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質問 # 32
What is the term for an algorithm that focuses on making the best choice achieve an immediate objective at a particular step or decision point, based on the available information and without regard for the longer-term best solutions?
- A. Efficient.
- B. Single-lane.
- C. Greedy.
- D. Optimized.
正解：C
解説：
A greedy algorithm is one that makes the best choice at each step to achieve an immediate objective, without considering the longer-term consequences. It focuses on local optimization at each decision point with the hope that these local solutions will lead to an optimal global solution. However, greedy algorithms do not always produce the best overall solution for certain problems, but they are useful when an immediate, locally optimal solution is desired. Reference: AIGP Body of Knowledge, algorithm types section.
質問 # 33
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?
- A. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
- B. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.
- C. Fines for SMEs and startups will be proportionally capped.
- D. Rules on General Purpose Al will apply after 6 months as a specific provision.
正解：B
解説：
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.
質問 # 34
The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?
- A. Human alternatives, consideration and fallback.
- B. High-risk mitigation standards.
- C. Data privacy.
- D. Safe and effective systems.
正解：B
解説：
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).
質問 # 35
A U.S. mortgage company developed an Al platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The Al platform will be used automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default.
Which of the following laws is NOT relevant to this use case?
- A. Equal Credit Opportunity Act.
- B. Fair Housing Act.
- C. Title VII of the Civil Rights Act of 1964.
- D. Fair Credit Reporting Act.
正解：C
解説：
The U.S. mortgage company's AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).
質問 # 36
Which of the following elements of feature engineering is most important to mitigate the potential bias in an Al system?
- A. Feature transformation.
- B. Feature importance analysis.
- C. Feature validation.
- D. Feature selection.
正解：D
解説：
Feature selection is the most important element of feature engineering to mitigate potential bias in an AI system. This process involves choosing the most relevant and representative features from the data set, which directly affects the model's performance and fairness. By carefully selecting features, data scientists can reduce the influence of biased or irrelevant attributes, ensuring that the AI system is more accurate and equitable. Proper feature selection helps in eliminating biases that might stem from socio-demographic factors or other sensitive variables, leading to a more balanced and fair AI model. Reference: AIGP Body of Knowledge on Fairness in AI and Feature Engineering.
質問 # 37
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. Generative Al is generally trained using intellectual property owned by third parties.
- B. Students must expressly consent to this use of generative Al.
- C. Content created by an LLM may be protectable under U.S. intellectual property law.
- D. Generative Al often creates content without attribution.
正解：B
解説：
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.
質問 # 38
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
- A. Adapting the existing governance structure algorithmic decision-making.
- B. Employing human-over-the-loop protocols for high-risk systems.
- C. Determining the level of human involvement in algorithmic decision-making.
- D. Establishing communications and collaboration among stakeholders.
正解：B
解説：
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.
質問 # 39
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.
正解：A
解説：
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.
質問 # 40
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?
- A. Socio-cultural and technical experts.
- B. Government regulators.
- C. Data architects.
- D. Legal and privacy governance experts.
正解：B
解説：
Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects).
Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry. Reference: AIGP BODY OF KNOWLEDGE and AI development frameworks.
質問 # 41
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. Human Rights, Democracy, and Rule of Law Impact Assessment (HUDERIA).
- B. IEEE Ethical System Design Risk Management Framework (IEEE 7000-21).
- C. NIST Cyber Security Risk Management Framework (CSF 2.0).
- D. NIST Information Security Risk (NIST SP 800-39).
正解：B
解説：
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.
質問 # 42
A company is creating a mobile app to enable individuals to upload images and videos, and analyze this data using ML to provide lifestyle improvement recommendations. The signup form has the following data fields:
1.First name
2.Last name
3.Mobile number
4.Email ID
5.New password
6.Date of birth
7.Gender
In addition, the app obtains a device's IP address and location information while in use.
What GDPR privacy principles does this violate?
- A. Transparency and Accuracy.
- B. Accountability and Lawfulness.
- C. Integrity and Confidentiality.
- D. Purpose Limitation and Data Minimization.
正解：D
解説：
The GDPR privacy principles that this scenario violates are Purpose Limitation and Data Minimization.
Purpose Limitation requires that personal data be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Data Minimization mandates that personal data collected should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. In this case, collecting extensive personal information (e.g., IP address, location, gender) and potentially using it beyond the necessary scope for the app's functionality could violate these principles by collecting more data than needed and possibly using it for purposes not originally intended.
質問 # 43
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.
What is the best reason the police department should continue to perform investigations even if the Al system scores an individual's likelihood of criminal activity at or above 90%?
- A. Because investigations may identify additional individuals involved in the crime.
- B. Because Al systems that affect fundamental civil rights should not be fully automated.
- C. Because investigations may uncover information relevant to sentencing.
- D. Because the department did not perform an impact assessment for this intended use.
