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Preparations of AIGP Exam 2026 Artificial Intelligence Governance Unlimited 204 Questions
IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 111
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?
- A. Implementing a formal incident response plan to address incidents that may occur during system operation.
- B. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.
- C. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
- D. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
Answer: D
Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remain internal or shared with regulators.
From theAI Governance in Practice Report 2024:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)
NEW QUESTION # 112
In the context of increasing use of AI in business operations, your company seeks to update its data privacy policies. You are tasked with evaluating the current policies and proposing necessary updates to address AI-specific risks regarding protection of personal data. Which of the following would be the most effective addition to the company's data privacy policies?
- A. Request regular audits of the AI Models.
- B. Require security training to employees before using AI systems.
- C. Prohibit the use of AI tools within the company.
- D. Request final review of the policy by senior management.
Answer: A
Explanation:
Regular audits of AI models help ensure ongoing compliance with data privacy regulations and identify AI-specific risks related to personal data protection.
NEW QUESTION # 113
What is the primary purpose of an Al impact assessment?
- A. Anticipate and manage the potential risks and harms of an Al system.
- B. To define and document the roles and responsibilities of Al stakeholders.
- C. To define and evaluate the legal risks associated with developing an Al system.
- D. To identify and measure the benefits of an Al system.
Answer: A
Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability.
The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.
NEW QUESTION # 114
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 tA. 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.
Each of the following steps would support fairness testing by the compliance team during the first month in production EXCEPT?
- A. Providing the loan applicants with information about the model capabilities and limitations.
- B. Using tools to help understand factors that may account for differences in decision-making.
- C. Validating a similar level of decision-making across different demographic groups.
- D. Identifying if additional training data should be collected for specific demographic groups.
Answer: A
Explanation:
Providing the loan applicants with information about the model capabilities and limitations would not directly support fairness testing by the compliance team. Fairness testing focuses on evaluating the model's decisions for biases and ensuring equitable treatment across different demographic groups, rather than informing applicants about the model.
Reference: The AIGP Body of Knowledge outlines that fairness testing involves technical assessments such as validating decision-making consistency across demographics and using tools to understand decision factors. While transparency to applicants is important for ethical AI use, it does not contribute directly to the technical process of fairness testing.
NEW QUESTION # 115
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?
- A. Liability for mistakes.
- B. Responsibility for improvements.
- C. Ownership of data and outputs.
- 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 Report2025:
"Access 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 doesn't guarantee audit access.
NEW QUESTION # 116
An EU bank intends to launch a multi-modal AI platform for customer engagement and automated decision-making to assist with the opening of bank accounts. The platform has been subject to thorough risk assessments and testing, where it proves to be effective in not discriminating against any individual on the basis of a protected class. What additional obligations must the bank fulfill prior to deployment?
- A. The bank must subject the AI system to an adequacy decision and publish its appropriate safeguards.
- B. The bank must disclose the use of the AI system and implement suitable measures for suers to contest automated decision-making.
- C. The bank must obtain explicit consent from users under the ePrivacy Directive.
- D. The bank must disclose how the AI system works under the EU Digital Services Act.
Answer: B
Explanation:
Under EU regulations like the GDPR and AI Act, the bank must disclose AI usage and provide users with mechanisms to contest automated decisions affecting them.
NEW QUESTION # 117
Which risk management framework/guide/standard focuses on value-based engineering methodology?
- A. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
- B. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for Al Systems.
- C. ISO/IEC Guide 51 (Safety).
- D. ISO 31000 Guidelines (Risk Management).
Answer: A
Explanation:
The IEEE 7000-2021 Standard focuses on a value-based engineering methodology for addressing ethical concerns during system design. This standard guides engineers and organizations in integrating ethical considerations into the design and development processes of AI systems, ensuring that these technologies are developed responsibly and align with human values. Reference: AIGP Study Material, section on risk management frameworks and standards.
NEW QUESTION # 118
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?
- A. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
- B. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
- C. Employing a new software platform to modernize existing compliance processes across the organization.
- D. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
Answer: C
Explanation:
When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles. Reference: AIGP Body of Knowledge on AI Governance and Compliance Integration.
NEW QUESTION # 119
Scenario:
A mid-sized tech firm is building its AI governance program and is exploring ISO/IEC standards that could support consistency in terminology and risk assessment processes across teams.
ISO/IEC 22989andISO/IEC 42001can be valuable resources for AI Governance professionals inall of the following ways EXCEPT:
- A. Recommending key activities to assess and manage risk: test, evaluate, verify and validate (TEVV)
- B. Addressing specific issues related to managing procurement processes with third parties that provide or develop AI systems for their organization
- C. Establishing terminology and describing concepts so that governance team members can communicate with diverse parties and stakeholders from around the world
- D. Being applicable to organizations of any size and industry seeking to use AI responsibly and effectively in their design processes, information systems and controls
Answer: B
Explanation:
The correct answer isC. ISO/IEC 22989 and 42001 focus onterminology, risk, and management systems, butdo not specifically address procurement-related concerns with third-party vendors.
