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ISACA Certified Data Privacy Solutions Engineer Sample Questions (Q170-Q175):

NEW QUESTION # 170
What is the BEST method to protect customers' personal data that is forwarded to a central system for analysis?

Answer: C

Explanation:
Pseudonymization is a technique that replaces direct identifiers in a data set with pseudonyms or artificial identifiers that do not reveal the identity of the data subjects. Pseudonymization is the best method to protect customers' personal data that is forwarded to a central system for analysis, as it reduces the linkability of the data set with the original identity of the customers and thus enhances the privacy and security of the data. Pseudonymization also preserves some characteristics or patterns of the original data that can be used for analysis or research purposes, without compromising the accuracy or quality of the results. The other options are not as effective as pseudonymization in protecting customers' personal data that is forwarded to a central system for analysis. Deletion is a technique that removes or destroys data from a storage device or media to prevent unauthorized access or recovery of the data, but it does not allow for any analysis or research purposes. 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 reduce the linkability of the data set with the original identity of the customers and may require additional security measures to protect the encryption keys or certificates. 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 may affect the accuracy or quality of the analysis or research results, as some characteristics or patterns of the original data may be lost or distorted1, p. 74-75 Reference: 1: CDPSE Review Manual (Digital Version)


NEW QUESTION # 171
Which of the following deployed at an enterprise level will MOST effectively block malicious tracking of user Internet browsing?

Answer: A

Explanation:
Domain name system (DNS) sinkhole is a technology that redirects malicious or unwanted domain names to alternative destinations, such as a fake or harmless website, a warning page, or a null address. DNS sinkhole is the most effective technology deployed at an enterprise level to block malicious tracking of user internet browsing, as it would prevent users from accessing websites that use tracking technologies, such as cookies, web beacons, or fingerprinting, to collect and analyze user behavior or preferences. DNS sinkhole would also protect users from other malicious activities, such as malware distribution, phishing attempts, or botnet command and control. The other options are not as effective as DNS sinkhole in blocking malicious tracking of user internet browsing at an enterprise level. Web application firewall (WAF) is a technology that monitors and filters incoming and outgoing web traffic to protect web applications from attacks, such as cross-site scripting (XSS), SQL injection, or denial-of-service (DoS), but it does not block malicious tracking of user internet browsing. Website URL blacklisting is a method of blocking access to websites that are known or suspected to be malicious or harmful, but it does not block malicious tracking of user internet browsing from unknown or legitimate websites that use tracking technologies. Desktop antivirus software is a technology that scans and removes viruses, malware, spyware, or other threats from desktop computers or devices, but it does not block malicious tracking of user internet browsing from websites that use tracking technologies1, p. 92 Reference: 1: CDPSE Review Manual (Digital Version)


NEW QUESTION # 172
A data processor that handles personal data tor multiple customers has decided to migrate its data warehouse to a third-party provider. What is the processor obligated to do prior to implementation?

Answer: C

Explanation:
Explanation
A data processor that handles personal data for multiple customers has decided to migrate its data warehouse to a third-party provider. The processor is obligated to seek approval from all in-scope data controllers prior to implementation. A data controller is an entity that determines the purposes and means of processing personal data. A data processor is an entity that processes personal data on behalf of a data controller. A third-party provider is an entity that provides services or resources to another entity, such as a cloud service provider or a hosting provider.
According to various privacy laws and regulations, such as the GDPR or the CCPA, a data processor must obtain explicit consent from the data controller before engaging another processor or transferring personal data to a third country or an international organization. The consent must specify the identity of the other processor or the third country or international organization, as well as the safeguards and guarantees for the protection of personal data. The consent must also be documented in a written contract or other legal act that binds the processor to respect the same obligations as the controller.
Seeking approval from all in-scope data controllers can help ensure that the processor complies with its contractual and legal obligations, respects the rights and preferences of the data subjects, and maintains transparency and accountability for its processing activities.
Obtaining assurance that data subject requests will continue to be handled appropriately, implementing comparable industry-standard data encryption in the new data warehouse, or ensuring data retention periods are documented are also good practices for a data processor that migrates its data warehouse to a third-party provider, but they are not obligations prior to implementation. Rather, they are requirements or recommendations during or after implementation.
Obtaining assurance that data subject requests will continue to be handled appropriately is a requirement for a data processor that processes personal data on behalf of a data controller. Data subject requests are requests made by individuals to exercise their rights regarding their personal data, such as access, rectification, erasure, restriction, portability, or objection. A data processor must assist the data controller in fulfilling these requests within a reasonable time frame and without undue delay.
Implementing comparable industry-standard data encryption in the new data warehouse is a recommendation for a data processor that transfers personal data to another system or location. Data encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Data encryption can help protect the confidentiality, integrity, and availability of personal data by preventing unauthorized access, disclosure, or modification.
Ensuring data retention periods are documented is a requirement for a data processor that stores personal data on behalf of a data controller. Data retention periods are the durations for which personal data are kept before they are deleted or anonymized. Data retention periods must be determined by the purpose and necessity of processing personal data and must comply with legal and regulatory obligations.
References: Data warehouse migration tips: preparation and discovery - Google Cloud, Plan a data warehouse migration - Cloud Adoption Framework, Migrating your traditional data warehouse platform to BigQuery ...


