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

A privacy audit is a systematic and independent examination of an organization’s privacy policies, procedures, practices, and controls to assess their compliance with applicable laws, regulations, standards, and best practices. A privacy audit may result in various outputs, such as findings, recommendations, observations, or opinions. Among the options given, the output that is most likely to trigger remedial action is the identification of deficiencies in how personal data is shared with third parties. This is because such deficiencies may pose significant risks to the privacy and security of the data subjects, as well as to the reputation and legal liability of the organization. Remedial action may include implementing contractual safeguards, technical measures, or organizational changes to ensure that third parties respect and protect the personal data they receive from the organization.

References: CDPSE Review Manual, 2021, p. 181

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.

References:

What Is Your Privacy and Data Protection Strategy? - ISACA

Why data privacy and third-party risk teams need to work together - OneTrust

The best approach for a local office of a global organization faced with multiple privacy-related compliance requirements is to focus on the requirements with the highest organizational impact, because this will help prioritize the most critical and urgent privacy issues and risks that may affect the organization’s reputation, operations, or legal obligations. Focusing on the highest impact requirements will also help allocate the resources and efforts more efficiently and effectively, as well as align the local office’s privacy practices with the global organization’s objectives and strategies12.

References:

CDPSE Exam Content Outline, Domain 1 – Privacy Governance (Governance, Management & Risk Management), Task 3: Participate in the evaluation of privacy policies, programs and policies for their alignment with legal requirements, regulatory requirements and/or industry best practices3.

CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy4.

The best way for an organization to maintain the effectiveness of its privacy breach incident response plan is to conduct annual data privacy tabletop exercises. A data privacy tabletop exercise is a simulated scenario that tests the organization’s ability to respond to a privacy breach incident, such as a data breach, leak, or misuse. A data privacy tabletop exercise involves key stakeholders, such as the privacy office, the information security team, the legal counsel, the public relations team, etc., who role-play their actions and decisions based on the scenario. A data privacy tabletop exercise helps to evaluate and improve the organization’s privacy breach incident response plan, such as identifying gaps or weaknesses, validating roles and responsibilities, verifying procedures and protocols, assessing communication and coordination, etc. References: : CDPSE Review Manual (Digital Version), page 83

Public key infrastructure (PKI) is a system that enables the use of public key cryptography, which is a method of encrypting and authenticating data using a pair of keys: a public key and a private key. Public key cryptography can protect against man-in-the-middle (MITM) attacks, which are attacks where an attacker intercepts and modifies the communication between two parties. PKI makes public key cryptography feasible by providing a way to generate, distribute, verify, and revoke public keys. PKI also uses digital certificates, which are documents that bind a public key to an identity, and certificate authorities, which are trusted entities that issue and validate certificates. By using PKI, the parties can ensure that they are communicating with the intended recipient and that the data has not been tampered with by an attacker.

References:

What is Public Key Infrastructure (PKI)? - Fortinet

How is man-in-the-middle attack prevented in TLS? [duplicate]

A brief look at Man-in-the-Middle Attacks and the Role of Public Key Infrastructure (PKI)

Beacons use Bluetooth low-energy (BLE) wireless technology to transmit information to nearby devices that have Bluetooth enabled. By disabling Bluetooth services on the mobile device, the user can prevent beacons from detecting and tracking their location and sending them unwanted messages or advertisements. This can help protect the user’s privacy and avoid potential security risks from malicious beacons. Disabling location services, enabling Trojan scanners, or enabling antivirus for mobile devices are not effective ways to address threats to mobile device privacy when using beacons as a tracking technology, because they do not prevent the communication between beacons and the mobile device.

References:

Beacon Technology: What It Is and How It Impacts You1

What Does It All Mean: Beacon Technology, GPS and Geofencing2

A mobile application implementation should meet the organization’s data security standards by ensuring that the application does not contain any vulnerabilities, errors or malicious code that could compromise the confidentiality, integrity or availability of the data. An automatic dynamic code scan is a technique that analyzes the application code while it is running to detect and report any security issues or defects. An automatic dynamic code scan can help to identify and fix any potential data security risks before the application is deployed. The other options are not sufficient to ensure data security standards. UAT is a process of verifying that the application meets the user requirements and expectations, but it does not necessarily test for data security. Data classification is a process of categorizing data according to its sensitivity and value, but it does not ensure that the data is protected by the application. A PIA is a process of identifying and evaluating the privacy impacts of a system or project that involves personal data, but it does not ensure that the system or project meets data security standards. , p. 89-90 References: : CDPSE Review Manual (Digital Version)

In GDPR parlance, organizations that use third-party service providers are often, but not always, considered data controllers, which are entities that determine the purposes and means of the processing of personal data, which can include directing third parties to process personal data on their behalf. The third parties that process data for data controllers are known as data processors.

