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Authentic ISACA CDPSE Exam Questions with Accurate Answers

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The CDPSE exam covers various topics, including privacy governance, privacy architecture, privacy operations, and privacy technology. CDPSE exam is designed to test the individual's knowledge of privacy laws and regulations, the implementation of privacy programs, and the management of privacy risks. CDPSE Exam also evaluates the individual's ability to design and implement privacy controls and technologies to mitigate privacy risks.

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ISACA CDPSE Exam is divided into four primary domains, each of which covers a specific area of data privacy and protection. These domains include data privacy governance, data privacy operations, data privacy implementation, and data privacy assurance. Candidates who successfully pass the CDPSE exam will have demonstrated their ability to effectively manage and protect sensitive data across each of these domains.

The CDPSE certification is designed to help professionals demonstrate their knowledge and skills in various areas, including data privacy and protection, risk management, compliance, governance, and data security. Certified Data Privacy Solutions Engineer certification exam covers a range of topics, including privacy regulations, data protection laws, privacy program management, privacy by design, data security, and risk management. Certified Data Privacy Solutions Engineer certification is ideal for professionals who want to demonstrate their expertise in data privacy and security and advance their careers in this field.

ISACA Certified Data Privacy Solutions Engineer Sample Questions (Q191-Q196):

NEW QUESTION # 191
Which of the following vulnerabilities is MOST effectively mitigated by enforcing multi-factor authentication to obtain access to personal information?

A. End users using weak passwords
B. Vulnerabilities existing in authentication pages
C. End users forgetting their passwords
D. Organizations using weak encryption to transmit data

Answer: A

Explanation:
One of the most common vulnerabilities that can compromise the access to personal information is end users using weak passwords. Weak passwords are passwords that are easy to guess, crack, or steal, such as passwords that are short, simple, common, or reused. Weak passwords can allow unauthorized or malicious parties to gain access to personal information and cause privacy breaches, leaks, or misuse. Multi-factor authentication is an effective way to mitigate this vulnerability, as it requires end users to provide more than one piece of evidence to verify their identity, such as something they know (e.g., password), something they have (e.g., token), or something they are (e.g., biometric). Multi-factor authentication makes it harder for attackers to bypass the authentication process and access personal information. Reference: : CDPSE Review Manual (Digital Version), page 107

NEW QUESTION # 192
Which of the following is an example of data anonymization as a means to protect personal data when sharing a database?

A. The data is encrypted and a key is required to re-identify the data.
B. The data is transformed such that re-identification is impossible.
C. Key fields are hidden and unmasking is required to access to the data.
D. Names and addresses are removed but the rest of the data is left untouched.

Answer: B

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 # 193
Which of the following has the GREATEST impact on the treatment of data within the scope of an organization's privacy policy?

A. Data processing agreement
B. Data classification
C. Data flow diagram
D. Data protection impact assessment (DPIA)

Answer: B

Explanation:
Data classification is the process of categorizing data according to its sensitivity, value, and criticality for the organization and the data subjects. Data classification has the greatest impact on the treatment of data within the scope of an organization's privacy policy, as it determines the appropriate level of protection, access, retention, and disposal for each type of dat a. Data classification also helps to comply with the privacy principles and regulations, such as data minimization, purpose limitation, accuracy, security, and accountability.

NEW QUESTION # 194
Which of the following should an organization do FIRST to ensure it can respond to all data subject access requests in a timely manner?

A. Create a policy for handling access request
B. Invest in a platform to automate data review
C. Confirm what is required for disclosure.
D. Understand the data in its possession.

Answer: D

Explanation:
Before an organization can respond to data subject access requests (DSARs), it needs to have a clear understanding of the data in its possession, such as what types of personal data are collected, where they are stored, how they are processed, who has access to them, and how long they are retained. This will help the organization to locate and retrieve the relevant data for each DSAR, and to ensure that the data are accurate, complete and up to date. Understanding the data in its possession will also help the organization to comply with other data protection principles and obligations, such as data minimization, purpose limitation, security and accountability.
The other options are less important or irrelevant to do first. Investing in a platform to automate data review may help to speed up the response process, but it does not guarantee that the organization has identified all the data sources and categories that are subject to DSARs. Confirming what is required for disclosure is also important, but it depends on the specific request and the applicable law or regulation. Creating a policy for handling access requests is a good practice, but it should be based on a thorough understanding of the data in its possession.
Reference:
Practical Data Security and Privacy for GDPR and CCPA - ISACA, section 2: "It is important to understand what personal information is collected and processed by an organization." Introduction to Data Subject Access Requests - Everlaw, section 3: "The first step in responding to a DSAR is identifying where the relevant personal data reside within your organization." Guidelines 01/2022 on data subject rights - Right of access Version 1, section 2.1: "The controller should have a clear overview of all processing activities involving personal data."

NEW QUESTION # 195
A new marketing application needs to use data from the organization's customer database. Prior to the application using the data, which of the following should be done FIRST?

A. Ensure the data loss prevention (DLP) tool is logging activity.
B. Determine what data is required by the application.
C. De-identify all personal data in the database.
D. Renew the encryption key to include the application.

Answer: B

Explanation:
Explanation
Before using data from the organization's customer database for a new marketing application, the first step should be to determine what data is required by the application and for what purpose. This will help to ensure that the data collection and processing are relevant, necessary, and proportionate to the intended use, and that the data minimization principle is followed. Data minimization means that only the minimum amount of personal data needed to achieve a specific purpose should be collected and processed, and that any excess or irrelevant data should be deleted or anonymized1. This will also help to comply with the data privacy laws and regulations that apply to the organization, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require organizations to inform data subjects about the types and purposes of data processing, and to obtain their consent if needed23.
References:
ISACA, Data Privacy Audit/Assurance Program, Control Objective 2: Data Minimization, p. 61 ISACA, GDPR Data Protection Impact Assessments, p. 4-52 ISACA, CCPA vs. GDPR: Similarities and Differences, p. 1-23

NEW QUESTION # 196
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