CDPSE Isaca Certified Data Privacy Solutions Engineer Free Practice Exam Questions (2025 Updated)

The best way to address the concern that data collected by a third-party vendor and provided back to the organization may not be protected according to the organization’s privacy notice is to validate contract compliance. This means that the organization should verify that the third-party vendor is adhering to the terms and conditions of the contract, which should include clauses on data protection, privacy, and security. The contract should also specify the obligations and responsibilities of both parties regarding data collection, processing, storage, transfer, retention, and disposal. By validating contract compliance, the organization can ensure that the third-party vendor is following the same privacy standards and practices as the organization.

References:

ISACA, CDPSE Review Manual 2021, Chapter 2: Privacy Governance, Section 2.3: Third-Party Management, p. 51-52.

ISACA, Data Privacy Audit/Assurance Program, Control Objective 8: Third-Party Management, p. 14-151

The data protection principle that is applied when an online business posts its customer data protection notice that includes a statement indicating information is collected on how products are used, the content viewed, and the time and duration of online activities is lawfulness and fairness. Lawfulness and fairness are two of the core principles of data protection under various laws and regulations, such as the GDPR or the CCPA. They state that personal data should be processed lawfully, fairly and in a transparent manner in relation to the data subject. By posting a customer data protection notice that informs customers about what information is collected and for what purpose, the online business demonstrates its compliance with these principles.

System use requirements, data integrity and confidentiality, or data use limitation are not the correct names of the data protection principles that are applied in this case. System use requirements are not a specific principle of data protection, but rather a general term that refers to the rules or policies that govern how users can access and use a system or service. Data integrity and confidentiality are two aspects of the security principle of data protection, which states that personal data should be processed in a manner that ensures appropriate security of the personal data. Data use limitation is not a specific principle of data protection either, but rather a concept that relates to the purpose limitation principle, which states that personal data should be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes.

References: A guide to the data protection principles | ICO, Data Protection Principles: Core Principles of the GDPR - Cloudian, Data Protection Basics: The 7 data protection principles

The applicable privacy legislation needs to be identified first to define the privacy requirements to use when assessing the selection of IT systems, because it sets the legal obligations and standards for the organization to comply with when processing personal data. The type of data, the control frameworks, and the technology platforms are all dependent on the privacy legislation that applies to the organization and its data processing activities. Therefore, the privacy legislation is the primary source of privacy requirements for IT systems.

References:

CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.1.2: Privacy Requirements, p. 75

Compliance with Cybersecurity and Privacy Laws and Regulations1

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.

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.

References: CDPSE Review Manual, 2021, p. 125

The primary reason that a single cryptographic key should be used for only one purpose, such as encryption or authentication, is that it minimizes the risk if the cryptographic key is compromised. A cryptographic key is a piece of information that is used to perform cryptographic operations, such as encryption or authentication. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Authentication is a process of verifying the identity or integrity of a user or data using a secret key or algorithm. If a single cryptographic key is used for multiple purposes, such as encryption and authentication, it increases the risk if the cryptographic key is compromised. For example, if an attacker obtains the cryptographic key that is used for both encryption and authentication, they can decrypt and access personal data, as well as impersonate or modify legitimate users or data. Therefore, a single cryptographic key should be used for only one purpose, and different keys should be used for different purposes. References: : CDPSE Review Manual (Digital Version), page 107

The primary reason for an organization to use hash functions when hardening application systems involved in biometric data processing is to reduce the risk of sensitive data breaches, because hash functions are one-way mathematical functions that transform biometric data into a unique and irreversible representation that cannot be reconstructed or reversed. This means that even if an attacker gains access to the hashed biometric data, they cannot use it to identify or impersonate the individual. Hash functions also help preserve the privacy and confidentiality of biometric data by preventing unauthorized access, modification, or disclosure.

References:

CDPSE Exam Content Outline, Domain 2 – Privacy Architecture (Privacy Architecture Implementation), Task 2: Implement privacy solutions1.

CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation2.

CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.4 – Remote Access3.

The most useful information for prioritizing database selection for encryption is the asset classification scheme. An asset classification scheme is a system of organizing and categorizing assets based on their value, sensitivity, criticality, or risk level. An asset classification scheme helps to determine the appropriate level of protection or handling for each asset. For example, an asset classification scheme may assign labels such as public, internal, confidential, or secret to different types of data based on their impact if compromised. Databases that contain higher-classified data should be prioritized for encryption to prevent unauthorized access, disclosure, or modification.

