Adaptive model components can output__________
D
Explanation:
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.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-
/rule-decision-/rule-decision-adaptivemodel/main.htm
An adaptive model instance is created when you________
A
Explanation:
An adaptive model instance is created when you execute a strategy containing the adaptive model
component. The adaptive model component references an adaptive model rule that defines the
predictors and the outcome of the model. The adaptive model instance stores the data and the
statistics
of
the
model
for
a
specific
context
and
action.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm
Which data is usually not appropriate to be used as a predictor?
C
Explanation:
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. Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm
Which statement about predictive models is true?
A
Explanation:
Predictive models need historical data to be created. Predictive models are statistical models that
use historical data to learn patterns and trends and make predictions for future outcomes. Predictive
models can be built with Pega machine learning or imported from third-party tools such as PMML or
H2O.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm
The use of an imported third-party model in a decision strategy is____
C
Explanation:
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.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm
Proactive retention is applicable when a customer is
D
Explanation:
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. Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning-
/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm
Pega machine learning supports the creation of which two distinct types of predictive models?
(Choose Two)
A,C
Explanation:
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. Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm
Which decision component enables you to use a PMML model?
A
Explanation:
The decision component that enables you to use a PMML model is Predictive Model. Predictive
Model is a component that references a predictive model rule that defines the input parameters and
the output score of the model. You can use a predictive model component to reference a PMML
model that is imported from a third-party tool and use it in your decision strategy. Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm
The standardized model operations process (MLOps) lets you replace a low-performing predictive
model that drives a prediction with a new one.
Which feature of MLOps lets you monitor the new model in the production environment without
affecting the business outcomes?
B
Explanation:
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
A large online store uses Pega Customer Decision Hub to smoothly adapt to changing customer
behavior. Adaptive models help accomplish this business objective as the models learn from
customer responses.
Which statement about adaptive models is correct? s
A
Explanation:
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. Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm