Easy To Download IAPP AIGP Exam Dumps Updated 204 Questions
New Updated AIGP Exam Questions 2026
IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 37
Retraining an LLM can be necessary for all of the following reasons EXCEPT?
- A. To minimize degradation in prediction accuracy due tochanges in data.
- B. To ensure interpretability of the model's predictions.
- C. Adjust the model's hyper parameters specific use case.
- D. Account for new interpretations of the same data.
Answer: B
Explanation:
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.
NEW QUESTION # 38
A company plans on procuring a tool from an Al provider for its employees to use for certain business purposes.
Which contractual provision would best protect the company's intellectual property in the tool, including training and testing data?
- A. The provider willdefend and indemnify the company against infringement claims.
- B. The provider willwarrant that the tool will work as intended.
- C. The provider willgive privacy notice to individuals before using their personal data to train or test the tool.
- D. The provider willobtain and maintain insurance to cover potential claims.
Answer: A
Explanation:
To protect the company's intellectual property, the most pertinent contractual provision is ensuring that the AI provider will defend and indemnify the company against infringement claims. This clause means the provider will take responsibility for any intellectual property disputes that arise, thereby safeguarding the company from potential legal and financial repercussions related to the use of the tool. Other options, while beneficial, do not directly address the protection of intellectual property. This concept is detailed in the contractual best practices section of the IAPP AIGP Body of Knowledge.
NEW QUESTION # 39
You are the chief privacy officer of a medical research company that would like to collect and use sensitive data about cancer patients, such as their names, addresses, race and ethnic origin, medical histories, insurance claims, pharmaceutical prescriptions, eating and drinking habits and physical activity.
The company will use this sensitive data to build an AI algorithm that will spot common attributes that will help predict if seemingly healthy people are more likely to get cancer. However, the company is unable to obtain consent from enough patients to sufficiently collect the minimum data to train its model.
Which of the following solutions would most efficiently balance privacy concerns with the lack of available data during the testing phase?
- A. Refocus the algorithm to patients without cancer.
- B. Deploy the current model and recalibrate it over time with more data.
- C. Utilize synthetic data to offset the lack of patient data.
- D. Extend the model to multimodal ingestion with text and images.
Answer: C
Explanation:
Using synthetic data allows the company to augment limited real patient data while protecting privacy, efficiently addressing data scarcity during testing.
NEW QUESTION # 40
What is the primary purpose of conducting ethical red-teaming on an Al system?
- A. To ensure compliance with applicable law.
- B. To improve the model's accuracy.
- C. To identify security vulnerabilities.
- D. To simulate model risk scenarios.
Answer: D
Explanation:
The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios.
Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system's reliability, fairness, and security. Reference: AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.
NEW QUESTION # 41
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
What is the best strategy to mitigate the bias uncovered in the loan applications?
- A. Retrain the model with data that reflects demographic parity.
- B. Procure a third-party statistical bias assessment tool.
- C. Delete all gender-based data in the data set.
- D. Document all instances of bias in the data set.
Answer: A
Explanation:
Retraining the model with data that reflects demographic parity is the best strategy to mitigate the bias uncovered in the loan applications. This approach addresses the root cause of the bias by ensuring that the training data is representative and balanced, leading to more equitable decision-making by the AI model.
Reference: The AIGP Body of Knowledge stresses the importance of using high-quality, unbiased training data to develop fair and reliable AI systems. Retraining the model with balanced data helps correct biases that arise from historical inequalities, ensuring that the AI system makes decisions based on equitable criteria.
NEW QUESTION # 42
What is the 1956 Dartmouth summer research project on Al best known as?
- A. A research project to create a test for machine intelligence.
- B. A meeting focused on the founding of the Al field.
- C. A meeting focused on the impacts of the launch of the first mass-produced computer.
- D. A research project on the impacts of technology on society.
Answer: B
Explanation:
The 1956 Dartmouth summer research project on AI is best known as a meeting focused on the founding of the AI field. This conference is historically significant because it marked the formal beginning of artificial intelligence as an academic discipline. The term "artificial intelligence" was coined during this event, and it laid the foundation for future research and development in AI.
