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NEW QUESTION # 67
If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as:
- A. Transparent.
- B. Reliable.
- C. Accountable.
- D. Explainable.
Answer: D
Explanation:
An AI system that can provide a rationale for its specific outputs is considered explainable because it offers understandable reasons for its decisions.
NEW QUESTION # 68
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. Including clauses in the procurement agreement for buyers of generative AI tools to put certain liabilities on the tool supplier
- B. Allowing publicly available information and personally identifiable information (PII) to be incorporated into the prompt
- C. Requiring an independent third-party bias audit for third-party generative AI tools
- D. Requiring new use cases of the generative AI tools or applications to be reviewed and approved by the generative AI governance body
Answer: B
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 # 69
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 organization's privacy policy
- C. The organization's acceptable use policy
- D. The user privacy notice
Answer: D
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 # 70
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?
- A. The location(s) where data is stored.
- B. How an individual may contest a decision.
- C. The fact that an Al system is being used.
- D. The human oversight measures employed.
Answer: A
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 # 71
What is most likely the first action that a developer takes to map, plan and scope an AI project?
- A. Perform an algorithmic impact assessment leveraging PIAs.
- B. Determine feasibility and optionality of redress.
- C. Define the business case and perform a cost/benefit analysis answering the question of "why AI?"
- D. Use a test, evaluation, verification, validation (TEVV) process.
Answer: C
Explanation:
The first action is to clearly define the business case and conduct a cost/benefit analysis to justify the use of AI and set project scope.
NEW QUESTION # 72
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 patients will receive an economic benefit from the use of AI.
- B. Whether the AI was trained on a representative dataset.
- C. Whether the AI will have an error rate comparable to human physicians.
- 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 # 73
Why is it important that conformity requirements are satisfied before an AI system is released into production?
- A. To ensure the AI system is easy for end-users to operate.
- B. To comply with legal and regulatory standards, ensuring the AI system is safe and trustworthy.
- C. To guarantee interoperability of the AI system across multiple platforms and environments.
- D. To ensure the visual design is fit for purpose.
Answer: B
Explanation:
The correct answer is D because conformity requirements are primarily intended to ensure that AI systems meet applicable legal, regulatory, and safety standards before deployment. AI governance frameworks, including the EU AI Act and international standards, require conformity assessments to verify that systems are safe, reliable, and compliant with risk management, documentation, and performance obligations. These assessments help identify and mitigate risks prior to market release, particularly for high-risk AI systems that may impact individuals' rights, health, or safety. Conformity ensures accountability, transparency, and trustworthiness, which are central principles of responsible AI governance. The other options relate to usability or technical considerations, but they do not address the pri mary purpose of conformity assessments, which is regulatory compliance and risk mitigation prior to deployment.
NEW QUESTION # 74
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. Require the company to register the tool with the EU database.
- B. Be banned under the EU Al Act.
- C. Require a detailed conformity assessment.
- D. Be subject approval by the relevant EU authority.
Answer: C
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 # 75
What is the primary purpose of an Al impact assessment?
- A. To identify and measure the benefits of an Al system.
- B. To define and document the roles and responsibilities of Al stakeholders.
- C. Anticipate and manage the potential risks and harms of an Al system.
- D. To define and evaluate the legal risks associated with developing an Al system.
Answer: C
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 # 76
What is the primary purpose of an AI impact assessment?
- A. To escalate the findings to the appropriate owner(s)
- B. To determine whether a conformity assessment is needed
- C. To identify and measure the benefits of an AI system
- 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 # 77
What is most likely the first action that a developer takes to map, plan and scope an AI project?
- A. Define the business case and perform a cost benefit analysis answering the question of why AI
- B. Perform an algorithmic impact assessment leveraging PIAs
- C. Determine feasibility and optionality of redress
- D. Use a test, evaluation, verification, validation TEVV process
Answer: A
Explanation:
The correct answer is A because the first step in any AI project lifecycle is to clearly define the business objective and justify the use of AI. AI governance frameworks emphasize that planning begins with understanding the purpose, value, and necessity of the system before moving into design or risk assessment stages. Establishing a business case ensures alignment with organizational goals, identifies expected benefits, and evaluates whether AI is the appropriate solution. This step corresponds to the planning phase of the AI lifecycle, where objectives and intended outcomes are documented. In contrast, TEVV processes occur during development and testing, while impact assessments and redress considerations arise later as part of risk management and governance. Starting with a clear "why AI" foundation ensures responsible, efficient, and goal-oriented system development.
NEW QUESTION # 78
Which risk management framework/guide/standard focuses on value-based engineering methodology?
- A. ISO/IEC Guide 51 (Safety).
- 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 31000 Guidelines (Risk Management).
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 # 79
Which of the following is an example of a high-risk application under the EU AI Act?
- A. A customer service chatbot tool.
- B. A resume scanning tool that ranks applicants.
