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NEW QUESTION # 13
The White House Executive Order from November 2023 requires companies that develop dual-use foundation models to provide reports to the federal government about all of the following EXCEPT?
- A. Any environmental impact study for each dual-use foundation model.
- B. The physical and cybersecurity protection measures of their dual-use foundation models.
- C. Any current training or development of dual-use foundation models.
- D. The results of red-team testing of each dual-use foundation model.
Answer: A
Explanation:
The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.
Reference: AIGP BODY OF KNOWLEDGE, sections on compliance and reporting requirements, and the White House Executive Order of November 2023.
NEW QUESTION # 14
In the machine learning context, feature engineering is the process of?
- A. Developing guidelines to train and test a model.
- B. Creating learning schema for a model apply.
- C. Converting raw data into clean data.
- D. Extracting attributes and variables from raw data.
Answer: D
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 # 15
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. Offer a regulatory sandbox for risk reporting
- B. Promote interoperability across AI systems
- 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 # 16
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. Content created by an LLM may be protectable under U.S. intellectual property law.
- B. Generative Al is generally trained using intellectual property owned by third parties.
- C. Students must expressly consent to this use of generative Al.
- D. Generative Al often creates content without attribution.
Answer: C
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 # 17
The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?
- A. Safe and effective systems.
- B. Data privacy.
- C. Human alternatives, consideration and fallback.
- D. High-risk mitigation standards.
Answer: D
Explanation:
The White House Blueprint for an AI Bill of Rights focuses on protecting civil rights, privacy, and ensuring AI systems are safe and effective. It includes principles like data privacy (D), human alternatives (A), and safe and effective systems (C). However, it does not specifically address high-risk mitigation standards as a distinct category (B).
NEW QUESTION # 18
According to the GDPR, an individual has the right to have a human confirm or replace an automated decision unless that automated decision?
- A. Is authorized with the data subject s explicit consent.
- B. Is deemed to solely benefit the individual and includes documented legitimate interests.
- C. Is authorized by applicable Ell law and includes suitable safeguards.
- D. Is necessary for entering into or performing under a contract between the data subject and data controller.
Answer: A
Explanation:
According to the GDPR, individuals have the right to not be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. However, there are exceptions to this right, one of which is when the decision is based on the data subject's explicit consent. This means that if an individual explicitly consents to the automated decision-making process, there is no requirement for human intervention to confirm or replace the decision. This exception ensures that individuals can have control over automated decisions that affect them, provided they have given clear and informed consent.
NEW QUESTION # 19
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?
- A. Risk probability/severity.
- B. Decision analysis.
- C. Adversarial robustness.
- D. Statistical sampling.
Answer: A
Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.
NEW QUESTION # 20
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?
- A. Risk probability/severity.
- B. Decision analysis.
- C. Adversarial robustness.
- D. Statistical sampling.
Answer: A
Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance.
Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.
NEW QUESTION # 21
What is the best reason for a company adopt a policy that prohibits the use of generative Al?
- A. Avoid using technology that cannot be monetized.
- B. Avoid the time necessary to train employees on acceptable use.
- C. Avoid accidental disclosure to its confidential and proprietary information.
- D. Avoid needing to identify and hire qualified resources.
Answer: C
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 # 22
The most important factor in ensuring fairness when training an Al system is?
- A. The model accuracy and scale.
- B. The architecture and model selection.
- C. The data attributes and variability.
- D. The data labeling and classification.
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 # 23
According to the EU Al Act, providers of what kind of machine learning systems will be required to register with an EU oversight agency before placing their systems in the EU market?
- A. Al systems that are "strong" general intelligence.
- B. Al systems that are harmful based on a legal risk-utility calculation.
- C. Al systems that are high-risk.
- D. Al systems trained on sensitive personal data.
Answer: C
Explanation:
According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act's framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards.
High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act's provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting "strong" general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.
NEW QUESTION # 24
CASE STUDY
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.
To 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 deploy 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 organization continues planning the adoption of an AI tool to support hiring, but is concerned about potential bias in content generated by AI systems and how that could affect public perception.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?