From the AIGP Body of Knowledge - Standards Section:
"ISO/IEC 22989 defines terminology and foundational concepts. ISO/IEC 42001 provides a management system standard for AI. They are not procurement-focused documents." Also confirmed in the AI Governance in Practice Report2025:
"These standards help establish common language and risk governance procedures. Procurement governance typically falls under separate frameworks or sector-specific guidance." Thus,procurement governance (Option C)is not a central use case for these standards.
NEW QUESTION # 120
Machine learning is best described as a type of algorithm by which?
- A. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
- B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
- C. Systems can automatically improve from experience through predictive patterns.
- D. Systems can mimic human intelligence with the goal of replacing humans.
Answer: C
Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.
NEW QUESTION # 121
Scenario:
An organization wants to leverage its existing compliance structures to identify AI-specific risks as part of an ongoing data governance audit.
Which of the following compliance-related controls within an organization is most easily adapted to identify AI risks?
- A. Privacy training
- B. Transfer risk assessments
- C. Privacy impact assessments
- D. Penetration testing
Answer: C
Explanation:
The correct answer is D - Privacy impact assessments (PIAs). These are directly adaptable for identifying risks in AI systems, particularly around data usage, bias, and individual impacts.
From the AIGP ILT Guide - Risk Management Module:
"PIAs and DPIAs are existing tools used in privacy compliance that can be extended to evaluate the risks of AI, including fairness, explainability, and legality." AI Governance in Practice Report 2024 further explains:
"Organizations can adapt privacy impact assessments to evaluate the ethical, legal, and technical risks posed by AI systems. They provide a structured and recognized method." PIAs are preferable over general security practices (like pen testing) which do not address algorithmic bias or legal compliance directly.
NEW QUESTION # 122
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?
- A. Require the procurement and deployment teams to agree upon the AI tool
- B. Test the AI tool pre- and post-deployment
- C. Ensure the vendor provides indemnification for the AI tool
- D. Continue to require the company's hiring personnel to manually screen all applicants
Answer: B
Explanation:
The correct answer isA. Pre- and post-deployment testing ensuresbias, accuracy, and fairnessare evaluated and corrected as needed, which isessential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report2025(Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).
NEW QUESTION # 123
Training data is best defined as a subset of data that is used to?
- A. Resemble the structure and statistical properties of production data.
- B. Fine-tune a model to improve accuracy and prevent overfitting.
- C. Enable a model to detect and learn patterns.
- D. Detect the initial sources of biases to mitigate prior to deployment.
Answer: C
Explanation:
Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.
NEW QUESTION # 124
A company has developed a proprietary AI model that analyzes consumer online behavior and predicts what prices consumers would be willing to pay for certain products, so that retailers may modify pricing accordingly. To test the model, the company has:
- Performed an impact assessment.
- Conducted repeatability tests.
- Exposed the model to edge cases and potential malicious input.
- Conducted adversarial testing to ID security threats.
- Assessed and mitigated discrimination risks.
Which additional responsible AI principle has the company failed to assess?
- A. Transparency.
- B. Data Integrity.
- C. Fairness.
- D. Robustness.
Answer: A
Explanation:
While the company addressed security, fairness, and robustness through testing and risk assessment, transparency, ensuring stakeholders understand how the model makes decisions, has not been explicitly assessed.
NEW QUESTION # 125
A US hospital plans to develop an AI that will review available patient data in order to propose an initial diagnosis to licensed physicians. The hospital will implement a policy that requires physicians to consider the AI proposal, but conduct their own physical examinations prior to making a final diagnosis.
An important ethical concern with this plan is?
- A. Whether the AI will have an error rate comparable to human physicians.
- B. Whether the AI was trained on a representative dataset.
- C. Whether patients will receive an economic benefit from the use of AI.
- D. Whether physicians understand how the AI works.
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:
"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.
NEW QUESTION # 126
Which of the following is a foundational characteristic of effective AI governance?
- A. Uniform policies and procedures across developer, deployer and user roles
- B. Thorough reviews of a company's public filings with experts
- C. Engagement of a cross-functional team
- D. Reliance on tested vendor management processes
Answer: C
Explanation:
The correct answer is Engagement of a cross-functional team. Effective AI governance requires collaboration among various organizational functions including legal, compliance, IT, ethics, and data science.
From the AIGP Body of Knowledge:
"AI governance cannot be siloed-it requires input and oversight from across departments... A cross- functional team ensures that ethical, technical, legal, and operational risks are all appropriately managed." Also confirmed in the ILT Participant Guide:
"Cross-functional teams allow organizations to bring in different perspectives... Legal, compliance, and technical experts must work together to ensure responsible AI outcomes."
NEW QUESTION # 127
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