Pass Exam Questions Efficiently With AIGP Questions (2026)
AIGP Questions - Truly Beneficial For Your IAPP Exam
IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 56
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 acceptable use policy
- B. The organization's privacy policy
- C. The user privacy notice
- D. The organization's website privacy notice
Answer: C
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 # 57
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?
- A. Office of Science and Technology Policy (OSTP).
- B. National Science and Technology Council (NSTC).
- C. Department of Homeland Security (DHS).
- D. National Institute of Standards and Technology (NIST).
Answer: D
Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.
NEW QUESTION # 58
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT?
- A. Conducting a conformity assessment.
- B. Retaining system-generated logs for at least six months.
- C. Publishing a detailed report on the training data used.
- D. Conducting post-market monitoring.
Answer: C
Explanation:
The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.
From theAI Governance in Practice Report 2024:
"It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations." (p. 34)
"To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle."
"Conducting post-market monitoring" and "conformity assessments" areexplicitrequirements for high-risk systems. (p. 34-35) However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.
* A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.
NEW QUESTION # 59
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company 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 to a human underwriter for final review.
The company 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. They have 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, the company realizes that the LLM declines a higher percentage of women's applications.
During the first month when the company monitors the model for bias, it is most important to:
- A. Analyze the quality of the training and testing data.
- B. Document the results of final decisions made by the human underwriter.
- C. Provide regular awareness training.
- D. Continue disparity testing.
Answer: D
Explanation:
During initial production monitoring, the most important action is to continue disparity testing to detect and quantify whether the model is producing biased outcomes across protected groups.
This directly aligns with the goal of evaluating fairness, accuracy, and reliability, enabling the organization to identify and respond to discriminatory patterns early.
NEW QUESTION # 60
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. Title VII of the Civil Rights Act of 1964.
- B. Fair Credit Reporting Act.
- C. Fair Housing Act.
- D. Equal Credit Opportunity Act.
Answer: A
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 # 61
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. Seek input from data scientists.
- B. Audit the model.
- C. Retrain the model.
- D. Communicate necessary updates to your users.
Answer: B
Explanation:
When a third-party developer releases a new model version, the deployer must first audit the updated model to determine whether the changes introduce new risks, alter performance, or affect compliance obligations before considering deployment or communicating updates.
NEW QUESTION # 62
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT:
- A. Gradient descent, which initially sets weights to arbitrary values, and then at each step changes them.
- B. Momentum, which improves the convergence speed and stability of neural network training.
- C. Autoregression, which analyzes and makes predictions about time-series data.
- D. Backpropagation, which starts from the last layer working backwards.
Answer: C
Explanation:
Autoregression is a statistical modeling technique for time-series analysis, not a weight optimization technique used in deep learning neural networks.
NEW QUESTION # 63
All of the following may be permissible uses of an Al system under the EU Al Act EXCEPT?
- A. To promote equitable distribution of welfare benefits.
- B. To manage border control.
- C. To implement social scoring.
- D. To detect an individual's intent for law enforcement purposes.
Answer: C
Explanation:
The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual's intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.
NEW QUESTION # 64
A hospital implements an AI system to assist doctors in diagnosing diseases based on historical patient data. Which one of the following model types best describes this system?
- A. Statistical.
- B. Deterministic.
- C. Probabilistic.
- D. Inference.
Answer: C
Explanation:
The AI system uses historical patient data to assess probabilities and uncertainties in diagnosing diseases, which aligns with a probabilistic model.
NEW QUESTION # 65
Each of the following actors are typically engaged in the AI development life cycle EXCEPT:
- A. Government regulators.
- B. Legal and privacy governance experts.
- C. Data architects.
- D. Socio-cultural and technical experts.
Answer: A
Explanation:
Government regulators oversee compliance but are not typically involved directly in the AI development life cycle.
NEW QUESTION # 66
An artist has been using an Al tool to create digital art and would like to ensure that it has copyright protection in the United States.
Which of the following is most likely to enable the artist to receive copyright protection?
- A. Provide a log of the prompts the artist used to generate the images.
- B. Update the images in a creative way to demonstrate that it is the artist's.
- C. Ensure the tool was trained using publicly available content.
- D. Obtain a representation from the Al provider on how the tool works.
Answer: B
Explanation:
For the artist to receive copyright protection, the most effective approach is to demonstrate that the final artwork includes sufficient creative input by the artist. By updating or altering the images in a way that reflects the artist's personal creativity, the artist can claim originality, which is a core requirement for copyright protection under U.S. law. The other options do not directly address the originality and creative input required for copyright. This is highlighted in the sections on copyright protection in the IAPP AIGP Body of Knowledge.
NEW QUESTION # 67
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT:
- A. Data loss.
- B. Model drift.
- C. Feature drift.
- D. System cost.
Answer: D
Explanation:
System cost is an operational concern but not directly related to the functional performance of the deployed model, unlike feature drift, model drift, and data loss.
NEW QUESTION # 68
In the machine learning context, feature engineering is the process of:
- A. Creating learning schema for a model to apply.
- B. Developing guidelines to train and test a model.
- C. Extracting attributes and variables from raw data.
- D. Converting raw data into clean data.
Answer: C
Explanation:
Feature engineering involves extracting and transforming relevant attributes or variables from raw data to improve model performance.
NEW QUESTION # 69
In the machine learning context, feature engineering is the process of?
- A. Creating learning schema for a model apply.
- B. Developing guidelines to train and test a model.
- C. Extracting attributes and variables from raw data.
- D. Converting raw data into clean data.
Answer: C
Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.
NEW QUESTION # 70
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company 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 to a human underwriter for final review.
The company 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. They have 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, the company realizes that the LLM declines a higher percentage of women's applications.
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide:
- A. Detailed terms of service.
- B. An opt-out mechanism.
- C. Customer service support.
- D. A transparency notice.
Answer: D
Explanation:
Providing a transparency notice informs customers about how the AI model's parameters affect underwriting decisions, meeting regulatory and ethical requirements for explainability.
NEW QUESTION # 71
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