Question 1

[Security Design]
An application has a web page where users can upload and view images. The application uses a
Content Security Policy (CSP) to prevent cross-site scripting attacks by restricting the sources of
scripts and images that the browser can load. The CSP has the following directives: default-src 'self';
script-src 'self'https://cdn.example.com; img-src 'self' data: blob:. What happens if a user tries to
upload and view an image fromhttps://malicious.comon the web page?

Accepted Answer

A

Explanation: The configured Content Security Policy (CSP) includes the directive img-src 'self' data: blob:. This directive instructs the browser to only load images from the application's own origin ('self'), as data: URIs, or as blob: URIs. The attempt to load an image from an external domain, https://malicious.com, violates this policy. A compliant browser enforces the CSP by blocking the request to the unauthorized domain. As a result, the image will fail to load, and the browser will typically log a security violation error in its developer console.

Question 2

[Reporting Design]
In a retail inventory management application, the system captures and stores product sales data in
the backend database to manage inventory levels effectively. The organization requires a
comprehensive report to analyze product sales trends over the past 5 years, including details on
which products sold the most and least, and their corresponding profit margins. As a Lead System
Architect, which well-designed solution do you suggest for implementing this requirement?

Accepted Answer

C

Explanation: This solution represents the most robust and scalable architectural pattern for handling large-scale historical reporting in Pega. Storing all application-related data, including product master data and transactional sales data, within the standard PegaDATA schema is the correct approach. For complex, long-term trend analysis spanning five years, using Business Intelligence Exchange (BIX) to extract data to a dedicated data warehouse is the Pega-recommended best practice. This strategy offloads resource-intensive analytical queries from the live transactional database, preventing performance degradation of the core application while enabling powerful analysis with specialized BI tools.

Question 3

[Data Modeling]
As an LSA developing a Pega application for an online grocery store, you are tasked with enabling
customers to navigate through various categories such as "Dairy," "Confectionery," "Frozen Food,"
and "Soft Drinks." Each category contains at least 10 sub-categories, with the workflow varying
depending on the selected sub-category. What is the best method of populating the categories and
sub-categories and retrieving the related information from the grocery store's database?

Accepted Answer

C

Explanation: This scenario describes a classic cascading or dependent data relationship. The best practice in Pega is to use separate, dedicated data pages for each distinct data entity. A list-structure data page should be created to populate the top-level Categories. A second, parameterized list-structure data page should be created for Sub-categories. This Sub-category data page accepts the selected Category ID as a parameter to filter and retrieve only the relevant sub-categories from the database. This approach is modular, reusable, and aligns with Pega's data virtualization principles for optimal performance and maintainability.

Question 4

[Data Modeling]
In a hospital's patient management Pega application, patient details are gathered during the initial
consultation process. This information must be accessible and current for all subsequent
appointments and treatments. Keeping patient information updated is crucial to effective planning
and implementation of treatment. Which one of the following options would you select as a
solution?

Accepted Answer

D

Explanation: The scenario requires patient data to be centrally managed, consistently up-to-date, and accessible by multiple, independent case types (appointments, treatments). The System of Record (SOR) data access pattern is the correct Pega design for this requirement. This pattern, often implemented using data pages, ensures that all processes reference a single, authoritative source for patient data. By fetching data on-demand from the SOR, the application guarantees that the information displayed and used in any process is always the most current version, avoiding the data staleness and integrity issues associated with copying data.

Question 5

[Deployment and Testing]
You are a Pega developer working on a large project. You must implement custom guardrail warnings
to ensure that the team follows certain coding standards. Which two of the following statements
about custom guardrail warnings are true? (Choose Two)

Accepted Answer

A, B

Explanation: Custom guardrail warnings are a feature in Pega Platform designed to extend the built-in application quality checks. Their primary purpose is to help development teams enforce project-specific or organization-specific coding standards that are not covered by the default guardrails (B). The implementation of a custom guardrail involves creating an activity with custom logic that checks for a specific issue. This allows the warning to be triggered based on precise, developer-defined conditions (A). These two aspects—enforcing custom standards via custom conditions—are the core functional characteristics of the feature.

Question 6

[Work Delegation and Asynchronous Processing]
Hospital XYZ wants to analyze patient data and identify those at high risk of readmission. The
hospital has patient data stored in an Electronic Health Record (EHR) system, and a separate system
for hospital admission records. Which two of the following are key advantages of using a Data Flow
over a Queue Processor for analyzing patient data in this scenario? (Choose Two)

Accepted Answer

A, B

Explanation: Data Flows are specifically engineered for complex data integration and large-scale processing tasks. A key advantage is their ability to use multiple Data Sets as sources, allowing them to natively combine and transform data from disparate systems like an EHR and an admissions database. Furthermore, Data Flows are the designated Pega tool for high-volume batch and real-time stream processing, leveraging scalable architecture to efficiently analyze large datasets, such as an entire patient population. A Queue Processor, by contrast, is designed for processing discrete, individual tasks from a single queue and is not optimized for large-scale data aggregation from multiple sources.

Question 7

[Application Design]
As a Lead System Architect, the primary objective is to adhere to the low-code application
development methodology and support citizen developers. Relevant records are a feature in App
Studio that can help facilitate this process. Which two of the following options outline the benefits of
relevant records? (Choose Two)

Accepted Answer

C, D

Explanation: The Library in App Studio serves as a centralized repository for reusable assets, significantly accelerating application development. Its primary advantages are enabling developers to discover and add pre-built components (such as UI templates, data objects, and utilities) to their current application, thereby quickly enabling new features. Strategically, it promotes an enterprise-wide reuse strategy by making assets from all built-on applications available, ensuring consistency, reducing redundant work, and optimizing the overall development process across the Pega ecosystem.