The best way to protect personal data in the custody of a third party is to include requirements to comply with the organization’s privacy policies in the contract. This means that the organization should specify the terms and conditions of data processing, such as the purpose, scope, duration, and security measures, and ensure that they are consistent with the organization’s privacy policies and applicable privacy regulations. The contract should also define the roles and responsibilities of both parties, such as data controller and data processor, and establish mechanisms for monitoring, reporting, auditing, and resolving any issues or incidents related to data privacy. References: : CDPSE Review Manual (Digital Version), page 41

Inferred data is the type of data that is produced by using a more complex method of analytics to find correlations between data sets and using them to categorize or profile people. Inferred data is not directly observed or collected from the data subjects, but rather derived from other sources of data, such as behavioral, transactional, or demographic data. Inferred data can be used to make assumptions or predictions about the data subjects’ preferences, interests, behaviors, or characteristics12.

References:

CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.1 – Data Classification3.

CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3 – Data Lifecycle, Section 3.2 – Data Classification4.

A bug bounty program is an assurance approach that involves offering rewards to external security researchers who find and report vulnerabilities in an API or other software. A bug bounty program can be more effective than other assurance approaches in identifying API vulnerabilities because it leverages the skills, creativity, and diversity of a large pool of ethical hackers who can test the API from different perspectives and scenarios. A bug bounty program can also incentivize continuous testing and reporting of vulnerabilities, which can help improve the security posture of the API over time.

References:

10 top API security testing tools, CSO Online

Bug Bounty Programs: What You Need to Know, ISACA Journal

The best course of action to prevent false positives from data loss prevention (DLP) tools is to re-establish baselines for configuration rules. False positives are events that are triggered by a DLP policy in error, meaning that the policy has mistakenly identified non-sensitive data as sensitive or blocked legitimate actions. False positives can reduce the effectiveness and efficiency of DLP tools by generating unnecessary alerts, wasting resources, disrupting workflows, and creating user frustration. To avoid false positives, DLP tools need to have accurate and updated configuration rules that define what constitutes sensitive data and what actions are allowed or prohibited. Configuration rules should be based on clear and consistent criteria, such as data classification levels, data sources, data destinations, data formats, data patterns, user roles, user behaviors, etc. Configuration rules should also be regularly reviewed and adjusted to reflect changes in business needs, regulatory requirements, or threat landscape.

Conducting additional discovery scans, suppressing the alerts generating the false positives, or evaluating new DLP tools are not the best ways to prevent false positives from DLP tools. Conducting additional discovery scans may help identify more sensitive data in the network, but it does not address the root cause of false positives, which is the misconfiguration of DLP policies. Suppressing the alerts generating the false positives may reduce the noise and annoyance caused by false positives, but it does not solve the problem of inaccurate or outdated DLP policies. Evaluating new DLP tools may offer some advantages in terms of features or performance, but it does not guarantee that false positives will be eliminated or reduced without proper configuration and tuning of DLP policies.

References: False Positives Handling| Endpoint Data Loss Prevention - ManageEngine …, Scenario-based troubleshooting guide - DLP Issues, Respond to a DLP policy violation in Power BI - Power BI

Awareness campaigns are initiatives that aim to educate and inform employees about the importance of privacy protection, the organization’s privacy policies and procedures, the applicable laws and regulations, and the best practices and behaviors to safeguard personal data. Awareness campaigns can support an organization’s efforts to create and maintain desired privacy protection practices among employees by raising their awareness, understanding and commitment to privacy, as well as by influencing their attitudes, values and culture. Awareness campaigns can use various methods and channels, such as posters, newsletters, videos, webinars, quizzes, games or events, to deliver consistent and engaging messages to the target audience. The other options are not the best ways to support an organization’s efforts to create and maintain desired privacy protection practices among employees. Skills training programs are focused on developing specific technical or functional skills related to privacy, but they may not address the broader aspects of privacy awareness or culture. Performance evaluations are focused on measuring and rewarding individual or team performance based on predefined criteria or objectives, but they may not reflect the actual level of privacy awareness or practice. Code of conduct principles are focused on establishing and enforcing ethical standards and rules of behavior for employees, but they may not be sufficient to create or maintain privacy awareness or practice without effective communication and education1, p. 103-104 References: 1: CDPSE Review Manual (Digital Version)

