[Oct 01, 2025] AIGP Exam Dumps - 100% Marks In AIGP Exam!
Exam Dumps Use Real Artificial Intelligence Governance Dumps With 166 Questions!
IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 63
Which of the following is an obligation of an importer of high-risk AI systems under the EU AI Act?
- A. Provide technical documentation.
- B. Affix the CE marking.
- C. Verify the Declaration of Conformity.
- D. Conduct a data protection impact assessment.
Answer: C
Explanation:
Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenot the parties responsible for affixing the CE marking or providing technical documentation-but they must verify that these have been done by the provider.
From theAI Governance in Practice Report 2024:
"Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed." (p. 34-35) Thus:
* A. Provide technical documentation- done by theprovider.
* B. Affix the CE marking-provider'sresponsibility.
* C. Verify the Declaration of Conformity-importer obligation.
* D. Conduct a DPIA- relevant under data protection laws,not requiredunder the EU AI Act for importers.
NEW QUESTION # 64
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 willgive privacy notice to individuals before using their personal data to train or test the tool.
- B. The provider willobtain and maintain insurance to cover potential claims.
- C. The provider willdefend and indemnify the company against infringement claims.
- D. The provider willwarrant that the tool will work as intended.
Answer: C
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 # 65
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 # 66
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers.
Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?
- A. A free large language model.
- B. A classic machine learning model.
- C. A subscription-based, multimodal model.
- D. A proprietary generative AI 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:
"Organizations 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.
NEW QUESTION # 67
Training data is best defined as a subset of data that is used to?
- A. Enable a model to detect and learn patterns.
- B. Detect the initial sources of biases to mitigate prior to deployment.
- C. Resemble the structure and statistical properties of production data.
- D. Fine-tune a model to improve accuracy and prevent overfitting.
Answer: A
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 # 68
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.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?
- A. Procure more data from clinical research partners.
- B. Create a bias bounty program.
- C. Perform an impact assessment.
- D. Engage a third party to perform an audit.
Answer: C
Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.
NEW QUESTION # 69
Which of the following steps occurs in the design phase of the Al life cycle?
- A. Risk impact estimation.
- B. Data augmentation.
- C. Performance evaluation.
- D. Model explainability.
Answer: A
Explanation:
Risk impact estimation occurs in the design phase of the AI life cycle. This step involves evaluating potential risks associated with the AI system and estimating their impacts to ensure that appropriate mitigation strategies are in place. It helps in identifying and addressing potential issues early in the design process, ensuring the development of a robust and reliable AI system. Reference: AIGP Body of Knowledge on AI Design and Risk Management.
NEW QUESTION # 70
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact Al system?
- A. Upon initial deployment of the system.
- B. Upon release of a new version of the system.
- C. When the algorithmic impact assessment has been completed.
- D. When use of the system causes or is likely to cause material harm.
Answer: A
Explanation:
According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.
NEW QUESTION # 71
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?
- A. Explainability of results.
- B. Diversity of data sources.
- C. Internal governance needs.
- D. Organizational accountability.
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 # 72
What type of organizational risk is associated with Al's resource-intensive computing demands?
- A. Third-party risk.
- B. Security risk.
- C. Environmental risk.
- D. People risk.
Answer: C
Explanation:
AI's resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.
NEW QUESTION # 73
A company initially intended to use a large data set containing personal information to train an Al model.
After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?
- A. Differential privacy.
- B. Federated learning.
- C. Anonymization.
- D. Pseudonymization.
Answer: C
Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.
NEW QUESTION # 74
All of the following are included within the scope of post-deployment Al maintenance EXCEPT?
- A. Defining thresholds to conduct new impact assessments.
- B. Evaluating the need for an audit under certain standards.
- C. Ensuring that all model components are subject a control framework.
- D. Dedicating experts to continually monitor the model output.
Answer: A
Explanation:
Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity.
Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing AI lifecycle management and post-deployment activities.
NEW QUESTION # 75
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?
- A. To evaluate the reliability of the Al system.
