DCPLA Exam Dumps - DSCI Certified Privacy Lead Assessor

While training third-party organizations is a relevant privacy governance function, it is not a primary technical or operational consideration when implementing data security solutions.

The other options (A, B, and C) directly relate to core security architecture, system-level controls, and data governance — all essential for privacy protection at a system level.

Hence, D is least likely to be considered in technical implementation.

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Privacy-enhancing technologies (PETs) are critical for operationalizing privacy principles. According to the DPF©:

Data minimization (I): Collect only necessary data

Data scrambling (III), Obfuscation (VI), and Encryption (VII): Techniques to protect identity and data content

Data loss prevention (IV): Prevent unauthorized sharing or leakage

Data mirroring and intrusion prevention systems are primarily security mechanisms and not specifically privacy-focused. Data portability, while a right, is not a technology per se for “upholding” privacy but for enabling user control.

Thus, C includes the most appropriate privacy technologies.

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The DSCI Assessment Framework for Privacy (DAF-P©) emphasizes the need for organizations to move beyond checkbox compliance to embrace “Demonstrable Accountability.”

This involves:

Being able to show evidence of privacy program implementation

Having appropriate governance structures

Showing that privacy principles are embedded into processes

This proactive and transparent approach to privacy governance aligns with leading global frameworks.

As per the official DSCI Privacy Framework (DPF©), the framework is built upon a set of nine core Privacy Principles that are foundational to establishing and assessing privacy initiatives in an organization. These principles are as follows:

Notice– Individuals must be informed about the collection and use of their personal data.

ChoiceandConsent– The data subject’s choice must be respected through consent mechanisms.

Collection Limitation– Personal data must be collected only for identified purposes.

Use Limitation– Data should be used only for the purposes specified at the time of collection.

Data Quality– Ensuring data is accurate, complete, and kept up-to-date.

AccessandCorrection– Data subjects must have access to their data and the ability to correct it.

Security– Adequate protection of personal data against unauthorized access and breaches.

Openness– Organizations must be transparent about their privacy practices.

Accountability– The entity collecting and processing data is responsible for complying with the principles.

These match exactly with the components listed in option A: I (Use Limitation), II (Accountability), III (Data Quality), IV (Notice), V (Preventing Harm—not explicitly named in DPF, hence not part of the standard nine), VI (ChoiceandConsent), VII (Access and Correction), VIII (Data Minimization), IX (Openness).

Hence, the correct nine principles according to DPF© are exactly as listed in option A.

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According to the DSCI Assessment Framework for Privacy (DAF-P©), the techniques for reducing identifiability differ in their effectiveness:

Pseudonymizationreplaces identifiable fields within a data record with artificial identifiers. However, if additional information (mapping or lookup tables) exists, re-identification is possible.

De-identificationremoves or masks identifiers, but residual or quasi-identifiers may still allow re-identification under certain conditions.

Anonymizationaims to irreversibly remove any link between the data and the identity of the subject, thus presenting the least risk of re-identification.

Therefore, when arranged in decreasing order of re-identification risk:

Pseudonymization(highest risk)

De-identification

Anonymization(lowest risk)

This validates optionA. I, IIas correct.

The DPF© outlines that the applicability and implementation of privacy principles depend on several contextual factors including:

The organization's role as data controller or processor (A)

Channels and methods of data inflow (B)

Jurisdictional regulations applicable to the organization's operations (C)

Public commitments, contracts, and stakeholder expectations (D)

The term “Opt-out” refers to a consent model in which individuals are automatically included in a data processing activity or program unless they explicitly indicate their desire not to participate.

Under the DSCI Privacy Framework, “Opt-out” is contrasted with “Opt-in,” where explicit affirmative consent is required before processing.

Opt-out is often implemented through mechanisms like pre-checked boxes or default settings, which the user can change. This is particularly common in direct marketing scenarios or cookies for analytics. The DAF-P© considers whether such consent mechanisms align with fairness and transparency principles.

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While several factors can influence the review of a privacy policy, changes in the legal or regulatory environment are the most critical. The DSCI Privacy Framework underscores that privacy policies must be aligned with applicable laws and standards.

A change in the legal/regulatory regime may necessitate revisions to ensure ongoing compliance and avoid legal risks. Internal awareness and peer practices are secondary considerations in comparison.