Reference: The AIGP Body of Knowledge highlights the importance of the Dartmouth Conference as a pivotal moment in the history of AI, which established AI as a distinct field of study and research.
NEW QUESTION # 43
AU.S. mortgage company developed an AI platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The AI platform will be used to automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default. Which of the following laws is NOT relevant to this use case?
- A. Fair Housing Act.
- B. Fair Credit Reporting Act.
- C. Title VII of the Civil Rights Act of 1964.
- D. Equal Credit Opportunity Act.
Answer: C
Explanation:
Title VII of the Civil Rights Act primarily governs employment discrimination and is not directly relevant to mortgage lending decisions, unlike the other listed laws.
NEW QUESTION # 44
Scenario:
An organization is planning to deploy a new internal application that uses AI to make automated decisions about individuals. This application will process personal information and may affect individuals' access to certain benefits or opportunities.
Which of the following documents must be updated to ensure transparency?
- A. The organization's website privacy notice
- B. The user privacy notice
- C. The organization's privacy policy
- D. The organization's acceptable use policy
Answer: B
Explanation:
The correct answer is D. Transparency obligations under data protection laws, such as GDPR and most AI governance frameworks, require that users whose data is being processed be directly informed.
From the AIGP ILT Guide (Privacy Module):
"The user privacy notice must be updated to explain the nature of automated processing, the logic involved, and the significance and consequences for the data subject." Also, per AI Governance in Practice Report 2024 (Part III):
"Transparency obligations apply throughout the lifecycle of AI... Individuals must be informed about automated decision-making and profiling that may impact them." Unlike internal policies or general privacy notices, the user privacy notice provides direct transparency to the individual data subjects affected by AI processing.
NEW QUESTION # 45
A US company has developed an AI system, CrimeBuster 7909, that collects information about incarcerated individuals that predicts whether someone is likely to commit another crime if released from prison. When considering expanding to the EU market, this type of technology would:
- A. Require the application of privacy enhancing technologies.
- B. Be banned under the EU AI Act.
- C. Require the company to register the tool with the EU database.
- D. Be subject to approval by the relevant EU authority.
Answer: B
NEW QUESTION # 46
What is the most important reason to document the results of AI testing?
- A. To create a verifiable audit trail.
- B. To identify areas for red-teaming focus.
- C. To support post-deployment maintenance.
- D. To limit the need for future testing cycles.
Answer: A
Explanation:
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 Report2025:
"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.
NEW QUESTION # 47
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an Al system?
- A. Risk to users.
- B. Cost of alternative mechanisms.
- C. Industry practice.
- D. Feasibility.
Answer: B
Explanation:
When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing user rights and ethical considerations in AI.
NEW QUESTION # 48
MULTI-SELECT
Please 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. Regulatory compliance.
- B. Ethical design.
- C. Ethical testing.
- D. System documentation.
- E. Technical performance.
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 Report2025:
"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.
NEW QUESTION # 49
Testing data is defined as a subset of data that is used to:
- A. Evaluate a model's handling of randomized edge cases.
- B. Provide a robust evaluation of a final model.
- C. Asses a model's on-going performance in production.
- D. Enable a model to discover and learn patterns.
Answer: B
Explanation:
Testing data is used to provide an unbiased evaluation of the final model's performance before deployment.
NEW QUESTION # 50
Scenario:
A large multinational organization is rolling out a company-wide AI governance initiative. To build awareness and support adoption, they are evaluating different ways to train employees and stakeholders across departments, including legal, technical, marketing, and customer-facing roles.
Which of the following typical approaches is a largeorganization leastlikely touse to responsibly train stakeholders on AI terminology, strategy and governance?
- A. Providing role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model
- B. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture
- C. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact
- D. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems
Answer: D
Explanation:
The correct answer isA. While educating technical staff is important, expectingall technical employees to be retooled as AI developersis unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should berole-specificand align with the individual's function and responsibilities...
Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report2025supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus,broad development training for all technical employeesis the least practical and least likely approach.
NEW QUESTION # 51
Each of the following actors are typically engaged in the AI development life cycle EXCEPT:
- A. Data architects.
- B. Socio-cultural and technical experts.
- C. Legal and privacy governance experts.
