While configuring GetNextWork, overriding the System Settings rule
GetNextWork__WorkBasketUrgencyThreshold imposes a minimum cutoff value for assignment urgency.
Which statement accurately depicts the system behavior when the GetNextWork_WorkBaseketUrgencyThreshold setting is overridden?
When the GetNextWork_WorkBasketUrgencyThreshold setting is overridden, the system behavior changes as follows:
Urgency Threshold: The system will search for assignments in the work queue that have an urgency higher than the specified threshold value. This means that only the assignments meeting or exceeding this urgency level will be considered for processing.
Filtering Assignments: This filtering ensures that only the most urgent tasks are prioritized and assigned, improving efficiency and focus on critical work items.
An agent is associated with the Background processing node type. Several nodes in the cluster are
configured to perform background processing. You need to create an agent that runs every day at
midnight and sends customers an email if their birthday is that day
which two options do you select to configure the agent?
Advanced Mode Configuration:
Configure the agent to run in Advanced mode to gain more control over its execution.
Periodic Pattern:
Set the agent to run with a Periodic pattern, scheduling it to execute daily at midnight.
Ensure the agent checks for customer birthdays and sends out emails accordingly.
Reference: Pega documentation on agent configuration and scheduling.
An application for the U+ Vehicle Insurance company generates insurance quotes for vehicles specified by the customer. There can be various types of vehicles processed by the application, such as cars, motorcycles, trucks, and so on. The business specifications can also differ for each vehicle type in the quote process.
Which one of the following possibilities is the best data model design for the quote case type?
For modeling different types of vehicles in an insurance quote application:
Separate Data Types:
Create a distinct data type for each vehicle type (e.g., Car, Motorcycle, Truck). This allows for specific properties and business logic for each vehicle type.
Single Vehicle Page List:
Maintain a single page list property in the quote case type to hold vehicle details.
At runtime, dynamically identify the page class for each entry in the list based on the type of vehicle being processed. This approach allows for flexibility and maintainability.
Pega Data Modeling Best Practices
Pega Case Management Guide
If an Operator ID in an application is associated with the work queues in the following figure, how will the system behave?
The system will consider the assignments with urgency value equal to and greater than the specified value in the Operator rule. Once all assignments are processed from the work queue based on the threshold urgency value, the lower urgency assignments are then considered.
Urgency Thresholds: The system checks the urgency of assignments in each work queue. Assignments with an urgency equal to or greater than the threshold are considered first.
Get from Work Queues First: With this option selected, the system prioritizes processing work queues before considering individual workbaskets.
Lower Urgency Assignments: After processing assignments meeting the urgency thresholds, the system will then consider lower urgency assignments if there are no higher urgency tasks left.
Pega Platform documentation on routing and assignment handling.
Pega Academy resources on urgency thresholds and work queue configurations.
U+ Bank wants to offer credit cards only to low-risk customers. The customers are divided into various risk segments from Good to Very Poor. The risk segmentation rules that the business provides use the Average Balance and the customer Credit Score.
As a decisioning architect, you decide to use a decision table and a decision strategy to accomplish this requirement in Pega Customer Decision HubTM.
Using the decision table, which label is returned for a customer with a credit score of 240 and an average balance 35000?
The decision table provided in the question lists rules for segmenting customers based on their credit score and average balance.
For a customer with a credit score of 240 and an average balance of 35000:
The first rule checks if the credit score is >= 400 and < 600, and average balance >= 30000. This rule doesn't apply as the credit score is 240.
The second rule checks if the credit score is >= 200 and < 400, and average balance >= 20000. This rule applies since the credit score is 240 and average balance is 35000.
According to this rule, the result is 'Fair'.
Therefore, the label returned for a customer with a credit score of 240 and an average balance of 35000 is 'Fair'.
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