IAPP Updated AIGP Exam Questions and Answers by carla

The correct answer isB – Proprietary methods. While transparency is important, organizations arenot obligated to disclose proprietary algorithms, methods, or trade secretsin public disclosures.

From the AIGP Body of Knowledge – Transparency & Disclosures:

“AI system users should disclose the purpose, capabilities, limitations, and applicable legal context—but not sensitive IP.”

AI Governance in Practice Report 2025 (Transparency Section) states:

“Disclosure requirements balance public understanding with the need to protect proprietary business interests. Proprietary training methods are not expected to be disclosed.”

Thus, while it’s best practice to disclose theintended purpose, legal compliance, and system limitations,internal proprietary techniquesare usually excluded.

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Data minimization requires organizations to collect and process only personal information that is adequate, relevant, and necessary for the specified purpose. A basic to-do application may reasonably require limited account or contact information, but political opinions and trade-union affiliations have no apparent necessity for compiling, researching, or prioritizing tasks. Those attributes are also treated as particularly sensitive under European data-protection law. Privacy by design requires privacy safeguards and data-protection principles to be incorporated into the product from the design stage and reflected in default settings. Therefore, unnecessary collection at installation directly implicates both minimization and privacy by design. Purpose limitation may also become relevant, but explicit consent does not by itself cure unjustified overcollection. Transparency, portability, and access rights address different privacy obligations and do not best characterize the defect described here.

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The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability. The assessment often involves a thorough evaluation of the AI system ' s design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.

The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios. Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system ' s reliability, fairness, and security. Reference: AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.