AIGP Practice Exam Tests Latest Updated on Dec-2024
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IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 50
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?
- A. Defining requirements specific to each industry sector and high-risk Al domain.
- B. Balancing Al innovation with ethical considerations.
- C. Establishing explain ability criteria to responsibly source and use data to train Al systems.
- D. Focusing on Al technical design and post-deployment monitoring.
Answer: B
Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).
NEW QUESTION # 51
In the machine learning context, feature engineering is the process of?
- A. Developing guidelines to train and test a model.
- B. Extracting attributes and variables from raw data.
- C. Converting raw data into clean data.
- D. Creating learning schema for a model apply.
Answer: B
Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.
NEW QUESTION # 52
A company developed Al technology that can analyze text, video, images and sound to tag content, including the names of animals, humans and objects.
What type of Al is this technology classified as?
- A. Deductive inference.
- B. Multi-modal model.
- C. Transformative Al.
- D. Expert system.
Answer: B
Explanation:
A multi-modal model is an AI system that can process and analyze multiple types of data, such as text, video, images, and sound. This type of AI integrates different data sources to enhance its understanding and decision-making capabilities. In the given scenario, the AI technology that tags content including names of animals, humans, and objects falls under this category. Reference: AIGP BODY OF KNOWLEDGE, which outlines the capabilities and use cases of multi-modal models.
NEW QUESTION # 53
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?
- A. Office of Science and Technology Policy (OSTP).
- B. National Institute of Standards and Technology (NIST).
- C. National Science and Technology Council (NSTC).
- D. Department of Homeland Security (DHS).
Answer: B
Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems.
NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.
NEW QUESTION # 54
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 can differentiate among ethnic backgrounds of pedestrians.
- B. That they have obtained appropriate intellectual property (IP) licenses to use data for training the Al.
- C. Thatthe Al has full human cognitive abilities that can independently decide where to take the driver.
- D. That the Al has strong cybersecurity to prevent malicious actors from taking control of the car.
Answer: C
Explanation:
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.
NEW QUESTION # 55
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
- A. Establishing communications and collaboration among stakeholders.
- B. Employing human-over-the-loop protocols for high-risk systems.
- C. Adapting the existing governance structure algorithmic decision-making.
- D. Determining the level of human involvement in algorithmic decision-making.
Answer: B
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 # 56
What is the 1956 Dartmouth summer research project on Al best known as?
- A. A meeting focused on the impacts of the launch of the first mass-produced computer.
- B. A research project to create a test for machine intelligence.
- C. A research project on the impacts of technology on society.
- D. A meeting focused on the founding of the Al field.
Answer: D
Explanation:
The 1956 Dartmouth summer research project on AI is best known as a meeting focused on the founding of the AI field. This conference is historically significant because it marked the formal beginning of artificial intelligence as an academic discipline. The term "artificial intelligence" was coined during this event, and it laid the foundation for future research and development in AI.
Reference: The AIGP Body of Knowledge highlights the importance of the Dartmouth Conference as a pivotal moment in the history of AI, which established AI as a distinct field of study and research.
NEW QUESTION # 57
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 # 58
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).
Answer: B
Explanation:
The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) would be most appropriate for XYZ Corp's governance needs in addition to the NIST AI Risk Management Framework. The IEEE framework specifically addresses ethical concerns during system design, which is crucial for ensuring the responsible use of AI in hiring. It complements the NIST framework by focusing on ethical risk management, aligning well with XYZ Corp's goals of deploying AI responsibly and mitigating associated risks.
NEW QUESTION # 59
Which of the following steps occurs in the design phase of the Al life cycle?
- A. Model explainability.
- B. Data augmentation.
- C. Performance evaluation.
- D. Risk impact estimation.
Answer: D
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 # 60
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 # 61
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?
- A. Reliable.
- B. Resilient.
- C. Robust.
- D. Reinforced.
Answer: B
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 # 62
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?
- A. Define a model-validation methodology.
- B. Document maintenance teams and processes.
- C. Identify known edge cases to monitor post-deployment.
- D. Perform a readiness assessment.
Answer: D
Explanation:
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.
NEW QUESTION # 63
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 uncover information relevant to sentencing.
- B. Because Al systems that affect fundamental civil rights should not be fully automated.
- C. Because investigations may identify additional individuals involved in the crime.
- D. Because the department did not perform an impact assessment for this intended use.
Answer: B
Explanation:
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.
NEW QUESTION # 64
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. Greedy.
- B. Single-lane.
- C. Optimized.
- D. Efficient.
Answer: A
Explanation:
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.
NEW QUESTION # 65
What type of organizational risk is associated with Al's resource-intensive computing demands?
- A. People risk.
- B. Third-party risk.
- C. Security risk.
- D. Environmental risk.
Answer: D
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 # 66
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?
- A. Testing should minimize human involvement to the extent practicable.
- B. Testing should be performed specific to the intended uses.
- C. The test data should be anonymized to the extent practicable.
- D. The test data should be representative of the expected operationaldata.
Answer: A
Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.
NEW QUESTION # 67
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.
Answer: C
Explanation:
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.
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.
In the design phase, what is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?
- A. Ensure the data is labeled and formatted.
- B. Apply privacy-enhancing technologies to the data.
- C. Identify fits and gaps in the combined data.
- D. Evaluate the country of origin of the data.
Answer: C
Explanation:
In the design phase of integrating data from different sources, identifying fits and gaps is crucial. This process involves understanding how well the data from the clinical research partner aligns with the healthcare network's existing data. It ensures that the combined data set is coherent and can be effectively used for training the AI algorithm. This step helps in spotting any discrepancies, inconsistencies, or missing data that might affect the performance and accuracy of the AI model. It directly addresses the integrity and compatibility of the data, which is foundational before applying any privacy-enhancing technologies, labeling, or evaluating the origin of the data. Reference: AIGP Body of Knowledge on Data Integration and Quality.
NEW QUESTION # 69
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