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ISACA Certified Data Privacy Solutions Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which of the following is the MOST important privacy consideration when developing a contact tracing application?

A. Whether the application can be audited for compliance purposes
B. Retention period for data storage
C. The creation of a clear privacy notice
D. The proportionality of the data collected tor the intended purpose

Answer: D

Explanation:
Explanation
The proportionality of the data collected for the intended purpose is the most important privacy consideration when developing a contact tracing application. This means that the application should only collect the minimum amount of personal data necessary to achieve the specific and legitimate purpose of preventing and controlling the spread of COVID-191. The application should also ensure that the data collected are relevant, adequate, and not excessive in relation to the purpose2. The application should avoid collecting or processing any data that are not essential for the purpose, such as location data, biometric data, or health data unrelated to COVID-193. The application should also respect the data minimization principle, which requires that the data are kept for no longer than necessary for the purpose4. References:
* European Data Protection Board Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak
* Article 5(1) of the General Data Protection Regulation (GDPR)
* Article 29 Data Protection Working Party Opinion 04/2017 on the Proposed Regulation for the ePrivacy Regulation
* Article 5(1)(e) of the GDPR

NEW QUESTION # 16
Which of the following zones within a data lake requires sensitive data to be encrypted or tokenized?

A. Raw zone
B. Trusted zone
C. Temporal zone
D. Clean zone

Answer: A

Explanation:
A raw zone is a zone within a data lake that contains unprocessed or unstructured data that is ingested from various sources without any transformation or validation. A raw zone may contain sensitive data that has not been identified or classified yet, such as personal data. Therefore, sensitive data in a raw zone should be encrypted or tokenized to protect its confidentiality and integrity. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Tokenization is a process of replacing sensitive data with non-sensitive substitutes called tokens. Both encryption and tokenization help to prevent unauthorized or unlawful access, use, disclosure, or transfer of sensitive data in a raw zone. Reference: : CDPSE Review Manual (Digital Version), page 169

NEW QUESTION # 17
Which of the following is the MOST important consideration to ensure privacy when using big data analytics?

A. Transparency about the data being collected
B. Continuity with business requirements
C. Disclosure of how the data is analyzed
D. Maintenance of archived data

Answer: A

Explanation:
Reference:
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

NEW QUESTION # 18
What is the BEST way for an organization to maintain the effectiveness of its privacy breach incident response plan?

A. Conduct annual data privacy tabletop exercises.
B. Require security management to validate data privacy security practices.
C. Involve the privacy office in an organizational review of the incident response plan.
D. Hire a third party to perform a review of data privacy processes.

Answer: A

Explanation:
Explanation
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

NEW QUESTION # 19
Which of the following is the GREATEST privacy risk associated with the use of application programming interfaces (APIs)?

A. API keys could be stored insecurely.
B. APIs are costly to assess and monitor.
C. APIS could create an unstable environment
D. APIs are complex to build and test

Answer: A

Explanation:
API keys are codes that are used to identify and authenticate an application or user when accessing an API. API keys could be stored insecurely, such as in plain text, in public repositories, or in unencrypted files. This could expose the API keys to unauthorized access, theft, or misuse by malicious actors, who could then access the API and the data it contains. This could result in data breaches, privacy violations, fraud, or other damages.
Reference:
ISACA Certified Data Privacy Solutions Engineer Study Guide, Domain 3: Privacy Engineering, Task 3.4: Implement privacy engineering techniques to protect data in applications and systems, p. 106-107.
What Is an API Key? | API Key Definition | Fortinet

NEW QUESTION # 20
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