Database administration audit logs, historical security incidents, or penetration test results are also useful information for database security, but they are not the most useful for prioritizing database selection for encryption. Database administration audit logs are records of activities performed by database administrators or other privileged users on the database system. Database administration audit logs help to monitor and verify the actions and changes made by authorized users and detect any anomalies or violations. Historical security incidents are records of events that have compromised or threatened the security of the database system in the past. Historical security incidents help to identify and analyze the root causes, impacts, and lessons learned from previous breaches or attacks. Penetration test results are reports of simulated attacks performed by ethical hackers or security experts on the database system to evaluate its vulnerabilities and defenses. Penetration test results help to discover and exploit any weaknesses or gaps in the database security posture and recommend remediation actions.

References: Data Classification Policy - SANS Institute, Database Security Best Practices - Oracle, [Database Security: An Essential Guide | IBM]

Privacy by design is an approach that embeds privacy principles and considerations into the design and development of products, services, systems, and processes that involve personal data. Privacy by design aims to protect the privacy and security of the data subjects, as well as to comply with the applicable privacy laws and regulations. One of the key principles of privacy by design is to obtain the consent and choice of the data subjects regarding the collection, use, and disclosure of their personal data. Therefore, the best example of privacy by design in the development of a consumer mobile application is to require consent before sharing locations, as this gives the data subjects control and transparency over their personal data. The other options are not as effective or sufficient as requiring consent before sharing locations, as they do not address the principle of consent and choice, or they may violate other privacy principles or requirements.

References: CDPSE Review Manual, 2021, p. 35

An audit log is a record of the activities and events that occur in an information system, such as an application hosting personal data. An audit log can help to monitor, detect, investigate and prevent unauthorized or malicious access, use, modification or deletion of personal data. An audit log can also help to demonstrate compliance with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). An audit log should capture the following information for each event: 9

The date and time of the event

The identity of the user or system that performed the event

The type and description of the event

The outcome or result of the event

The personal data that were accessed, used, modified or deleted

The last user who accessed personal data is the most important information to capture in the audit log, as it can help to identify who is responsible for any data breach or misuse of personal data. It can also help to verify that only authorized and legitimate users have access to personal data, and that they follow the data use policy and the principle of least privilege. The last user who accessed personal data can also help to support data subjects’ rights, such as the right to access, rectify, erase or restrict their personal data.

The other options are less important or irrelevant to capture in the audit log of an application hosting personal data. Server details of the hosting environment are not related to personal data, and they can be obtained from other sources, such as network logs or configuration files. Last logins of privileged users are important to capture in a separate audit log for user account management, but they do not indicate what personal data were accessed or used by those users. Application error events are important to capture in a separate audit log for system performance and reliability, but they do not indicate what personal data were affected by those errors.

References:

IS Audit Basics: Auditing Data Privacy, section 4: “Audit logs should be maintained for all systems that process PII.”

Data Protection Audit Manual, section 3.2: “Audit trails should be kept for all processing operations involving personal data.”

Audit Logging Best Practices, section 2: “An audit log entry should contain enough information to answer who did what and when.”

The best answer is D. Assess the organization’s exposure related to the migration.

A comprehensive explanation is:

Before an organization migrates data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction, it should first assess its exposure related to the migration. This means that the organization should identify and evaluate the potential risks and benefits of moving its data to the cloud, taking into account the legal, regulatory, contractual, and ethical obligations and implications of doing so.

Some of the factors that the organization should consider in its assessment are:

The nature, sensitivity, and value of the data being migrated, and the impact of its loss, theft, corruption, or disclosure on the organization and its stakeholders.

The security, privacy, and compliance requirements and standards that apply to the data in each jurisdiction where it is stored, processed, or accessed, and the differences or conflicts among them.

The trustworthiness, reliability, and reputation of the cloud service provider and its subcontractors, and the terms and conditions of their service level agreements (SLAs) and contracts.

The availability, performance, scalability, and cost-effectiveness of the cloud-hosted solution compared to the on-premise solution, and the trade-offs involved.

The technical feasibility and complexity of migrating the data from the on-premise solution to the cloud-hosted solution, and the tools and methods needed to do so.

The organizational readiness and capability to manage the change and transition from the on-premise solution to the cloud-hosted solution, and the training and support needed for the staff and users.

By conducting a thorough assessment of its exposure related to the migration, the organization can make an informed decision about whether to proceed with the migration or not, or under what conditions or modifications. The assessment can also help the organization to plan and implement appropriate measures and controls to mitigate or avoid any negative consequences and enhance or maximize any positive outcomes of the migration.

