Pegasystems PEGACPDS88V1 Exam Dumps Questions

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Full version: PEGACPDS88V1 Exam Dumps Questions
1.What two tasks does a system architect need to perform to export historical data? (Choose Two)
A. Switch to a resilient repository
B. Set the sample percentage for positive and negative outcomes
C. Export the data set
D. Validate the predictors used by the adaptive models
E. Create a data set
Answer: C,E
Explanation:
Two tasks that a system architect needs to perform to export historical data are export the data set
and create a data set.
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2.In a strategy defined in the "Retension" issue and the "X-Sell" group, you can import________
A. Actions from all groups under the "Retention" issue
B. Actions from "X-Sell" group
C. Actions from the "Sales" issue
D. All active actions
Answer: A
Explanation:
According to the Pega Academy1, a strategy is a unit of reasoning that defines how to select an
action for a customer. A strategy can be defined in different issues and groups, which are categories
that help organize actions. An issue represents a business goal (such as retention or sales), and a
group represents a subcategory of an issue (such as cross-sell or up-sell).
In a strategy defined in the “Retention” issue and the “X-Sell” group, you can import actions from all
groups under the “Retention” issue1. This allows you to use actions that are relevant to your business
goal and compare them with other actions in the same issue.
3.A strategy designer has created 10 actions in the Sales/Credit Cards group and 10 actions in the
Sales/Mortgages group. He would like to import all 10 actions from the Credit Cards group and only
two actions from the Mortgage group into one decision strategy.
What is the minimum number of Proposition Data components he needs to use in his strategy?
A. one
B. twelve
C. three
D. two
Answer: C
Explanation:
If a strategy designer has created 10 actions in the Sales/Credit Cards group and 10 actions in the
Sales/Mortgages group and would like to import all 10 actions from the Credit Cards group and only
two actions from the Mortgage group into one decision strategy, the minimum number of Proposition
Data components he needs to use in his strategy is three.
4.Predictive Analytics is a________________
A. real time predictive dashboard
B. method of visualizing our data___________________
C. science concerned with finding repeatable patterns in
D. query, reporting and a search tool
Answer: C
Explanation:
Predictive analytics is a branch of data science that uses statistical techniques and machine learning
algorithms to find repeatable patterns in data and make predictions about future outcomes or
behaviors.
References: https://academy.pega.com/module/predictive-analytics/topic/predictive-analytics-
overview
5.U+ Telecom wants to engage in proactive retention to reduce churn. As a data scientist, you create
a prediction that calculates the probability that a client is likely to cancel a subscription.
What type of prediction do you create?
A. Case management_____
B. Customer Decision Hub
C. Text analytics
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Answer: B
Explanation:
As a data scientist, you create a prediction that calculates the probability that a client is likely to
cancel a subscription. The type of prediction you create is Customer Decision Hub.
6.Which statement best describes the goal of Next-Best-Action?
A. Ensure that the customer is always given a desirable offer
B. Balance customer needs with business objectives
C. Ensure that every customer receives the same action
D. Provide insight into business processes
Answer: B
Explanation:
Balance customer needs with business objectives
Reference:
The goal of Next-Best-Action is to balance customer needs with business objectives.
7.U+ Bank introduces a new credit card that has no historical customer behavior data. U+ Bank wants
to offer this credit card on the personalized web portal. Given the scenario, which rule type must you
use?
A. Decision table
B. When rule
C. Adaptive model
D. Pega machine learning model
Answer: C
Explanation:
Given the scenario where U+ Bank introduces a new credit card that has no historical customer
behavior data and wants to offer this credit card on the personalized web portal, you must use an
adaptive model.
8.The standardized model operations process (MLOps) lets you replace a low-performing predictive
model that drives a prediction with a superior one.
When you place the new model in shadow mode in the production environment, the current
model___________
A. uses the outcomes of the new model as predictors
B. is automatically replaced
C. drives the prediction
D. no longer drives the prediction
Answer: C
Explanation:
When you place the new model in shadow mode in the production environment, the current model still
drives the prediction, but the new model runs in parallel and collects performance data for
comparison.