正解：B
解説：
The best reason for the police department to continue performing investigations even if the AI system scores an individual's likelihood of criminal activity at or above 90% is that AI systems affecting fundamental civil rights should not be fully automated. Human oversight is essential to ensure that decisions impacting civil liberties are made with due consideration of context and mitigating factors that an AI might not fully appreciate. This approach ensures fairness, accountability, and adherence to legal standards. Reference: AIGP Body of Knowledge on AI Ethics and Human Oversight.
質問 # 44
To maintain fairness in a deployed system, it is most important to?
- A. Protect against loss of personal data in the model.
- B. Optimize computational resources and data to ensure efficiency and scalability.
- C. Monitor for data drift that may affect performance and accuracy.
- D. Detect anomalies outside established metrics that require new training data.
正解：C
質問 # 45
Which of the following most encourages accountability over Al systems?
- A. Determining the business objective and success criteria for the Al project.
- B. Performing due diligence on third-party Al training and testing data.
- C. Defining the roles and responsibilities of Al stakeholders.
- D. Understanding Al legal and regulatory requirements.
正解：C
解説：
Defining the roles and responsibilities of AI stakeholders is crucial for encouraging accountability over AI systems. Clear delineation of who is responsible for different aspects of the AI lifecycle ensures that there is a person or team accountable for monitoring, maintaining, and addressing issues that arise. This accountability framework helps in ensuring that ethical standards and regulatory requirements are met, and it facilitates transparency and traceability in AI operations. By assigning specific roles, organizations can better manage and mitigate risks associated with AI deployment and use.
質問 # 46
What is the primary purpose of conducting ethical red-teaming on an Al system?
- A. To improve the model's accuracy.
- B. To ensure compliance with applicable law.
- C. To identify security vulnerabilities.
- D. To simulate model risk scenarios.
正解：D
解説：
The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios.
Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system's reliability, fairness, and security. Reference: AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.
質問 # 47
Which of the following Al uses is best described as human-centric?
- A. Machine learning is used for demand forecasting and inventory management, ensuring that consumers can find products they want when they want them.
- B. Virtual assistants are used adapt educational content and teaching methods to individuals, offering personalized recommendations based on ability and needs.
- C. Autonomous robots are used to move products within a warehouse, allowing human workers to reduce physical strain and alleviate monotony.
- D. Pattern recognition algorithms are used to improve the accuracy of weather predictions, which benefits many industries and everyday life.
正解：B
解説：
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.
質問 # 48
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?
- A. Toensure they provide transparency applicants on the model.
- B. Toapply their own judgment to the initial assessment.
- C. Tosolicit on-going feedback on model performance.
- D. Toprovide a reminder of a right appeal.
正解：B
解説：
Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment. While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.
質問 # 49
Testing data is defined as a subset of data that is used to?
- A. Enable a model to discover and learn patterns.
- B. Evaluate a model's handling of randomized edge cases.
- C. Assess a model's on-going performance in production.
- D. Provide a robust evaluation of a final model.
正解：D
解説：
Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.
質問 # 50
What is the primary purpose of an Al impact assessment?
- A. To define and document the roles and responsibilities of Al stakeholders.
- B. Anticipate and manage the potential risks and harms of an Al system.
- C. To define and evaluate the legal risks associated with developing an Al system.
- D. To identify and measure the benefits of an Al system.
正解：B
解説：
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.
質問 # 51
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?
- A. The personal data used during processing, including inferences drawn by the Al system about the data.
- B. The data protection impact assessments carried out on the Al system and legal bases for processing.
- C. The contact details of the data protection officer and the data protection national authority.
- D. The existence of automated decision-making and meaningful information on its logic and consequences.
正解：D
解説：
The GDPR's transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.
質問 # 52
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. Utilize synthetic data to offset the lack of patient data.
- B. Deploy the current model and recalibrate it over time with more data.
- C. Extend the model to multi-modal ingestion with text and images.
- D. Refocus the algorithm to patients without cancer.
正解：A
解説：
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.
質問 # 53
A company is working to develop a self-driving car that can independently decide the appropriate route to take the driver after the driver provides an address.
If they want to make this self-driving car "strong" Al, as opposed to "weak," the engineers would also need to ensure?
- A. That the Al has strong cybersecurity to prevent malicious actors from taking control of the car.
- B. Thatthe Al has full human cognitive abilities that can independently decide where to take the driver.
- C. That the Al can differentiate among ethnic backgrounds of pedestrians.
- D. That they have obtained appropriate intellectual property (IP) licenses to use data for training the Al.
正解：B
解説：
Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive abilities.
For the self-driving car to be classified as "strong" AI, it would need to possess full human cognitive abilities to make independent decisions beyond pre-programmed instructions. Reference: AIGP BODY OF KNOWLEDGE and AI classifications.
質問 # 54
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.
正解：B
解説：
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.
質問 # 55
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?
- A. Secure and Resilient.
- B. Accountable and Transparent.
- C. Explainable and Interpretable.
- D. Tested and Effective.
正解：D
解説：
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.
質問 # 56
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