Ensuring data loss prevention (DLP) alerts are turned on (A), encrypting the data while it is being migrated (B), and conducting a penetration test of the hosted solution © are all good practices to protect data privacy and security when migrating data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction. However they are not the first steps that should be done before the migration. They are more relevant during or after the migration process. They also do not address other aspects of exposure related to the migration, such as legal, regulatory, contractual, or ethical issues.

References:

Data Migration: On-Premise to Cloud - 10 Steps to Success1

8 Best Practices for On-Premises to Cloud Migration2

5 Steps for a Successful On-Premise to Cloud Migration3

Extend on-premises data solutions to the cloud4

On Premise to Cloud migration tool5

System hardening is a process of applying security measures and configurations to a system to reduce its attack surface and enhance its resistance to threats. System hardening can include disabling unnecessary services, removing default accounts, applying patches and updates, enforcing strong passwords and encryption, and implementing firewalls and antivirus software. The primary benefit of system hardening is that it increases system resiliency, which is the ability of a system to withstand or recover from adverse events that could affect its functionality or performance. The other options are not the primary benefits of system hardening, although they may be secondary benefits or outcomes. System hardening does not necessarily reduce external threats to data, as threats can originate from various sources and vectors. System hardening may reduce exposure of data, but only if the data is stored or processed by the system. System hardening does not eliminate attack motivation for data, as attackers may have different motives and incentives for targeting data. , p. 91-92 References: : CDPSE Review Manual (Digital Version)

The most important information to capture in the audit log of an application hosting personal data is the last user who accessed personal data. This is because the audit log is a record of the activities and events that occur within the application, such as user actions, system events, errors, or exceptions. The audit log helps to monitor and verify the compliance, security, and performance of the application, as well as to detect and investigate any incidents or anomalies. Capturing the last user who accessed personal data in the audit log helps to ensure the accountability and traceability of the data access, as well as to identify and prevent any unauthorized or inappropriate use, disclosure, or modification of personal data.

References: CDPSE Review Manual, 2021, p. 147

Requirements definition is a phase of the software development life cycle (SDLC) that involves gathering, analyzing and documenting the functional and non-functional requirements of the software system or application, such as features, performance, security and usability. It is most important to consider privacy by design principles during this phase, as it would help to ensure that privacy is embedded and integrated into the software system or application from the outset, rather than as an afterthought or an add-on. Considering privacy by design principles during requirements definition would also help to avoid costly rework or delays later in the SDLC, as well as to enhance customer trust and satisfaction, and comply with privacy laws and regulations. The other options are not as important as requirements definition in considering privacy by design principles. Application design is a phase of the SDLC that involves creating and specifying the architecture, components, interfaces and data models of the software system or application, based on the requirements defined in the previous phase. Implementation is a phase of the SDLC that involves coding, testing and debugging the software system or application, based on the design specifications created in the previous phase. Testing is a phase of the SDLC that involves verifying and validating that the software system or application meets the requirements and expectations of the users and stakeholders, as well as identifying and fixing any defects or errors1, p. 88-89 References: 1: CDPSE Review Manual (Digital Version)

User consent to share personal data is the most important factor when designing APIs that enable mobile device applications to access personal data, as it ensures that the user is informed and agrees to the purpose, scope, and duration of the data sharing. User consent also helps to comply with the data protection principles and regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), that require user consent for certain types of data processing and sharing134. References: 1 Domain 2, Task 7

Sectoral is a privacy protection reference model that refers to a system of laws and regulations that apply to specific sectors or industries within a jurisdiction, such as health, finance, education or telecommunications. Sectoral privacy protection is typically characterized by having different rules and standards for different types of personal data or data processing activities, depending on the sensitivity and value of the data or the impact and risk of the processing. When a government’s health division established the complete privacy regulation for only the health market, it is using a sectoral privacy protection reference model, as it is addressing the specific needs and challenges of the health sector in terms of privacy protection. The other options are not applicable in this scenario. Co-regulatory is a privacy protection reference model that refers to a system of laws and regulations that are supplemented by self-regulation mechanisms, such as codes of conduct, standards or certification schemes, developed by industry associations or professional bodies with oversight from government agencies or regulators. Comprehensive is a privacy protection reference model that refers to a system of laws and regulations that apply to all sectors and industries within a jurisdiction, regardless of the type or nature of personal data or data processing activities. Self-regulatory is a privacy protection reference model that refers to a system of laws and regulations that rely on voluntary compliance by organizations with their own policies and procedures, without any external oversight or enforcement from government agencies or regulators1, p. 63-64 References: 1: CDPSE Review Manual (Digital Version)