AIGP IAPP Artificial Intelligence Governance Professional Free Practice Exam Questions (2026 Updated)

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.

===========

The correct answer is A because developing a social media presence typically does not require AI and can be effectively achieved through traditional digital marketing strategies, content planning, and manual engagement. AI governance principles emphasize that organizations should first determine whether AI is necessary or proportionate to the problem being solved. Using AI where simpler, deterministic, or human-driven solutions suffice can introduce unnecessary complexity, cost, and risk. In contrast, options B, C, and D involve tasks such as campaign optimization, personalization, and automation, which benefit significantly from AI capabilities like pattern recognition, predictive analytics, and natural language processing. Responsible AI governance encourages a “fit-for-purpose” approach, ensuring AI is only deployed when it provides clear added value over non-AI alternatives.

The correct answer is B because one of the primary advantages of cloud-based AI deployment is its ability to easily scale resources up or down based on demand. Cloud platforms provide elastic infrastructure, allowing organizations to dynamically allocate compute power, storage, and processing capacity as needed. This is especially important for AI systems that experience fluctuating workloads, such as peak inference requests or large-scale model training. While cloud deployment can improve accessibility and efficiency, it does not inherently reduce latency compared to edge deployments, nor does it automatically enhance privacy or transparency. In AI governance, scalability is a key operational benefit that supports performance monitoring, cost efficiency, and responsiveness, enabling organizations to manage AI systems effectively across different environments and usage levels.

In selecting the specific type of algorithm for the AI solution, the healthcare network ' s data science team is most important. This team possesses the technical expertise and understanding of the data, the clinical context, and the performance requirements needed to make an informed decision about which algorithm is most suitable. While the cloud provider and consulting firm can offer support and infrastructure, and the AI governance committee provides oversight, the data science team’s specialized knowledge is crucial for selecting and implementing the appropriate algorithm. Reference: AIGP Body of Knowledge, AI governance and team roles section.

Deployers of proprietary models arenot responsible for design, but they are accountable for how the system performsin their context of use, including ensuring ethical behavior, performance, and legal compliance.

From theAI Governance in Practice Report 2025:

“Deployers of AI systems must take reasonable steps to ensure that systems are used ethically, perform safely, and align with applicable laws and standards.” (p. 11–12)

“Operational governance… includes performance monitoring protocols, incident management plans, and regulatory oversight.” (p. 12)

Thus:

✅A. Ethical testing– Required to mitigate misuse and unintended harms.

❌B. Ethical design– Belongs todevelopers/providers, not deployers.

✅C. Technical performance– Deployers must ensure that AI performs as expected.

❌D. System documentation– This is theprovider’sobligation.

✅E. Regulatory compliance– Deployers must ensure system use complies with applicable laws.

The correct answer isE – Superintelligence.

While the other options (Autonomy, Automation, Adaptability, Speed and Scale) are commonly cited as real-world characteristics that affect governance today,superintelligenceremainsa theoretical concept.

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

“Core AI characteristics—such as automation, adaptability, speed, and autonomy—require active governance due to their impact on decision-making, legal liability, and risk.”

However,superintelligenceis speculative and not a current feature of AI systems under governance frameworks like theEU AI Act or NIST RMF.

Thus, it ' snot a current characteristicrequiring governance in real-world enterprise settings.

The correct answer isA. TheAcceptable Use Policy (AUP)sets the primary terms and conditions under which the AI model can be used, including limitations, prohibited uses, and operational constraints.

From the AIGP ILT Guide:

“The AUP is the most critical document to assess what you can and cannot do with the AI system. It governs use cases, outlines forbidden uses (e.g., disinformation), and supports responsible AI practices.”

Also confirmed in the AI Governance in Practice Report 2025 (Third-Party AI Assurance section):

“Reviewing and adhering to the acceptable use policy ensures that the model is being used in a manner that aligns with both provider expectations and ethical AI practices.”

