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ISACA Certified Data Privacy Solutions Engineer Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of the following is an IT privacy practitioner's BEST recommendation to reduce privacy risk before an organization provides personal data to a third party?

Answer: D

Explanation:
Anonymization is a technique that removes or modifies all identifiers in a data set to prevent or limit the identification of the data subjects. Anonymization is an IT privacy practitioner's best recommendation to reduce privacy risk before an organization provides personal data to a third party, as it would protect the privacy of the data subjects by reducing the linkability of the data set with their original identity, and also comply with the data minimization principle that requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Anonymization would also preserve some characteristics or patterns of the original data that can be used for analysis or research purposes by the third party, without compromising the accuracy or quality of the results. The other options are not as effective as anonymization in reducing privacy risk before an organization provides personal data to a third party. Tokenization is a technique that replaces sensitive or confidential data with non-sensitive tokens or placeholders that do not reveal the original data, but it does not prevent or limit the identification of the data subjects, as tokens can be reversed or linked back to the original data using a tokenization system or key. Aggregation is a technique that combines individual data into groups or categories that do not reveal the identity of the data subjects, but it may not prevent or limit the identification of the data subjects, as aggregated data can be de-aggregated or re-identified using other sources of information or techniques. Encryption is a technique that transforms plain text data into cipher text using an algorithm and a key, making it unreadable by unauthorized parties, but it does not prevent or limit the identification of the data subjects, as encrypted data can be decrypted or linked back to the original data using an encryption system or key1, p. 74-75 Reference: 1: CDPSE Review Manual (Digital Version)


NEW QUESTION # 15
Which of the following is the MOST effective remote access model for reducing the likelihood of attacks originating from connecting devices?

Answer: A

Explanation:
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.
Reference:
CDPSE Review Manual, Chapter 2 - Privacy Architecture, Section 2.3 - Privacy Architecture Implementation1.
CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 - Privacy Architecture, Section 2.4 - Remote Access2.


NEW QUESTION # 16
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. Which data protection principle is applied?

Answer: C


NEW QUESTION # 17
Which of the following is the BEST control to detect potential internal breaches of personal data?

Answer: C

Explanation:
Explanation
User behavior analytics tools are the best control to detect potential internal breaches of personal data because they monitor and analyze the activities and patterns of users on the network and systems, and alert or block any anomalous or suspicious behavior that may indicate unauthorized access, misuse or exfiltration of personal data. Data loss prevention (DLP) systems, employee background checks and classification of data are useful controls to prevent or mitigate internal breaches of personal data, but they do not necessarily detect them.
References:
* CDPSE Review Manual (Digital Version), Domain 2: Privacy Architecture, Task 2.4: Design and/or implement privacy controls1
* CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3: Privacy Architecture, Section: Privacy Controls2


NEW QUESTION # 18
A global financial institution is implementing data masking technology to protect personal data used for testing purposes in non-production environments. Which of the following is the GREATEST challenge in this situation?

Answer: B

Explanation:
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.


NEW QUESTION # 19
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