AIGP Free Study Guide! with New Update 166 Exam Questions

Get up-to-date Real Exam Questions for AIGP UPDATED [2025]

NEW QUESTION # 37
The most important factor in ensuring fairness when training an Al system is?

• A. The model accuracy and scale.
• B. The data labeling and classification.
• C. The data attributes and variability.
• D. The architecture and model selection.

Answer: C

Explanation:
Ensuring fairness when training an AI system largely depends on the data attributes and variability. This involves having a diverse and representative dataset that accurately reflects the population the AI system will serve. Fairness can be compromised if the data is biased or lacks variability, as the model may learn and perpetuate these biases. Diverse data attributes ensure that the model learns from a wide range of examples, reducing the risk of biased predictions. Reference: AIGP Body of Knowledge on Ethical AI Principles and Data Management.

NEW QUESTION # 38
You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative Al tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative Al tool is an example of what is commonly called?

• A. Prompt injection.
• B. Model collapse.
• C. Overfitting.
• D. Hallucination.

Answer: D

Explanation:
In the context of AI, particularly generative models, "hallucination" refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.

NEW QUESTION # 39
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, which of the following steps is most important in gathering the data from the clinical research partner?

• A. Review the terms of use.
• B. Perform a privacy impact assessment.
• C. Segregate the data sets.
• D. Combine only anonymized data.

Answer: A

Explanation:
Reviewing the terms of use is essential when gathering data from a clinical research partner. This step ensures that the healthcare network complies with all legal and contractual obligations related to data usage. It addresses data ownership, usage limitations, consent requirements, and privacy obligations, which are critical to maintaining ethical standards and avoiding legal repercussions. This review helps ensure that the data is used in a manner consistent with the agreements made and the regulatory environment, which is fundamental for lawful and ethical AI development. Reference: AIGP Body of Knowledge on Legal and Regulatory Considerations.

NEW QUESTION # 40
In the machine learning context, feature engineering is the process of?

• A. Developing guidelines to train and test a model.
• B. Extracting attributes and variables from raw data.
• C. Creating learning schema for a model apply.
• D. Converting raw data into clean data.

Answer: B

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 # 41
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. Privacy impact assessments
• C. Transfer risk assessments
• D. Penetration testing

Answer: B

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 # 42
What is the primary purpose of an AI impact assessment?

• A. To escalate the findings to the appropriate owner(s)
• B. To identify and measure the benefits of an AI system
• C. To determine whether a conformity assessment is needed
• D. To anticipate and manage the potential risks and harms of an AI system

Answer: D

Explanation:
The correct answer is D. AI Impact Assessments are primarily used to identify and manage risks and harms associated with AI systems.
From the AIGP Body of Knowledge:
"The goal of an AI impact assessment is to ensure that risks are identified, evaluated, and mitigated prior to or during development and deployment." As further confirmed in the AI Governance in Practice Report 2024 (Part III):
"Risk-based tools like DPIAs and Algorithmic Impact Assessments help identify potential risks to individuals and society, enabling organizations to implement mitigation plans and safeguards." While benefits may be noted in such assessments, the core objective is to manage risks and promote responsible AI.

NEW QUESTION # 43
What is the most significant risk of deploying an AI model that can create realistic images and videos?

• A. Output cannot be protected.
• B. Security breaches.
• C. Downstream harms.
• D. Copyright infringement.

Answer: C

Explanation:
The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such as deepfakes, misinformation, reputational damage, and erosion of trust.
From theAI Governance in Practice Report 2024:
"With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful." (p. 55-56)

NEW QUESTION # 44
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent.
The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
If XYZ does not deploy and use the Al hiring tool responsibly in the United States, its liability would likely increase under all of the following laws EXCEPT?

• A. Anti-discriminationlaws.
• B. Privacy laws.
• C. Accessibility laws.
• D. Product liability laws.

Answer: D

Explanation:
In the United States, the use of AI hiring tools must comply with anti-discrimination laws, accessibility laws, and privacy laws to avoid increasing liability. Anti-discrimination laws (A) ensure that hiring practices do not unlawfully discriminate against protected classes. Accessibility laws (C) require that hiring tools are accessible to all applicants, including those with disabilities. Privacy laws (D) govern the handling of personal data during the hiring process. Product liability laws (B), however, typically apply to the safety and reliability of physical products and would not generally increase liability specifically related to the responsible use of AI hiring tools in the employment context.