- D. Government regulators.
Answer: D
Explanation:
Government regulators oversee compliance but are not typically involved directly in the AI development life cycle.
NEW QUESTION # 52
When assessing the success of an AI system in meeting its objectives, which of the following approaches best aligns with the requirement to ensure a comprehensive evaluation while avoiding automation bias?
- A. Focus on user feedback about the AI system's performance to directly measure the system's effectiveness in meeting the objectives.
- B. Rely on the AI system's output to determine if the goals were achieved as this will provide a reliable and objective measure.
- C. Evaluate and align pre-defined benchmarks which will provide evidence of having achieved your system goals.
- D. Conduct a thorough review that includes the AI system's input and output data, human interpretation, and potential secondary or unintended outputs.
Answer: D
NEW QUESTION # 53
Please select 3 of the 5 options below. No partial credit will be given.
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT?
- A. Speed and scale.
- B. Autonomy.
- C. Automation.
- D. Superintelligence.
- E. Adaptability.
Answer: A,C,D
Explanation:
AI governance frameworks consistently define the"unique characteristics"of AI that create novel governance challenges.
Across the OECD AI Principles, NIST AI RMF, ISO/IEC 42001, and the EU AI Act, the key characteristics requiring governance are:
* Autonomy- AI systems act without direct human intervention and take context-dependent decisions.
* Adaptability- AI systems learn, evolve, and update their behavior over time, making outputs non- deterministic.
These traits fundamentally distinguish AI from traditional software and shape all major governance activities (risk assessment, monitoring, assurance, accountability, alignment, etc.).
Below is why each option is correct or incorrect:
A). Autonomy - NOT selected
Reason:
Autonomy is repeatedly cited as one of the defining characteristics that necessitates governance.
NIST AI RMF references the risks ofautonomous behavior.
ISO/IEC 42001 emphasizes governance controls for "systems operating with varying levels of autonomy." Thus,Autonomy IS a unique characteristic, so it isnotan answer.
B). Automation - SELECTED
Reason:
"Automation" is not a unique AI property. Automation predates AI and applies to robotics, scripts, business process automation, etc.
AI governance literature doesnotclassify automation as a unique AI-specific characteristic.
Automation is aresultof AI, but not adistinguishing property.
Therefore,Automation is correctly selected as NOT a unique AI characteristic.
C). Adaptability - NOT selected
Reason:
Adaptability (systems that update, learn, or change behavior) is recognized universally as a core distinctive trait of AI.
EU AI Act emphasizes governance requirements for "adaptive systems."
NIST AI RMF highlights "learning and emergent behavior."
Thus,Adaptability IS a unique characteristic, so it shouldnotbe chosen as an exception.
D). Speed and scale - SELECTED
Reason:
While AI can operate at high speed and scale,these are not unique characteristics of AI.
Traditional computing systems, cloud workloads, and automation pipelines operate at similar scale.
AI governance documents mention speed/scale as arisk amplifier, not a defining characteristic.
Therefore,Speed and scaleshould be selected.
E). Superintelligence - SELECTED
Reason:
"Superintelligence" is not a characteristic of current AI systems and isnot referenced as a governance-relevant attributein mainstream policy documents (OECD, NIST, ISO, EU AI Act).
It is speculative rather than a defining feature.
Therefore, it is correctly chosen as NOT a unique characteristic requiring governance today.
NEW QUESTION # 54
Which of the following are not considered biometric data under U.S. privacy laws?
- A. Iris scans
- B. Keystroke dynamics
- C. Walking gait
- D. GPS location of a user's fitness watch
Answer: D
Explanation:
The correct answer is D. GPS location data is not biometric data-it is considered geolocation data, which is personal data but not biometric under most U.S. laws.
From the AIGP ILT Guide (Data Privacy Module):
"Biometric data includes measurable biological or behavioral characteristics such as iris scans, facial recognition, voice prints, and keystroke patterns when used for identification." AI Governance in Practice Report 2024 (Privacy and Data Protection section):
"Location data, while sensitive, is not considered biometric unless it's tied to a uniquely identifying biological trait." Thus, GPS location data, while potentially sensitive, is not classified as biometric.
NEW QUESTION # 55
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