- B. To determine the explainability of the Al system.
- C. To comply with applicable law.
- D. To assist the fairness of the Al system.
Answer: D
Explanation:
The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.
NEW QUESTION # 76
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?
- A. Accountable and Transparent.
- B. Tested and Effective.
- C. Secure and Resilient.
- D. Explainable and Interpretable.
Answer: B
Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.
NEW QUESTION # 77
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
Which Al risk would NOT have been identified during the procurement process based on the categories of information requested by the third-party consultant?
- A. Accuracy.
- B. Discrimination.
- C. Security.
- D. Explainability.
Answer: C
Explanation:
The AI risk that would not have been identified during the procurement process based on the categories of information requested by the third-party consultant is security. The consultant focused on accuracy rates, diversity of training data, and system functionality, which pertain to performance and fairness but do not directly address the security aspects of the AI system. Security risks involve ensuring that the system is protected against unauthorized access, data breaches, and other vulnerabilities that could compromise its integrity. Reference: AIGP Body of Knowledge on AI Security and Risk Management.
NEW QUESTION # 78
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?
- A. Reinforced.
- B. Reliable.
- C. Resilient.
- D. Robust.
Answer: C
Explanation:
An AI system that maintains its level of performance within defined acceptable limits despite real-world or adversarial conditions is described as resilient. Resilience in AI refers to the system's ability to withstand and recover from unexpected challenges, such as cyber-attacks, hardware failures, or unusual input data. This characteristic ensures that the AI system can continue to function effectively and reliably in various conditions, maintaining performance and integrity. Robustness, on the other hand, focuses on the system's strength against errors, while reliability ensures consistent performance over time. Resilience combines these aspects with the capacity to adapt and recover.
NEW QUESTION # 79
Which risk management framework/guide/standard focuses on value-based engineering methodology?
- A. ISO 31000 Guidelines (Risk Management).
- B. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
- C. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for Al Systems.
- D. ISO/IEC Guide 51 (Safety).
Answer: B
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 # 80
Which of the following Al uses is best described as human-centric?
- A. Autonomous robots are used to move products within a warehouse, allowing human workers to reduce physical strain and alleviate monotony.
- B. Machine learning is used for demand forecasting and inventory management, ensuring that consumers can find products they want when they want them.
- C. Pattern recognition algorithms are used to improve the accuracy of weather predictions, which benefits many industries and everyday life.
- D. Virtual assistants are used adapt educational content and teaching methods to individuals, offering personalized recommendations based on ability and needs.
Answer: D
Explanation:
Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.
Reference: AIGP BODY OF KNOWLEDGE, sections on trustworthy AI and human-centric AI principles.
NEW QUESTION # 81
What is the best reason for a company adopt a policy that prohibits the use of generative Al?
- A. Avoid needing to identify and hire qualified resources.
- B. Avoid the time necessary to train employees on acceptable use.
- C. Avoid using technology that cannot be monetized.
- D. Avoid accidental disclosure to its confidential and proprietary information.
Answer: D
Explanation:
The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company's competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.
NEW QUESTION # 82
Scenario:
A U.S.-based AI governance professional is evaluating resources from the National Institute of Standards and Technology (NIST) to guide the organization's AI risk assessment strategy. They are particularly interested in programs focused on assessing AI-specific impacts.
The main purpose of NIST's Assessing Risks and Impacts of AI (ARIA) program is to:
- A. Promote interoperability across AI systems
- B. Offer a regulatory sandbox for risk reporting
- C. Pilot new standards for AI red-teaming
- D. Provide a suite of resources to manage risks
Answer: D
Explanation:
The correct answer is A. The ARIA program by NIST is explicitly designed to support stakeholders in understanding and managing the risks and impacts of AI systems.
From the AIGP ILT Guide - U.S. Risk Frameworks Module:
"NIST's ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use." Also cited in the AI Governance in Practice Report 2024 (Frameworks Section):
"ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications." ARIA is not a red-teaming or sandbox program-it's an assessment and governance resource.
NEW QUESTION # 83
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