NEW QUESTION # 173
An organization has initiated a project to enhance privacy protections by improving its information security controls. Which of the following is the MOST useful action to help define the scope of the project?

Answer: B

Explanation:
Reviewing proposed privacy rules that govern the processing of personal data is the most useful action to help define the scope of the project because it helps identify the legal and regulatory requirements, the data protection principles and the privacy objectives that the information security controls need to support. Reviewing recent audit reports, identifying databases that contain personal data or do not have encryption in place are helpful actions to assess the current state of privacy and security, but they do not provide a clear direction for the project scope.
Reference:
CDPSE Review Manual (Digital Version), Domain 2: Privacy Architecture, Task 2.1: Identify and/or define privacy requirements1 CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3: Privacy Architecture, Section: Privacy Requirements2


NEW QUESTION # 174
Which of the following is an example of data anonymization as a means to protect personal data when sharing a database?

Answer: A

Explanation:
Data anonymization is a method of protecting personal data by modifying or removing any information that can be used to identify an individual, either directly or indirectly, in a data set. Data anonymization aims to prevent the re-identification of the data subjects, even by the data controller or processor, or by using additional data sources or techniques. Data anonymization also helps to comply with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require data controllers and processors to respect the privacy rights and preferences of the data subjects.
The data is transformed such that re-identification is impossible is an example of data anonymization, as it involves applying irreversible techniques, such as aggregation, generalization, perturbation, or synthesis, to alter the original data in a way that preserves their utility and meaning, but eliminates their identifiability. For example, a database of customer transactions can be anonymized by replacing the names and addresses of the customers with random codes, and by adding noise or rounding to the amounts and dates of the transactions.
The other options are not examples of data anonymization, but of other methods of protecting personal data that do not guarantee the impossibility of re-identification. The data is encrypted and a key is required to re-identify the data is an example of data pseudonymization, which is a method of replacing direct identifiers with pseudonyms, such as codes or tokens, that can be linked back to the original data with a key or algorithm. Data pseudonymization does not prevent re-identification by authorized parties who have access to the key or algorithm, or by unauthorized parties who can break or bypass the encryption. Key fields are hidden and unmasking is required to access to the data is an example of data masking, which is a method of concealing or obscuring sensitive data elements, such as names or credit card numbers, with characters, symbols or blanks. Data masking does not prevent re-identification by authorized parties who have permission to unmask the data, or by unauthorized parties who can infer or guess the hidden data from other sources or clues. Names and addresses are removed but the rest of the data is left untouched is an example of data deletion, which is a method of removing direct identifiers from a data set. Data deletion does not prevent re-identification by using indirect identifiers, such as age, gender, occupation or location, that can be combined or matched with other data sources to re-establish the identity of the data subjects.
Reference:
Big Data Deidentification, Reidentification and Anonymization - ISACA, section 2: "Anonymization is the ability for the data controller to anonymize the data in a way that it is impossible for anyone to establish the identity of the data." Data Anonymization - Overview, Techniques, Advantages, section 1: "Data anonymization is a method of ensuring that the company understands and enforces its duty to secure sensitive, personal, and confidential data in a world of highly complex data protection mandates that can vary depending on where the business and the customers are based."


NEW QUESTION # 175
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