- C. A government-run social scoring tool.
- D. An AI-enabled inventory management tool.
Answer: B
NEW QUESTION # 80
Which of the following best defines an "AI model"?
- A. A system that applies defined rules to execute tasks.
- B. A corpus of data which an AI algorithm analyzes to make predictions.
- C. A system of controls that is used to govern an AI algorithm.
- D. A program that has been trained on a set of data to find patterns within the data.
Answer: D
Explanation:
An AI model is a program trained on data to identify patterns and make predictions or decisions based on that training.
NEW QUESTION # 81
To maintain fairness in a deployed system, it is most important to?
- A. Monitor for data drift that may affect performance and accuracy.
- B. Optimize computational resources and data to ensure efficiency and scalability.
- C. Protect against loss of personal data in the model.
- D. Detect anomalies outside established metrics that require new training data.
Answer: A
NEW QUESTION # 82
What is the most important reason for requiring collaboration among cross-functional stakeholder teams during the AI development lifecycle?
- A. To establish accountability
- B. To establish a user-centric design.
- C. To minimize the involvement of third parties.
- D. To minimize potential liability to users.
Answer: A
Explanation:
Collaboration among cross-functional teams ensures clear accountability across different AI development stages, improving governance and risk management.
NEW QUESTION # 83
CASE STUDY
Please use the following to answer the next question:
A local police department in the United States procured an AI 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 AI system works by surveying 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 to 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 AI 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 AI 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 AI system gives a score of at least 90% and proceed directly with an arrest.
Which AI risk would NOT have been identified during the procurement process based on the categories of information requested by the third-party consultant?
- A. Security.
- B. Discrimination.
- C. Accuracy.
- D. Explainability.
Answer: A
Explanation:
The consultant evaluated accuracy, training data diversity (related to discrimination), and system workings (explainability), but security risks were not assessed during procurement.
NEW QUESTION # 84
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?
- A. To ensure they provide transparency to applicants on the model.
- B. To provide a reminder of a right to appeal.
- C. To apply their own judgment to the initial assessment.
- D. To solicit on-going feedback on model performance.
Answer: C
Explanation:
Training underwriters ensures they understand the AI's limitations and can apply their own judgment to initial assessments, preventing over-reliance on potentially biased model outputs.
NEW QUESTION # 85
You are the chief privacy officer of a medical research company that would like to collect and use sensitive data about cancer patients, such as their names, addresses, race and ethnic origin, medical histories, insurance claims, pharmaceutical prescriptions, eating and drinking habits and physical activity.
The company will use this sensitive data to build an Al algorithm that will spot common attributes that will help predict if seemingly healthy people are more likely to get cancer. However, the company is unable to obtain consent from enough patients to sufficiently collect the minimum data to train its model.
Which of the following solutions would most efficiently balance privacy concerns with the lack of available data during the testing phase?
- A. Refocus the algorithm to patients without cancer.
- B. Extend the model to multi-modal ingestion with text and images.
- C. Utilize synthetic data to offset the lack of patient data.
- D. Deploy the current model and recalibrate it over time with more data.
Answer: C
Explanation:
Utilizing synthetic data to offset the lack of patient data is an efficient solution that balances privacy concerns with the need for sufficient data to train the model. Synthetic data can be generated to simulate real patient data while avoiding the privacy issues associated with using actual patient data. This approach allows for the development and testing of the AI algorithm without compromising patient privacy, and it can be refined with real data as it becomes available. Reference: AIGP Body of Knowledge on Data Privacy and AI Model Training.
NEW QUESTION # 86
Business A sells software that provides users with writing and grammar assistance. Business B is a cloud services provider that trains its own AI models.
* Business A has decided to add generative AI features to their software.
* Rather than create their own generative AI model, Business A has chosen to license a model from Business B:
* Business A will then integrate the model into their writing assistance software to provide generative AI capabilities.
* Business A is most concerned that its writing assistance software could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?
- A. Business A should ask Business B for detailed documentation on the generative AI model's training data and whether it contained toxic or obscene sources.
- B. Business A should test that the AI model performs as expected and meets their minimum requirements for filtering toxic or obscene text.
- C. Business A should fine-tune the AI model on user-generated text that has been verified to be appropriate.
- D. Business A should establish a user reporting feature that allows users to flag toxic or obscene text, and report any incidents to Business B.
Answer: B
Explanation:
Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.
According to theAI Governance in Practice Report 2024, organizations thatdeployAI must engage in performance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:
"Operational governance... development of: #Performance monitoring protocols to ensure systems perform adequately for their intended purposes." (p. 12)
"Product governance... includes: #System impact assessments to identify and address risk prior to product development or deployment." (p. 11) Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:
"The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance..." (p. 7) Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.
Let's now evaluate the incorrect options:
* A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.
* C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.
* D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.
Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.
NEW QUESTION # 87
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