NEW QUESTION # 45
Scenario:
An enterprise is evaluating multiple third-party generative AI tools to integrate into its platform. As part of its AI governance policy, it is assessing the most effective methods to reduce risks related to bias, data misuse, and liability when using third-party solutions.
All of the following are commonly adopted processes and policies in reducing potential risks introduced by third-party AI tools or applications EXCEPT:

• A. Requiring new use cases of the generative AI tools or applications to be reviewed and approved by the generative AI governance body
• B. Including clauses in the procurement agreement for buyers of generative AI tools to put certain liabilities on the tool supplier
• C. Allowing publicly available information and personally identifiable information (PII) to be incorporated into the prompt
• D. Requiring an independent third-party bias audit for third-party generative AI tools

Answer: C

Explanation:
The correct answer is B. Allowing PII to be freely entered into prompts without safeguards is considered a major privacy and security risk and is not a responsible governance practice.
From the AIGP ILT Guide - Generative AI & Third-Party Risk Management:
"Use of personal or sensitive information in AI prompts can result in unintended exposure, regulatory breaches, and downstream liability." The AI Governance in Practice Report 2024 highlights:
"PII should be minimized or protected by design. Prompt engineering should prevent entry of personally identifiable data unless legally and technically safeguarded." A, C, and D are established best practices under responsible AI procurement and use.

NEW QUESTION # 46
A UK company has designed a facial recognition model to support border control.
The EU AI Act would apply to the model in all of the following situations EXCEPT if?

• A. The model is deployed at an EU border checkpoint.
• B. The model was released under an open source license.
• C. The model is deployed at UK border checkpoints.
• D. The model was trained by an EU company.

Answer: C

Explanation:
TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systems impact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or used entirely outside of the EU-such as in theUK, unless they affect the EU market or individuals.
From theAI Governance in Practice Report 2024:
"The act imposes regulatory obligations... depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems." (p. 7)
"The EU AI Act is the world's first comprehensive AI regulation... requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market." (p. 7-8) Thus:
* A. Open source releasedoes not exclude applicability if deployed in the EU.
* B. Deployment at an EU borderclearly invokes jurisdiction.
* D. Training by an EU companycreates jurisdictional links.
* C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.

NEW QUESTION # 47
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

• A. To enable and encourage participation by external stakeholders.
• B. To determine responsibility for allocating budgetary resources.
• C. To establish diverse, equitable and inclusive processes.
• D. To empower and train appropriate cross-functional teams.

Answer: D

Explanation:
The NIST AI Risk Management Framework's Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross- functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization's AI principles and ethical standards. Reference:
NIST AI Risk Management Framework documentation, Govern function section.

NEW QUESTION # 48
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
All of the following may be copyright risks from teachers using generative Al to create course content EXCEPT?

• A. Students must expressly consent to this use of generative Al.
• B. Content created by an LLM may be protectable under U.S. intellectual property law.
• C. Generative Al is generally trained using intellectual property owned by third parties.
• D. Generative Al often creates content without attribution.

Answer: A

Explanation:
All of the options listed may pose copyright risks when teachers use generative AI to create course content, except for students must expressly consent to this use of generative AI. While obtaining student consent is essential for ethical and privacy reasons, it does not directly relate to copyright risks associated with the creation and use of AI-generated content.
Reference: The AIGP Body of Knowledge discusses the importance of addressing intellectual property (IP) risks when using AI-generated content. Copyright risks are typically associated with the use of third-party data and the lack of attribution, rather than the consent of users.

NEW QUESTION # 49
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
All of the following are potential negative consequences created by using the Al tool when making hiring decisions EXCEPT?

• A. Intellectual property infringement.
• B. Reputational harm.
• C. Civil rights violations.
• D. Discriminatory treatment.

Answer: A

Explanation:
The potential negative consequences of using an AI tool in hiring include reputational harm (A), civil rights violations (B), and discriminatory treatment (C). These issues stem from biases in the AI system or its misuse, which can lead to unfair hiring practices and legal liabilities. Intellectual property infringement (D) is not a typical consequence of using AI in hiring, as it relates to the unauthorized use of protected intellectual property, which is not directly relevant to the hiring process or the potential biases within AI tools.

NEW QUESTION # 50
Scenario:
An organization is building a compliance program to ensure responsible AI deployment. It aims to align operations with AI risk frameworks and mitigate legal, ethical, and operational risks, while still promoting innovation.
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible AI?