- A. Continue to require the company's hiring personnel to manually screen all applicants
- B. Ensure the vendor provides indemnification for the AI tool
- C. Test the AI tool pre- and post-deployment
- D. Require the procurement and deployment teams to agree upon the AI tool
Answer: C
Explanation:
Note: This is the same scenario and question as Question 21 and thus has the same correct answer: A. It's possible this was duplicated in your original input.
Repeated for clarity:
"Testing AI tools pre- and post-deployment helps ensure they perform as expected and do not introduce bias, privacy issues, or fairness concerns. This mitigates reputational and legal risk." The AI Governance in Practice Report 2024 further reinforces:
"Ongoing monitoring and testing post-deployment allows organizations to catch and correct unintended impacts... especially important in HR and hiring contexts."
NEW QUESTION # 25
A US company has developed an Al system, CrimeBuster 9619, that collects information about incarcerated individuals to help parole boards predict 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. Be banned under the EU Al Act.
- B. Be subject approval by the relevant EU authority.
- C. Require the company to register the tool with the EU database.
- D. Require a detailed conformity assessment.
Answer: D
Explanation:
Under the EU AI Act, high-risk AI systems like CrimeBuster 9619 would require a detailed conformity assessment before being deployed in the EU market. This assessment ensures that the AI system complies with all relevant regulations and standards, addressing potential risks related to privacy, security, and discrimination. The company would not need to register the tool with the EU database (A), seek approval from an EU authority (B), or face a ban (D) as long as it meets the necessary conformity requirements.
NEW QUESTION # 26
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
- A. Determining the level of human involvement in algorithmic decision-making.
- B. Establishing communications and collaboration among stakeholders.
- C. Employing human-over-the-loop protocols for high-risk systems.
- D. Adapting the existing governance structure algorithmic decision-making.
Answer: C
Explanation:
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.
NEW QUESTION # 27
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.
Which of the following measures should XYZ adopt to best mitigate its risk of reputational harm from using the Al tool?
- A. Ensure the vendor assumes responsibility for all damages.
- B. Direct the procurement team to select the most economical Al tool.
- C. Continue to require XYZ's hiring personnel to manually screen all applicants.
- D. Test the Al tool pre- and post-deployment.
Answer: D
Explanation:
To mitigate the risk of reputational harm from using an AI hiring tool, XYZ Corp should rigorously test the AI tool both before and after deployment. Pre-deployment testing ensures the tool works correctly and does not introduce bias or other issues. Post-deployment testing ensures the tool continues to operate as intended and adapts to any changes in data or usage patterns. This approach helps to identify and address potential issues proactively, thereby reducing the risk of reputational harm. Ensuring the vendor assumes responsibility for damages (B) does not address the root cause of potential issues, selecting the most economical tool (C) may compromise quality, and continuing manual screening (D) defeats the purpose of using the AI tool.
NEW QUESTION # 28
What is the best method to proactively train an LLM so that there is mathematical proof that no specific piece of training data has more than a negligible effect on the model or its output?
- A. Transfer learning.
- B. Differential privacy.
- C. Data compartmentalization.
- D. Clustering.
Answer: B
Explanation:
Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.
Reference: AIGP BODY OF KNOWLEDGE, pages related to data privacy and security in model training.
NEW QUESTION # 29
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. Non-personal data, as long as it is not linked to a user ID
- B. Non-sensitive personal data, since it does not reveal information about health, gender or race
- C. Organizational data, since it is part of the authentication process
- D. Special category data, if it can be used to uniquely identify a person
Answer: D
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 # 30
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?
- A. System cost.
- B. Feature drift.
- C. Data loss.
- D. Model drift.
Answer: A
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 # 31
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. Employing a new software platform to modernize existing compliance processes across the organization.
- B. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
- C. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
- D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
Answer: A
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 # 32
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. When the algorithmic impact assessment has been completed.
- B. Upon release of a new version of the system.
- C. When use of the system causes or is likely to cause material harm.
- D. Upon initial deployment of the system.
Answer: D
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 # 33
Random forest algorithms are in what type of machine learning model?
- A. Symbolic.
- B. Natural language processing.
- C. Generative.
- D. Discriminative.
Answer: D
Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms.
They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks.
NEW QUESTION # 34
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