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IAPP AIGP Sample Question Answers
Question # 1
A company deploys an AI model for fraud detection in online transactions. During its operation, themodel begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent.Which is the best step the company should take to address this development?
A. Dedicate more resources to monitor the model. B. Maintain records of all false positives. C. Deactivate the model until an assessment is made. D. Conduct training for customer service teams to handle flagged transactions.
Answer: C
Explanation:
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:
œIncident management plans should enable identification, escalation, and system rollback to prevent
continued harm from malfunctioning AI systems. (p. 12, 35)
Question # 2
After initially deploying a third-party AI model, you learn the developer has released a new version.As deployer of this third-party model, what should you do?
A. Audit the model. B. Retrain the model. C. Seek input from data scientists. D. Communicate necessary updates to your users.
Answer: A
Explanation:
When anew versionof a third-party model is released, the deployer must ensure it still meets safety,
performance, and compliance requirements ” which calls for aformal audit.
From theAI Governance in Practice Report 2024:
œAny updates or changes to AI systems should trigger a re-evaluation to ensure continued
compliance and performance. (p. 12)
œPost-market monitoring includes reassessing the impact of updated models or retraining. (p. 35)
Question # 3
What is the most significant risk of deploying an AI model that can create realistic images andvideos?
A. Copyright infringement. B. Security breaches. C. Downstream harms. D. Output cannot be protected.
Answer: C
Explanation:
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 2024:
œWith generative AI, downstream harms such as deception, reputational damage, misinformation,
and manipulation can emerge even if original use was lawful. (p. 55“56)
Question # 4
A deployer discovers that a high-risk AI recruiting system has been making widespread errors,resulting in harms to the rights of a considerable number of EU residents who are deniedconsideration for jobs for improper reasons such as ethnicity, gender and age.According to the EU AI Act, what should the company do first?
A. Notify the provider, the distributor, and finally the relevant market authority of the seriousincident. B. Identify any decisions that may have been improperly made and re-open them for human review. C. Submit an incomplete report to the relevant market authority immediately and follow up with acomplete report as soon as possible. D. Conduct a thorough investigation of the serious incident within the 15 day timeline and presentthe completed report to the relevant market authority.
Answer: A
Explanation:
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 2024:
œSerious incidents involving high-risk systems¦ must be reported to the provider and relevant
market surveillance authority. (p. 35)
œTimely reporting is required when AI systems result in or may result in violations of fundamental
rights. (p. 35)
Question # 5
All of the following issues are unique for proprietary AI model deployments EXCEPT?
A. The acquisition of training data. B. The cost of AI chips. C. The potential for bias. D. The necessity of performing conformity assessments.
Answer: C
Explanation:
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 Report 2024:
œBias, discrimination and fairness challenges are present in both open and closed models, regardless
of how the model is sourced. (p. 41)
Question # 6
A company that deploys AI but is not currently a provider or developer intends to develop andmarket its own AI system.Which obligation would then be likely to apply?
A. Implementing a risk management framework. B. Conducting an impact assessment including a post-deployment monitoring plan. C. Developing documentation on the system, the potential risks and the safeguards applied. D. Developing a reporting plan for any observed algorithmic discrimination or harms to individualsrights and freedoms.
Answer: C
Explanation:
Once a company moves from being adeployerto also acting as aprovider or developer, it
assumesnew obligationsunder regulations like the EU AI Act. One of the core requirements for
providers is to produce and maintaintechnical documentation, including descriptions of the model,
associated risks, and mitigation strategies.
From theAI Governance in Practice Report 2024:
œProviders of high-risk AI systems must draw up technical documentation demonstrating the
systems conformity with the requirements... including potential risks and safeguards applied. (p.
34)
œThis documentation must be available before placing the system on the market. (p. 35)
Question # 7
A company developing and deploying its own AI model would perform all of the following steps tomonitor and evaluate the model's performance EXCEPT?
A. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders. B. Setting up automated tools to regularly track the model's accuracy, precision and recall rates inreal-time. C. Implementing a formal incident response plan to address incidents that may occur during systemoperation. D. Establishing a regular schedule for human evaluation of the model's performance, includingqualitative assessments.