• A. Meeting with and obtaining approval from senior management
• B. Employing a new software platform to modernize existing compliance processes across the organization
• C. Launching a survey to understand the concerns and interests of potentially impacted stakeholders
• D. Consulting experts to consider the ethical principles underpinning the use of AI within the organization

Answer: B

Explanation:
The correct answer is D. While modernization through software may support efficiency, it is not a foundational or essential component of designing an integrated strategy.
From the AI Governance in Practice Report 2024:
"Integrated strategies rely on senior management support, ethical reviews, and stakeholder engagement... The use of tools and platforms may come later as an operational enhancement." Also confirmed in AIGP Body of Knowledge:
"Key components of a governance framework include leadership buy-in, ethical analysis, and stakeholder input. Tools are supporting elements-not strategic drivers."

NEW QUESTION # 51
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?

• A. The human oversight measures employed.
• B. How an individual may contest a decision.
• C. The fact that an Al system is being used.
• D. The location(s) where data is stored.

Answer: D

Explanation:
Under the EU AI Act, organizations that develop and deploy high-risk AI systems are required to provide several key disclosures to ensure transparency and accountability. These include the human oversight measures employed, how individuals can contest decisions made by the AI system, and informing individuals that an AI system is being used. However, there is no specific requirement to disclose the exact locations where data is stored. The focus of the Act is on the transparency of the AI system's operation and its impact on individuals, rather than on the technical details of data storage locations.

NEW QUESTION # 52
What is the 1956 Dartmouth summer research project on Al best known as?

• A. A research project on the impacts of technology on society.
• B. A meeting focused on the impacts of the launch of the first mass-produced computer.
• C. A meeting focused on the founding of the Al field.
• D. A research project to create a test for machine intelligence.

Answer: C

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 # 53
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The frameworks that would be most appropriate for XYZ's governance needs would be the NIST Al Risk Management Framework and?

• A. NIST Cyber Security Risk Management Framework (CSF 2.0).
• B. IEEE Ethical System Design Risk Management Framework (IEEE 7000-21).
• C. Human Rights, Democracy, and Rule of Law Impact Assessment (HUDERIA).
• D. NIST Information Security Risk (NIST SP 800-39).

Answer: B

Explanation:
The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) would be most appropriate for XYZ Corp's governance needs in addition to the NIST AI Risk Management Framework. The IEEE framework specifically addresses ethical concerns during system design, which is crucial for ensuring the responsible use of AI in hiring. It complements the NIST framework by focusing on ethical risk management, aligning well with XYZ Corp's goals of deploying AI responsibly and mitigating associated risks.

NEW QUESTION # 54
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 large organization least likely to use to responsibly train stakeholders on AI terminology, strategy and governance?

• A. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact
• B. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture
• C. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems
• D. Providing role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model

Answer: C

Explanation:
The correct answer is A. While educating technical staff is important, expecting all technical employees to be retooled as AI developers is unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should be role-specific and 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 Report 2024 supports 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 employees is the least practical and least likely approach.

NEW QUESTION # 55
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?

• A. Organizational accountability.
• B. Explainability of results.
• C. Internal governance needs.
• D. Diversity of data sources.

Answer: C

Explanation:
An AI system'sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.
From theAI Governance in Practice Report 2024:
"There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization's role, footprint, broader risk-governance profile and maturity." (p. 4)
"AI 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.

NEW QUESTION # 56
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?

• A. Model drift.
• B. System cost.
• C. Data loss.
• D. Feature drift.

Answer: B

Explanation:
When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

NEW QUESTION # 57
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
The data processed by the AI system would be classified as:

• A. Special category data, if it can be used to uniquely identify a person
• B. Non-sensitive personal data, since it does not reveal information about health, gender or race
• C. Non-personal data, as long as it is not linked to a user ID
• D. Organizational data, since it is part of the authentication process

Answer: A

Explanation:
The correct answer is D. Keystroke dynamics, used to identify individuals, fall under biometric data, which is a special category of personal data under the GDPR and other frameworks.
From the AI Governance in Practice Report 2024:
"Keystroke dynamics may constitute biometric data if used to uniquely identify an individual... Biometric data is classified as special category personal data and requires higher protection standards." Also reflected in ILT Participant Guide:
"Biometric data, such as facial images, voiceprints, iris scans or keystroke patterns, are treated as special category data when they are used for the purpose of uniquely identifying individuals."

NEW QUESTION # 58
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