===========

Under theEU AI Act, serious incidents involvinghigh-risk AI systemsmust be reported. The deployer is required topromptly inform the provider and relevant authoritiesabout the issue.

From theAI Governance in Practice Report 2025:

“Serious incidents involving high-risk systems… must be reported to the provider and relevant market surveillance authority.” (p. 35)

“Timely reporting is required when AI systems result in or may result in violations of fundamental rights.” (p. 35)

According to the GDPR, individuals have the right to not be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. However, there are exceptions to this right, one of which is when the decision is based on the data subject ' s explicit consent. This means that if an individual explicitly consents to the automated decision-making process, there is no requirement for human intervention to confirm or replace the decision. This exception ensures that individuals can have control over automated decisions that affect them, provided they have given clear and informed consent.

In the context of AI, particularly generative models, " hallucination " refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.

The November 2023 White House Executive Order provides guidance that governmental agencies should limit access to specific uses of generative AI. This means that generative AI tools should be used in a controlled manner, where their applications are restricted to well-defined, approved use cases that ensure the security, privacy, and ethical considerations are adequately addressed. This approach allows for the benefits of generative AI to be harnessed while mitigating potential risks and abuses.

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 is D. Key stroke dynamics, used to identify individuals, fall under biometric data, which is a special category of personal data under the GDPR and other frameworks.

From the AI Governance in Practice Report 2025:

“Keystroke dynamics may constitute biometric data if used to uniquely identify an individual… Biometric data is classified as special category personal data and requires higher protection standards.”

Also reflected in ILT Participant Guide:

“Biometric data, such as facial images, voiceprints, iris scans or keystroke patterns, are treated as special category data when they are used for the purpose of uniquely identifying individuals.”

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model ' s predictions. Model drift is when the model ' s performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

Respecting intellectual property rights means adhering to licensing terms and ensuring that generated content complies with these terms. A system that categorizes and applies filters based on licensing terms ensures that content is used legally and ethically, respecting the rights of content creators. While providing attribution is important, categorization and application of filters based on licensing terms are more directly tied to compliance with intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge sections on intellectual property and compliance.

The correct answer is Explainable Ai because it specifically refers to the ability of a system to describe the logic behind its decisions or output in a way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressing the black-box problem in AI.

From the AIGP ILT Participant Guide (Module on Transparency and Explainability):

“Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems.”

Also, according to the AI Governance in Practice Report 2025:

“Explainability refers to the representation of the underlying mechanisms of the AI system’s operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained.”

Thus, while Trustworthy and Responsible AI are broader concepts,explainabilityspecifically targets the regulatory concern about understanding outputs.

===========

Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model. Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.

Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.

According to theAI Governance in Practice Report 2025, organizations thatdeployAI must engage inperformance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:

“Operational governance… development of: →Performance monitoring protocols to ensure systems perform adequately for their intended purposes.” (p. 12)

“Product governance... includes: →System impact assessments to identify and address risk prior to product development or deployment.” (p. 11)

Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:

“The act imposes regulatory obligations… such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance...” (p. 7)

Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A’s minimum content safety requirements.

Let’s now evaluate the incorrect options:

A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.

C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A ' s primary concern.

D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A’s content safety standards.

Thus,testing the model ' s behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.

Ethical impact assessment should begin early and remain iterative throughout the AI lifecycle, making D correct. Waiting until production, a user threshold, or an actual ethical incident would make the assessment reactive rather than preventive. UNESCO describes ethical impact assessment as an " ethics by design " mechanism covering research, design, development, deployment, use, maintenance, monitoring, and eventual termination, while emphasizing its particular value during early research and design. OECD-listed assessment guidance similarly states that the first assessment should occur during design, with reassessments at development, deployment, and other significant lifecycle stages. NIST ' s AI RMF Playbook also recommends impact assessment at key stages because risks, operating conditions, stakeholders, data, and system capabilities can change over time. Continuous reassessment therefore supports proactive identification and mitigation of emerging harms.

================