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Free PEGACPDS88V1 Exam Braindumps (page: 7)

Page 6 of 36
PDF with 140 Questions

You are a company with a new and unique product, and you want to offer it to the right customer.
Give the scenario, which rule type should you use?

  1. Adaptive model
  2. Decision table
  3. Predictive model
  4. Scorecard

Answer(s): A

Explanation:

You are a company with a new and unique product, and you want to offer it to the right customer. Given the scenario, you should use an adaptive model rule type. An adaptive model rule type allows you to define the predictors and the outcome of the model and associate it with an action. An adaptive model learns from customer responses in real time and predicts the propensity of each customer to accept the action. An adaptive model is suitable for new products or markets where there is no historical data available.


Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule- decision-/rule-decision-adaptivemodel/main.htm



An online store is interested in increasing its revenues from cross-selling and wants to predict the acceptance rate of the offers presented on their website. A customer's propensity to accept an offer increases when_________.

  1. Similar offers were rejected by the customer
  2. The offer was rejected by similar customers
  3. Similar offers were accepted by the customer
  4. The offer was accepted by similar customers

Answer(s): C

Explanation:

This is because a customer's propensity to accept an offer depends on their past behavior and preferences. If a customer has accepted similar offers in the past, they are more likely to accept a new offer that matches their interests https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN- StudentGuide.pdf



The Predictive Model Markup Language (PMML) allows for predictive models to

  1. Perform better
  2. Be easily shared between applications
  3. Use the same modeling process
  4. Be developed faster

Answer(s): B

Explanation:

The Predictive Model Markup Language (PMML) allows for predictive models to be easily shared between applications. PMML is a standard XML format that describes the input parameters, output score, and mathematical formulas of predictive models. PMML enables interoperability between different tools and platforms that support PMML, such as Pega Customer Decision Hub.


Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data- predictivemodel-/data-predictivemodel-pmml/main.htm



How does a prediction help in proactive retention?

  1. The prediction selects the next best action
  2. The prediction suggests the best offer
  3. The prediction predicts the customer's churn risk
  4. The prediction identifies successful offers in past interactions

Answer(s): 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 outcome based 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.


Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning- /decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm






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