CDPSE Exam Dumps - Certified Data Privacy Solutions Engineer

A privacy impact assessment (PIA) is a systematic process to identify and evaluate the potential privacy impacts of a system, project, program or initiative that involves the collection, use, disclosure or retention of personal data. A PIA should be done first when planning a new implementation for tracking consumer web browser activity, as it would help to ensure that privacy risks are identified and mitigated before the implementation is executed. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with consumer expectations and preferences. The other options are not as important as conducting a PIA when planning a new implementation for tracking consumer web browser activity. Seeking approval from regulatory authorities may be required or advisable for some types of personal data or data processing activities, but it may not be necessary or sufficient for tracking consumer web browser activity, depending on the context and jurisdiction. Obtaining consent from the organization’s clients may be required or advisable for some types of personal data or data processing activities, but it may not be necessary or sufficient for tracking consumer web browser activity, depending on the context and jurisdiction. Reviewing and updating the cookie policy may be required or advisable for some types of personal data or data processing activities, but it may not be necessary or sufficient for tracking consumer web browser activity, depending on the context and jurisdiction1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

The most useful information for prioritizing database selection for encryption is the asset classification scheme. An asset classification scheme is a system of organizing and categorizing assets based on their value, sensitivity, criticality, or risk level. An asset classification scheme helps to determine the appropriate level of protection or handling for each asset. For example, an asset classification scheme may assign labels such as public, internal, confidential, or secret to different types of data based on their impact if compromised. Databases that contain higher-classified data should be prioritized for encryption to prevent unauthorized access, disclosure, or modification.

Database administration audit logs, historical security incidents, or penetration test results are also useful information for database security, but they are not the most useful for prioritizing database selection for encryption. Database administration audit logs are records of activities performed by database administrators or other privileged users on the database system. Database administration audit logs help to monitor and verify the actions and changes made by authorized users and detect any anomalies or violations. Historical security incidents are records of events that have compromised or threatened the security of the database system in the past. Historical security incidents help to identify and analyze the root causes, impacts, and lessons learned from previous breaches or attacks. Penetration test results are reports of simulated attacks performed by ethical hackers or security experts on the database system to evaluate its vulnerabilities and defenses. Penetration test results help to discover and exploit any weaknesses or gaps in the database security posture and recommend remediation actions.

References: Data Classification Policy - SANS Institute, Database Security Best Practices - Oracle, [Database Security: An Essential Guide | IBM]

A virtual private network (VPN) is a technology that creates a secure and encrypted connection over a public network, such as the internet. A VPN should be established first before authorizing remote access to a data store containing personal data, as it protects the data from unauthorized interception, modification, or disclosure by third parties. A VPN also helps to ensure the identity and authenticity of the remote users and devices accessing the data store. References: 2 Domain 2, Task 8

Before de-identifying data, it is important to determine the categories of personal data collected, such as names, addresses, phone numbers, email addresses, social security numbers, health information, and so on. This will help to identify which data elements are considered identifiers or quasi-identifiers, and which de-identification techniques are appropriate for each category. For example, some data elements may need to be removed completely, while others may be masked, generalized, or perturbed.

References:

• Anonymize and De-identify | Research Data Management
• Data De-identification: An Overview of Basic Terms - ed

The best answer is D. Assess the organization’s exposure related to the migration.

A comprehensive explanation is:

Before an organization migrates data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction, it should first assess its exposure related to the migration. This means that the organization should identify and evaluate the potential risks and benefits of moving its data to the cloud, taking into account the legal, regulatory, contractual, and ethical obligations and implications of doing so.

Some of the factors that the organization should consider in its assessment are:

• The nature, sensitivity, and value of the data being migrated, and the impact of its loss, theft, corruption, or disclosure on the organization and its stakeholders.
• The security, privacy, and compliance requirements and standards that apply to the data in each jurisdiction where it is stored, processed, or accessed, and the differences or conflicts among them.
• The trustworthiness, reliability, and reputation of the cloud service provider and its subcontractors, and the terms and conditions of their service level agreements (SLAs) and contracts.
• The availability, performance, scalability, and cost-effectiveness of the cloud-hosted solution compared to the on-premise solution, and the trade-offs involved.
• The technical feasibility and complexity of migrating the data from the on-premise solution to the cloud-hosted solution, and the tools and methods needed to do so.
• The organizational readiness and capability to manage the change and transition from the on-premise solution to the cloud-hosted solution, and the training and support needed for the staff and users.

By conducting a thorough assessment of its exposure related to the migration, the organization can make an informed decision about whether to proceed with the migration or not, or under what conditions or modifications. The assessment can also help the organization to plan and implement appropriate measures and controls to mitigate or avoid any negative consequences and enhance or maximize any positive outcomes of the migration.

Ensuring data loss prevention (DLP) alerts are turned on (A), encrypting the data while it is being migrated (B), and conducting a penetration test of the hosted solution © are all good practices to protect data privacy and security when migrating data from an on-premise solution to a cloud-hosted solution that spans more than one jurisdiction. However they are not the first steps that should be done before the migration. They are more relevant during or after the migration process. They also do not address other aspects of exposure related to the migration, such as legal, regulatory, contractual, or ethical issues.

References:

• Data Migration: On-Premise to Cloud - 10 Steps to Success1
• 8 Best Practices for On-Premises to Cloud Migration2
• 5 Steps for a Successful On-Premise to Cloud Migration3
• Extend on-premises data solutions to the cloud4
• On Premise to Cloud migration tool5

Collecting only the data necessary to meet objectives is the best approach to minimize privacy risk when collecting personal data. This is based on the principle of data minimization, which states that personal data should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. Using a third party, collecting data through a secure web server, or aggregating data immediately may reduce some privacy risks, but they do not eliminate the possibility of collecting excessive or unnecessary data. References: CDPSE Exam Content Outline, Domain 3, Task 3.2

Data retention is a process of keeping personal data for a specified period of time for legitimate purposes, such as legal obligations, contractual agreements, business operations or historical records. Data retention should be based on the principle of data minimization, which requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. Data retention should also comply with the principle of storage limitation, which requires deleting or disposing of personal data when it is no longer needed or justified. The most likely valid use case for keeping a customer’s personal data after contract termination is a required retention period due to regulations, such as tax laws, financial laws, health laws or consumer protection laws, that mandate the organization to retain certain types of customer data for a certain period of time after the end of the contractual relationship. The other options are not valid use cases for keeping a customer’s personal data after contract termination, as they do not meet the criteria of necessity, relevance or justification. For the purpose of medical research, the organization would need to obtain the consent of the customer or have another legal basis for processing their personal data for a different purpose than the original contract. A forthcoming campaign to win back customers or ease of onboarding when the customer returns are not legitimate purposes for retaining customer data after contract termination, as they are not related to the original contract and may violate the customer’s privacy rights and preferences. , p. 99-100 References: : CDPSE Review Manual (Digital Version)