega Process AI™. You can then use the prediction outcome in a decision step to route claims based on their fraud risk.

https://academy.pega.com/challenge/creating-fraud-prediction/v3

According to the  Data Scientist Student Guide 1 , page 18, the correct answer is  B. A predictive model  and  C. A control group . A predictive model is a mathematical representation of a real-world process that can be used to predict an outcome based on input data. A control group is a subset of customers who are not exposed to a treatment (such as an offer) and are used to measure the effectiveness of the treatment by comparing their behavior with the treated group.