CDPSE exam collection: Certified Data Privacy Solutions Engineer & CDPSE torrent VCE

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The CDPSE exam covers a wide range of topics related to data privacy solutions engineering, including privacy governance, privacy architecture, data lifecycle management, privacy operations, and data protection. CDPSE exam consists of 150 multiple-choice questions that are designed to test the candidate's understanding of the key concepts and best practices in the field of data privacy solutions engineering. To be eligible for the CDPSE Certification, candidates must have at least five years of experience in privacy or data protection, with a minimum of three years of experience in data privacy solutions engineering.

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ISACA Certified Data Privacy Solutions Engineer Sample Questions (Q208-Q213):

NEW QUESTION # 208
Which of the following is the MOST important consideration when determining retention periods for personal data?

Answer: D

Explanation:
Explanation
The notice provided to customers during data collection is the most important consideration when determining retention periods for personal data, as it reflects the transparency and accountability principles of privacy and the expectations and preferences of the data subjects. The notice should inform the customers about the purposes and legal bases of the data processing, the rights and choices of the customers, and the safeguards and measures to protect the data, including how long the data will be kept and when it will be deleted or disposed of. The notice should also be consistent with the applicable laws and regulations that may prescribe or limit the retention periods for certain types of personal data. The other options are not as important as the notice provided to customers during data collection when determining retention periods for personal data.
Sectoral best practices for the industry may provide some guidance or benchmarks for retention periods, but they may not reflect the specific context or needs of the organization or the customers. Data classification standards may help to categorize data according to its sensitivity and value, but they may not indicate how long the data should be retained or deleted. Storage capacity available for retained data may affect the feasibility or cost of retaining data, but it should not determine or override the retention periods based on privacy principles, laws or customer expectations1, p. 99-100 References: 1: CDPSE Review Manual (Digital Version)


NEW QUESTION # 209
An organization is concerned with authorized individuals accessing sensitive personal customer information to use for unauthorized purposes. Which of the following technologies is the BEST choice to mitigate this risk?

Answer: A

Explanation:
Explanation
User behavior analytics is a technology that uses data analysis and machine learning to monitor, detect and respond to anomalous or malicious user activities, such as accessing sensitive personal customer information to use for unauthorized purposes. User behavior analytics is the best choice to mitigate this risk, as it would help to identify and prevent insider threats, data breaches, fraud or misuse of data by authorized individuals.
User behavior analytics can also help to enforce policies and controls, such as access control, audit trail or data loss prevention. The other options are not as effective as user behavior analytics in mitigating this risk. Email filtering system is a technology that scans and blocks incoming or outgoing emails that contain spam, malware or phishing attempts, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes. Intrusion monitoring is a technology that monitors and alerts on unauthorized or malicious attempts to access a system or network, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes. Mobile device management (MDM) is a technology that manages and secures mobile devices that are used to access or store organizational data, but it does not address the issue of authorized individuals accessing sensitive personal customer information to use for unauthorized purposes1, p. 92 References: 1:
CDPSE Review Manual (Digital Version)


NEW QUESTION # 210
Which of the following is an example of data anonymization as a means to protect personal data when sharing a database?

Answer: D

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 # 211
Which of the following privacy-enhancing technologies (PETs) is MOST effective for ensuring individual data records cannot be linked back to specific individuals in a dataset?

Answer: D

Explanation:
Differential privacy provides mathematically provable protection against re-identification and linkage, adding calibrated noise to outputs so individuals cannot be singled out or linked. Pseudonymization (C) and masking (D) reduce direct identifiers but remain vulnerable to linkage attacks. Secure multiparty computation (B) protects computation among parties, not release-time linkability.
"Differential privacy limits what can be learned about any individual from query results, resisting re-identification/linkage."


NEW QUESTION # 212
Which of the following is BEST used to validate compliance with agreed-upon service levels established with a third party that processes personal data?

Answer: C

Explanation:
Explanation
The best way to validate compliance with agreed-upon service levels established with a third party that processes personal data is to have a contractual right to audit, which means that the organization can conduct audits or inspections of the third party's privacy practices, policies, and procedures to verify that they meet the contractual obligations and expectations. A contractual right to audit can also help identify and address any privacy risks or gaps that may arise from the third party's processing of personal data12.
References:
* CDPSE Exam Content Outline, Domain 1 - Privacy Governance (Governance, Management & Risk Management), Task 7: Participate in the management and evaluation of contracts, service levels and practices of vendors and other external parties3.
* CDPSE Review Manual, Chapter 1 - Privacy Governance, Section 1.4 - Third-Party Management4.


NEW QUESTION # 213
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