A primary objective of performing a privacy impact assessment (PIA) prior to onboarding a new Software as a Service (SaaS) provider for a customer relationship management (CRM) system is to identify controls to mitigate data privacy risks, such as data breaches, unauthorized access, misuse or loss of data. A PIA would help to evaluate the potential privacy impacts of using a new SaaS provider for CRM data processing activities, such as collecting, storing, analyzing or transferring customer data, and to implement appropriate controls to mitigate those impacts, such as encryption, access control, backup, audit trail or contractual clauses. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with customer expectations and preferences. The other options are not primary objectives of performing a PIA prior to onboarding a new SaaS provider for CRM data processing activities. Classifying personal data according to the data classification scheme is an activity that may be part of a PIA process, but it is not an objective in itself. Assessing the risk associated with personal data usage is an activity that may be part of a PIA process, but it is not an objective in itself. Determining the service provider’s ability to maintain data protection controls is an activity that may be part of a PIA process, but it is not an objective in itself1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

Data retention is a process of keeping personal data for a specified period of time for legitimate purposes, such as legal obligations, contractual agreements, business operations or historical records. Data retention should be based on the principle of data minimization, which requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Data retention should also comply with the principle of storage limitation, which requires deleting or disposing of personal data when it is no longer needed or justified. The most likely valid use case for keeping a customer’s personal data after contract termination is a required retention period due to regulations, such as tax laws, financial laws, health laws or consumer protection laws, that mandate the organization to retain certain types of customer data for a certain period of time after the end of the contractual relationship. The other options are not valid use cases for keeping a customer’s personal data after contract termination, as they do not meet the criteria of necessity, relevance or justification. For the purpose of medical research, the organization would need to obtain the consent of the customer or have another legal basis for processing their personal data for a different purpose than the original contract. A forthcoming campaign to win back customers or ease of onboarding when the customer returns are not legitimate purposes for retaining customer data after contract termination, as they are not related to the original contract and may violate the customer’s privacy rights and preferences. , p. 99-100 References: : CDPSE Review Manual (Digital Version)

Senior leadership must define the roles and responsibilities of the person with oversight, who is responsible for ensuring compliance with the data privacy policy and applicable laws and regulations. This person may also be known as the data protection officer, the privacy officer, or the chief privacy officer, depending on the organization and jurisdiction. The person with oversight should have the authority, resources, and independence to perform their duties effectively.

References:

ISACA, CDPSE Review Manual 2021, Chapter 2: Privacy Governance, Section 2.1: Privacy Governance Framework, p. 35-36.

ISACA, Data Privacy Audit/Assurance Program, Control Objective 1: Data Privacy Governance, p. 4-51

The value proposition of a PIA is not understood by management is the greatest obstacle to conducting a PIA, as it may result in lack of support, funding, resources or commitment for the PIA process and outcomes. Management may not appreciate or recognize the benefits of a PIA, such as enhancing privacy protection, reducing privacy risks and costs, increasing customer trust and satisfaction, and complying with privacy laws and regulations. Management may also perceive a PIA as a burden, a delay or a hindrance to the system or project development and delivery. The other options are not as significant as the value proposition of a PIA is not understood by management as obstacles to conducting a PIA. Conducting a PIA requires significant funding and resources is an obstacle to conducting a PIA, but it may be overcome by demonstrating the return on investment or the cost-benefit analysis of a PIA. PIAs need to be performed many times in a year is an obstacle to conducting a PIA, but it may be mitigated by adopting a scalable or modular approach to PIAs that can be tailored to different types or levels of systems or projects. The organization lacks knowledge of PIA methodology is an obstacle to conducting a PIA, but it may be resolved by acquiring or developing the necessary skills, tools or guidance for performing PIAs1, p. 67-68 References: 1: CDPSE Review Manual (Digital Version)