Answer: A
Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or
standard practicefor internal performance evaluation. Risk assessments and reporting typically
remaininternal or shared with regulators.
From theAI Governance in Practice Report 2024:
œOrganizations must assess secondary risks¦ but disclosure is subject to context, regulatory
requirements, and risk management discretion. (p. 30)
Question # 8
A leading software development company wants to integrate AI-powered chatbots into theircustomer service platform. After researching various AI models in the market which have beendeveloped by third-party developers, they're considering two options:Option A - an open-source language model trained on a vast corpus of text data and capable of beingtrained to respond to natural language inputs.Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformerbasedarchitectures to generate human-like responses based on multimodal user input.Option A would be the best choice for the company because?
A. It is less expensive to run B. It may be better suited for applications requiring customization. C. It can handle voice commands and is more suitable for phone-based customer support. D. It is built for large-scale, complex dialogues and would be more effective in handling high-volumecustomer inquiries.
Answer: B
Explanation:
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:
œOpen-source AI allows organizations to review, adapt, and control model behavior in line with
organizational needs and policies. (p. 39)
Question # 9
Which model is best for efficiency and agility, and tailored for lower-resource settings?
A. Supervised learning model. B. Multimodal model. C. Small language model. D. Generative language model.
Answer: C
Explanation:
Small language models (SLMs)arelightweight, requireless compute, and arebetter suited to lowresource
or edge environments, making them ideal for agility and efficiency.
From general AI best practices:
œSLMs 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)
Question # 10
What is the most important factor when deciding whether or not to select a proprietary AI model?
A. What business purpose it will serve. B. How frequently it will be updated. C. Whether its training data is disclosed. D. Whether its system card identifies risks.
Answer: A
Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for
business purpose. Whether it serves the intended goals is foundational before evaluating technical
or governance features.
From theAI Governance in Practice Report 2024:
œAI governance starts with defining the corporate strategy for AI¦ and aligning systems with business
purpose and operational context. (p. 11)
B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.
Question # 11
All of the following are potential benefits of using private over public LLMs EXCEPT?
A. Reduction in time taken for data validation and verification. B. Confirmation of security and confidentiality. C. Reduction in possibility of hallucinated information. D. Application for specific use cases within the enterprise.
œRisk-based approaches are often distilled into organizational risk management efforts, which put
impact assessments at the heart of deciding whether harm can be reduced. (p. 29)
œDPIAs¦ help organizations identify, analyze and minimize data-related risks and demonstrate
accountability. (p. 30)
A . Environmental scanis too general.
B . Red teamingis useful for adversarial risk but not broad.
C . Integration testingfocuses on technical/system compatibility, not overall risk.
Question # 13
Why is it important that conformity requirements are satisfied before an AI system is released intoproduction?
A. To ensure the visual design is fit-for-purpose. B. To ensure the AI system is easy for end-users to operate. C. To guarantee interoperability of the AI system across multiple platforms and environments. D. To comply with legal and regulatory standards, ensuring the AI system is safe and trustworthy.
Answer: D
Explanation:
Conformity assessmentsare a core requirement under theEU AI Actfor high-risk systems and serve to
confirm that the AI meetsregulatory, safety, and ethical standardsbefore it is put into production.
From theAI Governance in Practice Report 2024:
œConformity assessments¦ ensure that systems comply with legal requirements, safety criteria, and
intended purpose before being placed on the market. (p. 34)
œThey are a critical step to demonstrate safety and trustworthiness in AI deployment. (p. 35)
Question # 14
In procuring an AI system from a vendor, which of the following would be important to include in acontract to enable proper oversight and auditing of the system?
A. Liability for mistakes. B. Ownership of data and outputs. C. Responsibility for improvements. D. Appropriate access to data and models.
Answer: D
Explanation:
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data,
documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report 2024:
œAccess to technical documentation and system internals is essential to enable effective auditing,
conformity checks, and accountability mechanisms. (p. 11, 34)
Ais about liability, not auditability.
Bmatters for IP rights, not oversight.