Question 8

[Performance Optimization]
An online streaming service faces performance issues because of a high volume of user data stored
in the database. As a Lead System Architect, what are the two best corrective actions that you
suggest for enhancing application performance? (Choose Two)

Accepted Answer

B, D

Explanation: The most effective corrective actions an LSA can recommend to address performance degradation from high data volume are application-centric. Archiving and purging resolved cases (B) directly reduces the size of the active dataset in transactional tables, which significantly improves query performance and processing times. This is a standard Pega best practice. Concurrently, ensuring critical tables are properly indexed (D) is essential. As data volume grows, unindexed or poorly indexed queries become a primary bottleneck. An LSA uses tools like the Performance Analyzer (PAL) to identify slow queries and creates database indexes on properties to optimize data retrieval for reports, UI displays, and background processing.

Question 9

[Application Design]
In the context of software system design and architecture, which two of the following statements
accurately describe the Center-out business architecture approach used in Pega applications?
(Choose Two)

Accepted Answer

A, C

Explanation: The Center-out business architecture is a Pega-specific model designed to optimize business processes by focusing on customer outcomes. It starts with the core of the business—the customer Microjourney—and works outward to the channels of engagement and the enterprise systems of record. This approach avoids the common pitfalls of traditional top-down (channel-specific) and bottom-up (system-specific) designs. By centralizing business logic around the customer journey, it ensures consistency across all channels, accelerates the delivery of business value, and creates a more agile and sustainable architecture.

Question 10

[Data Modeling] Dynamic class references (DCR) are one approach to creating a data model in a Pega implementation. Which one of the following best describes the DCR approach?

Accepted Answer

A

Explanation: Dynamic Class Referencing (DCR) is a Pega Platform feature that enhances data model flexibility by deferring the specification of a page's class until runtime. Instead of hard-coding a concrete class at design time, a developer can configure a page property to resolve its class dynamically based on the value of another property, a data transform, or other conditional logic. This allows a single set of rules to operate on different types of data objects, which is essential for building reusable framework components that can be adapted to various implementation-specific data structures without modification.

Question 11

[Application Design]
As a Lead System Architect, the primary objective is to adhere to the low-code application
development methodology and support citizen developers. Relevant records are a feature in App
Studio that can help facilitate this process. Which two of the following options outline the benefits of
relevant records? (Choose Two)

Accepted Answer

B, C

Explanation: Relevant records are a design-time feature specifically created to enhance the low-code experience in App Studio. Their primary purpose is to allow Lead System Architects (LSAs) to curate a list of pertinent rules for citizen developers. This curation has two main benefits. First, it filters the options presented in App Studio, preventing developers from being overwhelmed by irrelevant technical rules and reducing search time (Benefit B). Second, it enables LSAs to deliberately "promote" specific, pre-approved rules from built-on or framework applications for reuse in the current application layer, ensuring consistency and leveraging enterprise assets (Benefit C).

Question 12

[Application Design]
Assume that two customers are simultaneously attempting to book tickets for the same flight, which
has limited seat availability. How does the design pattern for limited availability and concurrency
handle this scenario? (Choose Two)

Accepted Answer

C, D

Explanation: The Pega design pattern for handling limited availability and concurrency, such as booking a flight with few seats, relies on a case locking strategy. The fundamental principle is first-come-first-serve (D), where the first user to successfully acquire a lock and commit the transaction secures the resource. In a common implementation using optimistic locking, both users can initiate the booking process. However, when the first user confirms their booking, the system updates the flight's seat count. When the second user attempts to commit, the system detects the data has changed (no seats left) and notifies them that the booking cannot be completed, as described in (C).

Question 13

[Security Design] What are the primary uses of the Rule Security Analyzer in Pega Platform? (Choose Two)

Accepted Answer

C, D

Explanation: The Rule Security Analyzer (RSA) is a static analysis tool within the Pega Platform designed specifically to enhance application security. Its primary function is to scan an application's rules to proactively identify potential security risks. It achieves this by checking rules against a predefined set of security policies to find potential vulnerabilities, such as cross-site scripting (XSS) risks in custom code or unauthenticated access to activities. The tool flags rules that are not compliant with Pega's security guidelines, allowing developers to remediate issues before deployment.

Question 14

[Performance Optimization] A company is implementing field-level auditing in their application. What two scenarios can affect the field-level auditing feature in an application? (Choose Two)

Accepted Answer

A, D

Explanation: Field-level auditing can introduce performance overhead. The two primary factors affecting its performance are the scope and frequency of the audit triggers. Auditing a large number of fields (scope) increases the processing required for every case save operation, as the system must check each audited field for changes. Auditing fields that are modified frequently (frequency) results in a high volume of write operations to the history tables in the database, increasing I/O load and database storage consumption. Both scenarios can significantly degrade application performance if not managed carefully.

Question 15

[Work Delegation and Asynchronous Processing] What is the main difference between a Data Flow and a Queue Processor?

Accepted Answer

A

Explanation: The main difference lies in their primary processing model. A Queue Processor is designed to handle discrete, individual items asynchronously. It can be configured for dedicated processing, which allows a single queued item to be processed almost immediately after being queued. This is ideal for offloading single, distinct tasks from user-facing threads. In contrast, a Data Flow is architected to process large volumes or streams of data in a set-based manner, either in batch or real-time. It is not designed for the immediate, ad-hoc processing of a single, isolated item in the same way a Queue Processor is.

Free Pegasystems PEGACPLSA23V1 Actual Exam Questions