CDPSE Exam Dumps - Certified Data Privacy Solutions Engineer

The primary consideration to protect against privacy violations when utilizing artificial intelligence (AI) driven business decisions is ensuring proper data sets are used to train the models. AI is a technology that enables machines or systems to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, etc. AI relies on large amounts of data to train its models and algorithms to perform these tasks. However, if the data sets used to train the models are inaccurate, incomplete, biased, or outdated, they can result in privacy violations, such as discrimination, profiling, manipulation, or harm to the data subjects. Therefore, an IT privacy practitioner should ensure that the data sets used to train the models are proper, meaning that they are relevant, representative, reliable, and respectful of the data subjects’ rights and interests. References: : CDPSE Review Manual (Digital Version), page 141

The most important consideration for developing data retention requirements is the applicable regulations that govern the data. Different types of data may be subject to different legal and regulatory obligations, such as how long the data must be kept, how it must be protected, and how it can be accessed or disposed of. Failing to comply with these obligations can result in fines, penalties, lawsuits, or reputational damage for the organization. Therefore, it is essential to identify and follow the applicable regulations for each data category.

The most effective way to support organizational privacy awareness objectives is D. Customizing awareness training by business unit function.

A comprehensive explanation is:

Organizational privacy awareness objectives are the goals and expectations that an organization sets for its employees and stakeholders regarding the protection and management of personal data. Privacy awareness objectives may vary depending on the nature, scope, and purpose of the organization’s data processing activities, as well as the legal, regulatory, contractual, and ethical obligations and implications that apply to them.

One of the best practices to support organizational privacy awareness objectives is to customize awareness training by business unit function. This means that the organization should design and deliver privacy awareness training programs that are tailored to the specific roles, responsibilities, and needs of each business unit or department within the organization. Customizing awareness training by business unit function can have several benefits, such as:

Enhancing the relevance and effectiveness of the training content and methods for each audience group, by addressing their specific privacy challenges, risks, and opportunities.

Increasing the engagement and motivation of the trainees, by showing them how privacy relates to their daily tasks, goals, and performance.

Improving the retention and application of the training knowledge and skills, by providing practical examples, scenarios, and exercises that reflect the real-world situations and problems that the trainees may encounter.

Fostering a culture of privacy across the organization, by creating a common language and understanding of privacy concepts, principles, and practices among different business units or departments.

Some examples of how to customize awareness training by business unit function are:

Providing different levels or modules of training based on the degree of access or exposure to personal data that each business unit or department has. For example, a basic level of training for all employees, an intermediate level of training for employees who handle personal data occasionally or incidentally, and an advanced level of training for employees who handle personal data regularly or extensively.

Providing different topics or themes of training based on the type or category of personal data that each business unit or department processes. For example, a general topic of training for employees who process non-sensitive or non-personal data, a specific topic of training for employees who process sensitive or special data categories (such as health, biometric, financial, or political data), and a specialized topic of training for employees who process high-risk or high-value data (such as intellectual property, trade secrets, or customer loyalty data).

Providing different formats or modes of training based on the preferences or constraints of each business unit or department. For example, a face-to-face format of training for employees who work in the same location or office, an online format of training for employees who work remotely or across different time zones, and a blended format of training for employees who work in a hybrid mode or have flexible schedules.

The other options are not as effective as option D.

Funding in-depth training and awareness education for data privacy staff (A) may improve the competence and confidence of the data privacy staff who are responsible for designing and implementing the privacy policies and practices of the organization, but it does not necessarily support the organizational privacy awareness objectives for the rest of the employees and stakeholders.

Implementing an annual training certification process (B) may ensure that the employees and stakeholders are updated and refreshed on the privacy policies and practices of the organization on a regular basis, but it does not necessarily address their specific privacy needs and challenges based on their business unit function.

Including mandatory awareness training as part of performance evaluations © may incentivize the employees and stakeholders to participate in and complete the privacy awareness training programs offered by the organization, but it does not necessarily enhance their understanding and application of privacy concepts and principles based on their business unit function.

Data minimization requires collecting and using only what is necessary for the stated purpose—directly limiting privacy risk in AI development and use. Encryption (A) and masking (D) protect data post-collection, and pseudonymization (B) reduces identifiability but does not limit collection or use. Minimization addresses privacy by default at the source.

“Collect and process no more personal data than necessary for the specified purpose.”

The most effective way to incorporate privacy by design principles into applications is to include privacy requirements in software development practices, because this ensures that privacy is considered and integrated from the early stages of the design process and throughout the entire lifecycle of the application. Software development practices include activities such as defining the scope, objectives, and specifications of the application, identifying and analyzing the privacy risks and impacts, selecting and implementing the appropriate privacy-enhancing technologies and controls, testing and validating the privacy functionality and performance, and monitoring and reviewing the privacy compliance and effectiveness of the application. By including privacy requirements in software development practices, the organization can achieve a proactive, preventive, and embedded approach to privacy that aligns with the privacy by design principles.

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)

Application hardening measures are the most important action to protect a mobile banking app and its data against manipulation and disclosure because they prevent attackers from reverse engineering, tampering, or injecting malicious code into the app. Application hardening measures include techniques such as code obfuscation, encryption, integrity checks, anti-debugging, and anti-tampering mechanisms. These measures make the app more resilient and secure against various types of cyberattacks.