Crelates to lifecycle responsibility but doesnt guarantee audit access.
Question # 15
Your organization is searching for a new way to help accurately forecast sales predictions by varioustypes of customers.Which of the following is the best type of model to choose if your organization wants to customizethe model and avoid lock-in?
A. A free large language model. B. A classic machine learning model. C. A proprietary generative AI model. D. A subscription-based, multimodal model.
Answer: B
Explanation:
Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor
lock-in,classic ML models(e.g., regression, decision trees, random forests) are optimal.
From theAI Governance in Practice Report 2024:
œOrganizations seeking transparency, customizability, and control often prefer classic ML models due
to their flexibility and ease of governance. (p. 33)
AandCmay have limited transparency and are often tied to specific providers.
Dinvolves ongoing costs and limited model control.
Question # 16
A US-based mortgage lender has purchased a chatbot. They plan to have the chatbot collectinformation from consumers who are interested in loans and offer the consumers 2-3 differentoptions based on its current pricing and product offerings, which change frequently. This chatbot wasinitially developed and previously deployed by a Russian airline for booking flights.The best option for the part of the process that generates the loan offers is?
A. Retrieval-Augmented Generation. B. Multimodal Generative AI. C. Expert System. D. Quantum computing
Answer: C
Explanation:
Offeringloan products based on current offerings and rulesrequires a system that can followexplicit
business logic, not generate open-ended content. Anexpert system, which is a rules-based AI that
uses œif-then logic, is ideal here.
From the AI governance context:
œRule-based AI systems are often preferred when decisions must adhere to precise regulatory or
financial criteria. (aligned with AI best practices in regulated sectors)
A . RAGis used to integrate external knowledge”not suitable for structured, rule-based logic.
B . Multimodal modelshandle varied input types”not needed here.
D . Quantum computingis not yet practical or relevant for this business use case.
Question # 17
During the first month when the company monitors the model for bias, it is most important to?
A. Continue disparity testing. B. Provide regular awareness training. C. Analyze the quality of the training and testing data. D. Document the results of final decisions made by the human underwriter.
Answer: A
Explanation:
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 Report 2024:
œPerformance monitoring protocols¦ should include mechanisms to assess and measure disparities
in outcomes across different demographic groups. (p. 12)
œBias 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 doesnt detect bias in AI outputs.
Question # 18
Retrieval-Augmented Generation (RAG) is defined as?
A. Combining LLMs with private knowledge bases to improve their outputs. B. Reducing computational processing requirements of the LLMs. C. Applying advanced filtering techniques to the LLMs. D. Fine tuning LLMs to minimize biased outputs.
Answer: A
Explanation:
Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by
integratingexternal, up-to-date, or proprietary informationinto the generation pipeline”allowing
the model tofetch relevant factsfrom a trusted knowledge source at query time.
Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI
governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or
high-stakes environmentswhere hallucinations are a concern.
B, C, and Ddescribe optimization or bias mitigation”not the core function of RAG.
Question # 19
MULTI-SELECTPlease select 3 of the 5 options below. No partial credit will be given.Training an AI model is time-consuming because of?
A. The complexity of the AI model. B. The maturity of AI governance. C. The volume of training data. D. The number of stakeholders. E. The quality of the training data.
Answer: A,C,E
Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and
the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report 2024:
œMost AI requires sizeable amounts of high-quality data¦ to ensure desired and accurate output. (p.
15)
œThe accuracy of AI model outputs depends significantly on the quality of their inputs. (p. 24)
œComplex AI systems¦ with many parameters¦ result in long development and training phases. (p.
32)
B . Maturity of governanceaffects oversight, not training time.
D . Number of stakeholdersaffects alignment, not direct training duration.
Question # 20
The best practice to manage third-party risk associated with AI systems is to create and implementpolicies that?
A. Focus on the financial stability of third-party vendors as the primary criterion for risk assessment. B. Provide for an appropriate level of due diligence and ongoing monitoring based on the definedrisk. C. Require third-party AI systems to undergo a comprehensive audit by an external cybersecurity firmevery six months. D. Focus on the technical aspects of AI systems, such as data security, while ethical risks areaddressed through suitable contracts.
