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[Hardware] Reliable ISACA CDPSE Exam Tips & Valid CDPSE Study Notes

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【Hardware】 Reliable ISACA CDPSE Exam Tips & Valid CDPSE Study Notes

Posted at before yesterday 10:50      View：46 | Replies：1        Print      Only Author   [Copy Link] 1#
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ISACA Certified Data Privacy Solutions Engineer Sample Questions (Q122-Q127):NEW QUESTION # 122
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?
  • A. System use requirements
  • B. Lawfulness and fairness
  • C. Data use limitation
  • D. Data integrity and confidentiality
Answer: B
Explanation:
Lawfulness and fairness is a data protection principle that states that personal data should be processed in a lawful, fair, and transparent manner in relation to the data subject. This means that personal data should be collected and used for legitimate purposes that are specified and communicated to the data subject, and that respect the rights and interests of the data subject. By posting 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, an online business is applying the lawfulness and fairness principle. The online business is informing the customers about the purpose and scope of data collection, and obtaining their consent or legal basis for processing their personal data. Reference: : CDPSE Review Manual (Digital Version), page 2

NEW QUESTION # 123
Which types of controls need to be applied to ensure accuracy at all stages of processing, storage, and deletion throughout the data life cycle?
  • A. Integrity controls
  • B. Time-based controls
  • C. Processing flow controls
  • D. Purpose limitation controls
Answer: A
Explanation:
Reference:
Integrity controls are the types of controls that ensure accuracy at all stages of processing, storage, and deletion throughout the data life cycle. They include methods such as validation, verification, checksums, encryption, hashing, digital signatures, and audit trails. Processing flow controls, time-based controls, and purpose limitation controls are important for privacy, but they do not directly address accuracy. Reference: CDPSE Exam Content Outline, Domain 3, Task 3.1

NEW QUESTION # 124
What should be the PRIMARY consideration of a multinational organization deploying a user and entity behavior analytics (UEBA) tool to centralize the monitoring of anomalous employee behavior?
  • A. Cross-border data transfer
  • B. User notification
  • C. Global public interest
  • D. Support staff availability and skill set
Answer: D

NEW QUESTION # 125
Which of the following provides the BEST assurance that a potential vendor is able to comply with privacy regulations and the organization's data privacy policy?
  • A. Including mandatory compliance language in the request for proposal (RFP)
  • B. Obtaining self-attestations from all candidate vendors
  • C. Conducting a risk assessment of all candidate vendors
  • D. Requiring candidate vendors to provide documentation of privacy processes
Answer: C
Explanation:
Explanation
Conducting a risk assessment of all candidate vendors is the best way to provide assurance that a potential vendor is able to comply with privacy regulations and the organization's data privacy policy, because it allows the organization to evaluate the vendor's privacy practices, controls, and performance against a set of criteria and standards. A risk assessment can also help to identify any gaps, weaknesses, or threats that may pose a risk to the organization's data privacy objectives and obligations. A risk assessment can be based on various sources of information, such as self-attestations, documentation, audits, or independent verification. A risk assessment can also help to prioritize the vendors based on their level of risk and impact, and to determine the appropriate mitigation or monitoring actions.
References:
* 8 Steps to Manage Vendor Data Privacy Compliance, DocuSign
* Supplier Security and Privacy Assurance (SSPA) program, Microsoft Learn

NEW QUESTION # 126
Which of the following is an example of data anonymization as a means to protect personal data when sharing a database?
  • A. Key fields are hidden and unmasking is required to access to the data.
  • B. The data is transformed such that re-identification is impossible.
  • C. Names and addresses are removed but the rest of the data is left untouched.
  • D. The data is encrypted and a key is required to re-identify the data.
Answer: B
Explanation:
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.
References:
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 # 127
......
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