Isaca Certified Data Privacy Solutions Engineer CDPSE Exam Dumps: Updated Questions & Answers (November 2025)

A private cloud is a cloud deployment model that provides exclusive access and control to a single organization or a specific group of users within the organization. A private cloud is best for an organization whose main objectives are to logically isolate personal data from other tenants and adopt custom privacy controls for the data, as it offers the highest level of security, privacy, and customization among the cloud deployment models. A private cloud allows the organization to implement its own privacy policies, standards, and procedures for the personal data, as well as to configure the cloud infrastructure, services, and applications according to its specific needs and preferences. A private cloud also reduces the risk of data breaches, unauthorized access, or co-mingling of data from other tenants, as the personal data is stored and processed in a dedicated and isolated environment.

Requiring independent audits of the providers’ data privacy controls is the best way to ensure third-party providers that process an organization’s personal data are addressed as part of the data privacy strategy. Independent audits can verify that the providers are complying with the applicable data privacy laws and regulations, as well as the organization’s own policies and standards. Independent audits can also identify any gaps or weaknesses in the providers’ data privacy controls and recommend corrective actions or improvements.

A thin client remote desktop protocol (RDP) is the most effective remote access model for reducing the likelihood of attacks originating from connecting devices, because it minimizes the amount of data and processing that occurs on the remote device. A thin client RDP only sends keyboard, mouse and display information between the remote device and the server, while the actual processing and storage of data happens on the server. This reduces the exposure of sensitive data and applications to potential attackers who may compromise the remote device.

Following an established privacy framework is the most important step when developing an organizational data privacy program because it provides a structured and consistent approach to identify, assess, and manage privacy risks and compliance obligations. A privacy framework can also help to align the privacy program with the organization’s strategic goals, values, and culture, as well as to communicate and demonstrate the privacy program’s effectiveness to internal and external stakeholders. Some examples of established privacy frameworks are the NIST Privacy Framework, the ISO/IEC 27701:2019, and the AICPA Privacy Maturity Model.

Data masking is the process of hiding original data with modified content to protect sensitive data from unauthorized access or disclosure. Data masking is often used for testing purposes in non-production environments, where personal data is not needed or allowed. However, data masking can pose several challenges, especially for a global financial institution that has multiple interconnected systems and applications. One of the greatest challenges is to preserve the complex relationships within and across systems while masking the data. This means that the masked data must maintain the same format, referential integrity, semantic integrity, and uniqueness as the original data, so that the testing results are valid and reliable. For example, if a customer’s name is masked in one system, it must be masked consistently in all other systems that reference it. If a transaction amount is masked in one system, it must not violate any business rules or constraints in another system. If a credit card number is masked in one system, it must still be a valid credit card number in another system. Preserving these complex relationships can be challenging because it requires a thorough understanding of the data model, the business logic, and the dependencies among systems. It also requires a robust and flexible data masking tool that can handle different types of data and platforms.

Providing data subjects direct access through a profile page is the best practice because it supports transparency and control while fulfilling data subject rights (e.g., access, rectification). Limiting edits (A) or disabling modifications (D) restricts rights. Using email to generate IDs (B) is unrelated to enabling data subject access.

“Data subjects should have the ability to view and manage their own information directly.”

The best reason for a health organization to use desktop virtualization to implement stronger access control to systems containing patient records is that it can improve data integrity and reduce effort for privacy audits. Desktop virtualization is a technology that allows users to access a virtual desktop environment that is hosted on a remote server, rather than on their local device. Desktop virtualization can enhance data privacy by providing stronger access control to systems containing patient records, such as requiring authentication, authorization, encryption, logging, etc. Desktop virtualization can also improve data integrity by ensuring that patient records are stored and processed in a centralized and secure location, rather than on multiple devices that may be vulnerable to loss, theft, damage, or corruption. Desktop virtualization can also reduce effort for privacy audits by simplifying the management and monitoring of data privacy compliance across different devices and locations. References: : CDPSE Review Manual (Digital Version), page 153

Using hash values with stored personal data best enables an organization to detect changes to the data, because hash values are unique and fixed outputs that are generated from the data using a mathematical algorithm. If the data is altered in any way, even by a single bit, the hash value will change dramatically. Therefore, by comparing the current hash value of the data with the original or expected hash value, the organization can verify the integrity and authenticity of the data. If the hash values match, it means that the data has not been tampered with. If the hash values differ, it means that the data has been corrupted or modified.