Answer: B
Explanation:
Third-party risk management for AI systems should beproportional and risk-based, involvinginitial
due diligenceandongoing monitoringthat reflects thelevel of risk posedby the third party's AI system.
From theAI Governance in Practice Report 2024:
œThird-party due diligence assessments to identify possible external risk and inform selection. (p.
11)
œLegal due diligence may include verification of the personal data's lawful collection by the data
broker, review of contractual obligations¦ (p. 19)
Afocuses too narrowly on financial stability.
Cis excessive and not scalable or aligned with best practices.
Dinappropriately separates ethical and technical risks; both must be evaluated holistically.
Question # 21
What is the most important reason for documenting risks when developing an AI system?
A. To provide transparency to stakeholders. B. To align with industry standards. C. To promote knowledge sharing. D. To mitigate potential liability.
Answer: D
Explanation:
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:
œAn effective AI governance model is about collective responsibility¦ which should encompass
oversight mechanisms such as privacy, accountability, compliance. (p. 13)
œAccountability¦ 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
documentations primary role is liability mitigation.
Question # 22
An AI system's function, the industry and the location in which it operates are important factors inconsidering which of the following?
A. Organizational accountability. B. Internal governance needs. C. Diversity of data sources. D. Explainability of results.
Answer: B
Explanation:
An AI systemsfunction,industry, anddeployment locationdefine itsrisk profile, which directly
influences theinternal governance structuresan organization must put in place.
From theAI Governance in Practice Report 2024:
œThere are many challenges and potential solutions for AI governance, each with unique
proximityand significance based on an organizations role, footprint, broader risk-governance profile
and maturity. (p. 4)
œAI governance starts with defining the corporate strategy for AI¦ and formulating policy standards
and operational procedures to reflect industry, use case, and location. (p. 11)
A“ Organizational accountability is broader and not directly scoped by industry or function.
C“ Diversity of data sources is tied to data strategy.
D“ Explainability is more influenced by model type, not use context.
Question # 23
MULTI-SELECTPlease select 3 of the 5 options below. No partial credit will be given.What are the roles and responsibilities of deployers of a proprietary model?
A. Ethical testing. B. Ethical design. C. Technical performance. D. System documentation. E. Regulatory compliance.
Answer: A,C,E
Explanation:
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 2024:
œDeployers 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)
œOperational governance¦ includes performance monitoring protocols, incident management plans,
and regulatory oversight. (p. 12)
Thus:
✅
A. Ethical testing“ Required to mitigate misuse and unintended harms.
â ŒB. Ethical design“ Belongs todevelopers/providers, not deployers.
✅C. Technical performance“ Deployers must ensure that AI performs as expected.
â ŒD. System documentation“ This is theprovidersobligation.
✅E. Regulatory compliance“ Deployers must ensure system use complies with applicable laws.
Question # 24
What is the most important reason to document the results of AI testing?
A. To support post-deployment maintenance. B. To identify areas for red-teaming focus. C. To create a verifiable audit trail. D. To limit the need for future testing cycles.
Answer: C
Explanation:
Testing results need to bedocumented thoroughlyto ensuretraceability, accountability, and
compliance. This is central to enabling audits, investigations, or regulatory inquiries into the systems
development and performance.
From theAI Governance in Practice Report 2024:
œDocumentation and recordkeeping are essential components¦ to demonstrate AI system
compliance, trace system behavior, and support audits and conformity assessments. (p. 34“35)
œMaintaining 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.
Question # 25
A US hospital plans to develop an AI that will review available patient data in order to propose aninitial diagnosis to licensed physicians. The hospital will implement a policy that requires physiciansto consider the AI proposal, but conduct their own physical examinations prior to making a finaldiagnosis.An important ethical concern with this plan is?
A. Whether patients will receive an economic benefit from the use of AI. B. Whether the AI was trained on a representative dataset. C. Whether physicians understand how the AI works. D. Whether the AI will have an error rate comparable to human physicians.
Answer: B
Explanation:
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 Report 2024:
œTraining 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)
œBias, 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; its not the core ethical issue.
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