Isaca Certified Data Privacy Solutions Engineer CDPSE Exam Practice Test

Answer : C

The most important consideration to ensure privacy when using big data analytics is C. Transparency about the data being collected.

A comprehensive explanation is:

Big data analytics involves the processing of large and complex data sets to extract valuable insights and patterns that can support decision making, innovation, and optimization. However, big data analytics also poses significant challenges and risks for the privacy of individuals and groups whose data is collected, stored, analyzed, and shared. Therefore, it is essential to adopt appropriate measures and principles to protect the privacy of big data while still enabling its beneficial use.

One of the key measures and principles for ensuring privacy when using big data analytics is transparency. Transparency means that the data collectors and processors inform the data subjects (the individuals or groups whose data is involved) about what data is being collected, how it is collected, why it is collected, how it is used, who it is shared with, what are the benefits and risks, and what are the rights and choices of the data subjects. Transparency also means that the data collectors and processors are accountable for their actions and comply with the relevant laws, regulations, standards, and ethical guidelines.

Transparency is important for ensuring privacy when using big data analytics for several reasons. First, transparency respects the dignity and autonomy of the data subjects by acknowledging their interests and preferences regarding their personal data. Second, transparency fosters trust and confidence between the data subjects and the data collectors and processors by providing clear and accurate information and communication. Third, transparency enables informed consent and participation of the data subjects by giving them the opportunity to understand and agree to the data collection and use or to opt out or object if they wish. Fourth, transparency facilitates oversight and governance of the big data practices by allowing external audits, reviews, complaints, and remedies.

Some examples of how transparency can be implemented in big data analytics are:

Providing clear and concise privacy notices or policies that explain what data is being collected, how it is collected, why it is collected, how it is used, who it is shared with, what are the benefits and risks, and what are the rights and choices of the data subjects.

Obtaining explicit or implicit consent from the data subjects before collecting or using their data, or providing them with easy ways to opt out or object if they do not consent.

Implementing privacy by design and by default principles that ensure that privacy is considered and integrated throughout the entire lifecycle of big data analytics, from planning to implementation to evaluation.

Adopting privacy-enhancing technologies (PETs) that minimize or anonymize the personal data collected or used in big data analytics, or that enable secure encryption, pseudonymization, or aggregation of the data.

Establishing privacy governance frameworks that define the roles and responsibilities of the different actors involved in big data analytics, such as data owners, collectors, processors, analysts, users, regulators, auditors, etc., and that specify the rules and standards for privacy protection.

Conducting privacy impact assessments (PIAs) that identify and evaluate the potential privacy risks and benefits of big data analytics projects or initiatives, and that propose measures to mitigate or avoid the risks and enhance or maximize the benefits.

Providing mechanisms for feedback, consultation, participation, or co-creation of the data subjects in big data analytics projects or initiatives, such as surveys, focus groups, workshops, forums, etc.

Enabling access, correction, deletion, portability, or restriction of the personal data of the data subjects upon their request or demand.

Reporting on the outcomes and impacts of big data analytics projects or initiatives to the relevant stakeholders, such as the data subjects, regulators, customers, partners, society at large etc., in a transparent and accountable manner.

Maintenance of archived data (A), disclosure of how the data is analyzed (B), and continuity with business requirements (D) are also important considerations for ensuring privacy when using big data analytics. However they are not as important as transparency about the data being collected . Maintenance of archived data involves ensuring that the personal data stored in backup systems or historical records is protected from unauthorized access, modification or deletion. Disclosure of how the data is analyzed involves explaining the methods, techniques, tools, and algorithms used to process and interpret the personal data. Continuity with business requirements involves aligning the objectives, scope, and outcomes of big data analytics with the expectations, needs, and values of the organization and its stakeholders. These considerations are more related to the technical, procedural, and strategic aspects of ensuring that the personal data is processed in a secure, accurate, and relevant manner, which are necessary but not sufficient conditions for achieving the privacy protection of big data.

The Big Data World: Benefits, Threats and Ethical Challenges1

Big Data Privacy: A Technological Perspective And Review2

Big Data And Privacy What You Need To Know3

Answer : B

A multinational organization that operates across different countries and regions should perform an annual review of changes to privacy regulations in all jurisdictions where its corporate data is processed. This is because different jurisdictions may have different privacy laws and requirements that apply to the collection, use, storage, transfer, and disposal of personal dat

a. For example, the EU General Data Protection Regulation (GDPR) applies to any organization that processes personal data of individuals in the EU, regardless of where the organization is located or where the data is processed. Therefore, the organization should keep track of the changes to privacy regulations in all relevant jurisdictions and update its data privacy policy accordingly to ensure compliance and avoid penalties or lawsuits.

Answer : B

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.

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

Answer : A

Data minimization is a privacy principle that requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Data minimization should be used to address data kept beyond its intended lifespan, as it helps to reduce the amount of data that is stored or retained, and to delete or dispose of data that is no longer needed or justified. Data minimization also reduces the privacy risks and costs associated with data storage and retention, such as data breaches, unauthorized access, misuse or loss of data. The other options are not effective ways to address data kept beyond its intended lifespan. Data anonymization is a technique that removes or modifies all identifiers in a data set to prevent or limit the identification of the data subjects, but it does not address the issue of data retention or deletion. Data security is a process of applying technical, administrative and physical measures to protect the confidentiality, integrity and availability of data, but it does not address the issue of data retention or deletion.Data normalization is a technique that organizes the data in a database to reduce redundancy and improve consistency, but it does not address the issue of data retention or deletion1, p.75-76Reference:1: CDPSE Review Manual (Digital Version)

Answer : B

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.

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.

Answer : C

Including privacy requirements in vendor contracts is the best way to ensure privacy considerations are included when working with vendors because it establishes the obligations, expectations and responsibilities of both parties regarding the protection of personal dat

a. It also provides a legal basis for enforcing compliance and resolving disputes. Including privacy requirements in the request for proposal (RFP) process, monitoring privacy-related service level agreements (SLAs) and requiring vendors to complete privacy awareness training are helpful measures, but they do not guarantee that vendors will adhere to the privacy requirements or that they will be held accountable for any violations.

CDPSE Review Manual (Digital Version), Domain 1: Privacy Governance, Task 1.7: Participate in the management and evaluation of contracts, service levels and practices of vendors and other external parties1

CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2: Privacy Governance, Section: Vendor Management2

Answer : B

The principle of least privilege is the most important principle to apply when granting access to an ERP system that contains a significant amount of personal dat

a. The principle of least privilege states that users should only have the minimum level of access and permissions necessary to perform their legitimate tasks and functions, and no more. Applying the principle of least privilege helps to protect the privacy and security of the personal data in the ERP system, as it reduces the risk of unauthorized or inappropriate access, disclosure, modification, or deletion of the data. It also helps to comply with the privacy laws and regulations, such as the GDPR, that require data controllers and processors to implement appropriate technical and organizational measures to ensure a level of security appropriate to the risk.