Free IAPP AIGP Practice Exam with Questions & Answers

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.

Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking forbias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.

From theAI Governance in Practice Report2025:

“Performance monitoring protocols… should include mechanisms to assess and measure disparities in outcomes across different demographic groups.” (p. 12)

“Bias may not be evident during pre-deployment testing but can emerge in real-world use.” (p. 41)

B. Awareness trainingis helpful, but not a technical bias mitigation activity.

C. Analyzing training datais apre-deploymenttask.

D. Documenting human decisionsmay support auditability but doesn’t detect bias in AI outputs.

Private LLMs offer advantages likecustomizability,reduced hallucination,confidentiality, andalignment with enterprise-specific tasks, but theydo not inherently reduce the time or effortneeded fordata validation or verification— which remains an essential step regardless of model privacy.

From the AI risk and quality sections:

“Ensuring the quality of the data… is highly contextual and must be validated regardless of the model’s deployment environment.” (p. 17)

B, C, Dare legitimate benefits of private LLMs.

Ais incorrect — validation still requires time and resources.

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 Report2025, 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.

Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems — regardless of origin — require evaluation for fairness, accuracy, and representativeness.

From theAI Governance in Practice Report2025:

“Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced.” (p. 41)

A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI. Reference: AIGP Body of Knowledge on Impact Assessments.

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

From theAI Governance in Practice Report2025:

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

Developing a social media presence for a non-profit is best served by non-AI solutions. This task primarily involves content creation, community engagement, and strategic planning, which are effectively managed by human expertise and traditional marketing tools. AI is more suitable for tasks requiring automation, large-scale data analysis, and personalized recommendations, such as e-commerce personalization, forecasting cost overruns, or automating customer service responses. Reference: AIGP Body of Knowledge on AI Use Cases and Applications.

The core ethical concern when deploying diagnostic AI in a healthcare setting is ensuringfairness and accuracy across diverse patient populations. If the AI is trained on a dataset that isnot representativeof the population it will serve, it risks reinforcing health disparities and leading to misdiagnoses.

From theAI Governance in Practice Report2025:

“Training datasets lacking in diversity can produce outputs that systematically underperform for certain groups… this can lead to inaccurate or biased outcomes in healthcare settings.” (p. 41)

“Bias, discrimination and fairness challenge… inadequate or nonrepresentative training data can result in AI systems that propagate historical disparities.” (p. 42)

While physician oversight may reduce risk,biased data can still shape clinical decision-making.

A– Economic benefit is not central to ethical risk here.

C– Important but less critical than data representativeness.

D– Error rate matters but is addressed via validation; it’s not the core ethical issue.

The best human oversight mechanism for the police department to implement is for a police officer to consider the AI recommendation as part of the criminal investigation. This ensures that the AI system's output is used as a tool to aid human decision-making rather than replace it. The police officer should integrate the AI's insights with other evidence and contextual information to make informed decisions, maintaining a balance between technological aid and human judgment. Reference: AIGP Body of Knowledge on AI Integration and Human Oversight.