Free Practice Questions for IAPP AIGP Exam

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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The correct answer isD. While modernization through software may support efficiency, it isnot a foundational or essential componentof designing an integrated strategy.

From the AI Governance in Practice Report 2025:

“Integrated strategies rely on senior management support, ethical reviews, and stakeholder engagement... The use of tools and platforms may come later as an operational enhancement.”

Also confirmed in AIGP Body of Knowledge:

“Key components of a governance framework include leadership buy-in, ethical analysis, and stakeholder input. Tools are supporting elements—not strategic drivers.”

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The correct answer is A because it directly demonstrates meaningful human oversight over AI-generated outcomes, which is a key requirement in AI governance frameworks and regulations such as the EU AI Act. Human oversight requires that a qualified human can review, intervene, and override AI decisions before they produce legal or significant real-world effects. In high-risk contexts like healthcare diagnostics, governance frameworks emphasize “human-in-the-loop” controls to prevent harm and ensure accountability. Option A ensures a licensed medical professional validates the AI output before it is finalized, aligning with safety, accountability, and risk mitigation principles. Other options describe training, system design, or monitoring, which are important governance measures but do not constitute direct oversight of individual AI decisions at the point of impact, making them insufficient under strict regulatory expectations.

Under the EU AI Act, high-risk AI systems like Crime Buster 9619 would require a detailed conformity assessment before being deployed in the EU market. This assessment ensures that the AI system complies with all relevant regulations and standards, addressing potential risks related to privacy, security, and discrimination. The company would not need to register the tool with the EU database (A), seek approval from an EU authority (B), or face a ban (D) as long as it meets the necessary conformity requirements​​​​.

The correct answer isD – Privacy impact assessments (PIAs). These aredirectly adaptablefor identifying risks in AI systems, particularly around data usage, bias, and individual impacts.

From the AIGP ILT Guide – Risk Management Module:

“PIAs and DPIAs are existing tools used in privacy compliance that can be extended to evaluate the risks of AI, including fairness, explainability, and legality.”

AI Governance in Practice Report 2025 further explains:

“Organizations can adapt privacy impact assessments to evaluate the ethical, legal, and technical risks posed by AI systems. They provide a structured and recognized method.”

PIAs are preferable over general security practices (like pen testing) which do not address algorithmic bias or legal compliance directly.

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Small language models (SLMs)arelightweight, requireless compute, and arebetter suited to low-resource or edge environments, making them ideal for agility and efficiency.

From general AI best practices:

“SLMs can be deployed in environments with limited computing power, ensuring lower cost and faster integration in constrained contexts.” (aligned with industry-wide AI deployment strategies)

The correct answer isA. While location of processors may have implications (such as for data transfers under GDPR), there is no absolute requirement that third-party processors be based in the same country.

From the AI Governance in Practice Report 2025 and ILT Guide:

“Data controllers are responsible for ensuring that third-party processors have adequate protections, but not necessarily that they reside in the same jurisdiction. What is required is legal safeguards (e.g., SCCs) for international transfers, not same-country location.”

In contrast, DPIAs, DSARs, and implementation of technical/organizational safeguards are explicitly required under GDPR and responsible AI frameworks.

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The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such asdeepfakes, misinformation, reputational damage, and erosion of trust.

From theAI Governance in Practice Report 2025:

“With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful.” (p. 55–56)

TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systemsimpact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or usedentirely outside of the EU—such as in theUK, unless they affect the EU market or individuals.

From theAI Governance in Practice Report 2025:

“The act imposes regulatory obligations… depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems.” (p. 7)

“The EU AI Act is the world’s first comprehensive AI regulation… requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market.” (p. 7–8)

Thus:

A. Open source releasedoes not exclude applicability if deployed in the EU.

B. Deployment at an EU borderclearly invokes jurisdiction.

D. Training by an EU companycreates jurisdictional links.

C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.

Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.