Free Practice Questions for IAPP AIGP Exam

The most critical reason for documenting AI-related risks is toreduce exposure to legal, regulatory, and reputational liabilities. Clear documentation demonstrates thatrisks were identified, assessed, and addressed, which is essential for accountability and defensibility in the face of audits, litigation, or enforcement actions.

From theAI Governance in Practice Report 2024:

“An effective AI governance model is about collective responsibility… which should encompass oversight mechanisms such as privacy, accountability, compliance.” (p. 13)

“Accountability… is based on the idea that there should be a person or entity that is ultimately responsible for any harm resulting from the use of the data, algorithm and AI system's underlying processes.” (p. 28)

While transparency, alignment with standards, and knowledge sharing are all secondary benefits,risk documentation’s primary role is liability mitigation.

Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.

Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs— making it preferable when deep control is needed.

From theAI Governance in Practice Report 2024:

“Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies.” (p. 39)

When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.

From theAI Governance in Practice Report 2024:

“Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems.” (p. 12, 35)

Testing results need to bedocumented thoroughlyto ensuretraceability, accountability, and compliance. This is central to enabling audits, investigations, or regulatory inquiries into the system’s development and performance.

From theAI Governance in Practice Report 2024:

“Documentation and recordkeeping are essential components… to demonstrate AI system compliance, trace system behavior, and support audits and conformity assessments.” (p. 34–35)

“Maintaining audit trails across development and deployment enables transparency and accountability.” (p. 12)

AandBare benefits, but not theprimary governance justification.

D– Limiting future testing is not a recommended goal.

Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenotthe parties responsible for affixing the CE marking or providing technical documentation—but they must verify that these have been done by the provider.

From theAI Governance in Practice Report 2024:

“Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed.” (p. 34–35)

Thus:

A. Provide technical documentation– done by theprovider.

B. Affix the CE marking–provider'sresponsibility.

C. Verify the Declaration of Conformity–importer obligation.

D. Conduct a DPIA– relevant under data protection laws,not requiredunder the EU AI Act forimporters.

Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.

According to definitions and discussions from theAI Governance in Practice Report 2024and U.S. privacy frameworks:

“Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition.” (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.)

Here’s how the options relate:

A. Iris scans– These are physical biometric identifiers.

B. Walking gait– Behavioral biometric used increasingly in surveillance and identification.

C. Keystroke dynamics– Behavioral biometric based on typing patterns.

D. GPS location of a user's fitness watch– This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.

Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute — especially when such data is used to train foundational AI models.

From theAI Governance in Practice Report 2024:

“As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management.” (p. 19)

“Legal due diligence may include verification of the personal data's lawful collection by the databroker...” (p. 19)

This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.

A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.

B. The tech companymay train the model but depends on lawful sourcing by data aggregators.

Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.