References: https://academy.pega.com/module/predictive-analytics/topic/mlops
9.Which two factors do you inspect to access the general health of the adaptive models in Prediction
Studio? (Choose Two)
A. Performance of the models
B. Number of responses
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C. Number of decisions
D. Model transparency
Answer: A,D
Explanation:
These factors indicate how accurate and explainable the models are, which are key measures of
model health. The number of responses and decisions are related more to model usage rather than
health.
10.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. Adaptive models perform a binary model calculation
B. Adaptive models require underlying predictive models
C. Adaptive models require a historical data set to start learning
D. Adaptive models perform a continuous model calculation
Answer: 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.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm
11.What type of a predictor can you use in an adaptive model?
A. Symbolic
B. Integer
C. Page Type
D. Logical
Answer: A
Explanation:
In an adaptive model, you can use Symbolic predictors.
12.How does a prediction help in proactive retention?
A. The prediction selects the next best action
B. The prediction suggests the best offer
C. The prediction predicts the customer's churn risk
D. The prediction identifies successful offers in past interactions
Answer: C
Explanation:
A prediction helps in proactive retention by predicting the customer’s churn risk. A prediction is an
estimate of the likelihood of a future outcomebased on historical data and statistical models. A
prediction can help identify customers who are at risk of leaving and target them with appropriate
actions to retain them.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisionin
g-/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm
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13.The purpose of regular inspection is to detect factors that negatively influence the performance of
the adaptive models and the success rate of the actions.
Which two issues should be discussed with the business? (Choose Two)
A. Predictors with a low performance_________
B. Actions that have a low number of responses
C. Actions that are offered so often that they dominate other actions
D. Predictors that are never used
E. Actions for which the model is not predictive
Answer: A,C
Explanation:
When performing regular inspection of adaptive models, two issues that should be discussed with the
business are predictors with a low performance and actions that are offered so often that they
dominate other actions.
14.When selecting the list of predictors for an adaptive model you should
A. Select up to a maximum of 500 predictors
B. Consider properties from a wide range of sources
C. Always use numeric type for integer properties
D. Select at least one date property
Answer: B
Explanation:
When selecting the list of predictors for an adaptive model you should consider properties from a wide
range of sources. Predictors are properties that influence the customer behavior and can be derived
from various sources such as customer profile, interaction history, proposition details, etc.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm
15.The result of a Predictive Model is stored in a property called__________.
A. pyPrediction
B. pxResult
C. pyOutcome
D. pxSegment
Answer: D
16.Adaptive model predictors are selected from the____________.
A. communication channel
B. similar propositions
C. customer profile
D. proposition profile
Answer: C
Explanation:
Adaptive model predictors are selected from the customer profile, which contains information about
the customer’s attributes and behavior. Predictors can be either scalar or aggregate properties that
capture customer context, such as channel, location, time, etc.
References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-
archived/topic/configuring-adaptive-models
17.A telecommunications company wants to apply text analysis to incoming emails to understand how
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satisfied its customers are with various products and services. That setup requires natural language
processing (NLP) of the email texts.
What is one of the types of analysis that occurs during natural language processing?
A. Presumptive analysis
B. Intent analysis
C. Subjective analysis
D. Semantic analysis
Answer: D
Explanation:
One of the types of analysis that occurs during natural language processing is Semantic analysis.
18.Two results of an adaptive model are
A. Priority and Propensity
B. Priority and Evidence
C. Propensity and Performance
D. Propensity and Rank
Answer: C
Explanation:
Two results of an adaptive model are propensity and performance. Propensity is the probability that
the customer will accept or respond to an offer. Performance is a measure of how well the adaptive
model predicts customer behavior over time.
References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-
archived/topic/adaptive-models-overview
19.In a decision strategy, the Adaptive Model decision component belongs the
A. Decision Analytics category
B. Business Rules category
C. Arbitration category
D. Predictive Model category
Answer: A
Explanation:
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
20.In a predictive model rule, the predictors must be mapped to
A. Strategy properties
B. Customer properties
C. Model properties
D. Action properties
Answer: B
Explanation:
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
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21.When implementing a Next-Best-Action project, which step is recommended to be taken first?
A. Define Issue and Group hierarchy
B. Define propositions
C. Define business rules
D. Define prioritization formula
Answer: A
Explanation:
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
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