매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
Certified Pega Decisioning Consultant 25 온라인 연습
최종 업데이트 시간: 2026년01월01일
당신은 온라인 연습 문제를 통해 Pegasystems PEGACPDC25V1 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 PEGACPDC25V1 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 111개의 시험 문제와 답을 포함하십시오.
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Explanation:
To present offers from the two groups, you must map a real-time container to the Top-level or Issue-level. A real-time container is a configuration that defines how to deliver offers and treatments to a specific channel, such as a website or a mobile app. By mapping a real-time container to the Top-level or Issue-level, you can enable all the offers under that level to be available for delivery through that channel. Verified Pega Academy - Decisioning Consultant - Configuring real-time containers
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Explanation:
To select the best offer from both groups based on customer behavior, you need to ensure that the propensity is enabled in Arbitration tab. Propensity is a measure of how likely a customer is to accept an offer, based on their past behavior and profile. By enabling propensity in Arbitration tab, you can compare the propensities of different offers across groups and select the one with the highest propensity as the next best action. Verified Pega Academy - Decisioning Consultant - Arbitrating actions
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Explanation:
To test the visualization and the rendering of the email content, including replacing of the placeholders with customer information, you use a seed list from the Test email tab. A seed list is a predefined set of customers that you can use to test your email treatments before sending them to your target audience. You can select one or more customers from the seed list and send them a test email with your treatment. You can then verify how the email looks in their inbox and how the placeholders are replaced with their actual values. Verified [Pega Academy - Decisioning Consultant - Testing email treatments]
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Explanation:
To implement this requirement, you need to create an action and the associated web treatment, a real-time container, and a business structure. An action is a proposition that you want to present to a customer, such as a credit card offer. A treatment is the way you present the action to a customer, such as an image or a text message. A real-time container is a configuration that defines how to deliver actions and treatments to a specific channel, such as a website or a mobile app. A business structure is a hierarchy of business groups and business issues that organizes actions into meaningful categories. Verified Pega Academy - Decisioning Consultant - Creating actions and treatments, Pega Academy - Decisioning Consultant - Configuring real-time containers, [Pega Academy - Decisioning Consultant - Defining business structure]
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Explanation:
Propensity is the name of the property that the system computes automatically when you use an Adaptive Model decision component. Propensity is a measure of how likely a customer is to accept an action, based on their past behavior and profile. An Adaptive Model component uses machine learning to calculate the propensity for each action and store it in a property with the same name as the action. Verified Pega Academy - Decisioning Consultant - Using adaptive models
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The score calculation is independent of the strategy and no change is required. When you use a scorecard component in a decision strategy, you only need to specify the name of the scorecard rule and the output property that will store the score value. The scorecard rule itself defines how the score is calculated based on the input properties and factors. Therefore, if you update the scorecard rule to include a new property in the calculation, you do not need to make any changes in the decision strategy for the updated scorecard to take effect. Verified [Pega Academy - Decisioning Consultant - Using scorecards]
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To enable access to customer properties in a Filter component, you need to use a Data Import component. A Data Import component allows you to read data from various sources, such as data sets, data pages, or data flows, and make it available for other components in the strategy. In this case, you need to use a Data Import component that reads from a customer data source that contains income and age properties. Verified Pega Academy - Decisioning Consultant - Importing data
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To implement the business requirement, you need to add a decision table to a decision strategy and pass the credit score as the parameter. A decision table allows you to define rules based on one or more input parameters and return an output value. In this case, you can use the credit score as an input parameter and return the risk category/grade as an output value. You can then use this output value to filter out customers who are not in the low-risk segment (AAA). Verified Pega Academy - Decisioning Consultant - Using decision tables
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To output the most profitable shoe, you need to add a Prioritize component in the blank space. A Prioritize component allows you to rank actions based on one or more properties. In this case, you can rank the shoes based on the Profit property and select the highest ranked shoe as the output. Verified Pega Academy - Decisioning Consultant - Prioritizing actions
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To access a property from an unconnected component, you use the component-dot-property construct. For example, if you want to access the property .Rank from an unconnected component named ActionRanking, you use ActionRanking.Rank. Verified Pega Academy - Decisioning Consultant - Accessing properties from unconnected components
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When you create a new trigger in the Next-Best-Action Designer, Pega Customer Decision Hub automatically generates a decision strategy for that trigger and channel. You do not need to create or modify any strategies manually. Verified Pega Academy - Decisioning Consultant - Creating triggers
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Aggregation components are used to perform calculations on a list of actions, such as sum, average, count, minimum, or maximum. For example, you can use an aggregation component to calculate the total value of all the actions in a group. Verified Pega Academy - Decisioning Consultant - Aggregating actions
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Explanation:
Using the decision table, you can find the label for a customer with a credit score of 240 and an average balance of 35000 by following these steps:
Start from the top row and check if the customer’s credit score is less than 150. If yes, then the label is Very Poor. If no, then move to the next row.
Check if the customer’s credit score is less than 175 and their average balance is less than 25000. If yes, then the label is Poor. If no, then move to the next row.
Check if the customer’s credit score is less than 200 and their average balance is less than 50000. If yes, then the label is Fair. If no, then move to the next row.
Check if the customer’s credit score is less than 250 and their average balance is less than 75000. If yes, then the label is Good. If no, then move to the last row.
The last row applies to all other cases that do not match any of the previous conditions. The label for this row is Very Poor.
In this case, the customer’s credit score is not less than 150, so the first row does not apply. The customer’s credit score is less than 175, but their average balance is not less than 25000, so the second row does not apply either. The customer’s credit score is not less than 200, so the third row does not apply. The customer’s credit score is less than 250 and their average balance is less than 75000, so the fourth row applies. Therefore, the label for this customer is Poor.