This page was exported from Valid Premium Exam [ http://premium.validexam.com ] Export date:Thu Apr 3 22:16:50 2025 / +0000 GMT ___________________________________________________ Title: [Mar-2025] AIGP Dumps Full Questions - Artificial Intelligence Governance Exam Study Guide [Q59-Q74] --------------------------------------------------- [Mar-2025] AIGP Dumps Full Questions - Artificial Intelligence Governance Exam Study Guide Exam Questions and Answers for AIGP Study Guide IAPP AIGP Exam Syllabus Topics: TopicDetailsTopic 1Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.Topic 2Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.Topic 3Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.Topic 4Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.   NO.59 Which of the following steps occurs in the design phase of the Al life cycle?  Data augmentation.  Model explainability.  Risk impact estimation.  Performance evaluation. 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.NO.60 An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?  Robust.  Reliable.  Resilient.  Reinforced. 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.NO.61 CASE STUDYPlease 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.If XYZ does not deploy and use the Al hiring tool responsibly in the United States, its liability would likely increase under all of the following laws EXCEPT?  Anti-discriminationlaws.  Product liability laws.  Accessibility laws.  Privacy laws. In the United States, the use of AI hiring tools must comply with anti-discrimination laws, accessibility laws, and privacy laws to avoid increasing liability. Anti-discrimination laws (A) ensure that hiring practices do not unlawfully discriminate against protected classes. Accessibility laws (C) require that hiring tools are accessible to all applicants, including those with disabilities. Privacy laws (D) govern the handling of personal data during the hiring process. Product liability laws (B), however, typically apply to the safety and reliability of physical products and would not generally increase liability specifically related to the responsible use of AI hiring tools in the employment context.NO.62 Testing data is defined as a subset of data that is used to?  Assess a model’s on-going performance in production.  Enable a model to discover and learn patterns.  Provide a robust evaluation of a final model.  Evaluate a model’s handling of randomized edge cases. 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.NO.63 The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?  Human alternatives, consideration and fallback.  High-risk mitigation standards.  Safe and effective systems.  Data privacy. 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).NO.64 Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?  The human oversight measures employed.  How an individual may contest a decision.  The location(s) where data is stored.  The fact that an Al system is being used. 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.NO.65 Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?  Tested and Effective.  Secure and Resilient.  Explainable and Interpretable.  Accountable and Transparent. 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.NO.66 All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?  Risk probability/severity.  Adversarial robustness.  Statistical sampling.  Decision analysis. 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.NO.67 Random forest algorithms are in what type of machine learning model?  Symbolic.  Generative.  Discriminative.  Natural language processing. 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.NO.68 Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?  Segmentation.  Generative Al.  Expert systems.  Supervised learning. Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference:AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).NO.69 According to the GDPR, an individual has the right to have a human confirm or replace an automated decision unless that automated decision?  Is authorized with the data subject s explicit consent.  Is authorized by applicable Ell law and includes suitable safeguards.  Is deemed to solely benefit the individual and includes documented legitimate interests.  Is necessary for entering into or performing under a contract between the data subject and data controller. 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.NO.70 CASE STUDYPlease 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 tA. 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.The best approach to enable a customer who wants information on the Al model’s parameters for underwriting purposes is to provide?  A transparency notice.  An opt-out mechanism.  Detailed terms of service.  Customer service support. The best approach to enable a customer who wants information on the AI model’s parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.Reference: According to the AIGP Body of Knowledge, transparency in AI systems is essential to ensure that stakeholders, including customers, understand how their data is being used and how decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are informed and can make knowledgeable decisions regarding their interactions with AI systems.NO.71 CASE STUDYPlease 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?  Reputational harm.  Civil rights violations.  Discriminatory treatment.  Intellectual property infringement. 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.NO.72 Training data is best defined as a subset of data that is used to?  Enable a model to detect and learn patterns.  Fine-tune a model to improve accuracy and prevent overfitting.  Detect the initial sources of biases to mitigate prior to deployment.  Resemble the structure and statistical properties of production data. Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model’s capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.NO.73 Which of the following is NOT a common type of machine learning?  Deep learning.  Cognitive learning.  Unsupervised learning.  Reinforcement learning. The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.NO.74 What is the 1956 Dartmouth summer research project on Al best known as?  A meeting focused on the impacts of the launch of the first mass-produced computer.  A research project on the impacts of technology on society.  A research project to create a test for machine intelligence.  A meeting focused on the founding of the Al field. 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. Loading … IAPP Certified Artificial Intelligence Governance Professional Free Update With 100% Exam Passing Guarantee: https://www.validexam.com/AIGP-latest-dumps.html --------------------------------------------------- Images: https://premium.validexam.com/wp-content/plugins/watu/loading.gif https://premium.validexam.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2025-03-31 11:04:26 Post date GMT: 2025-03-31 11:04:26 Post modified date: 2025-03-31 11:04:26 Post modified date GMT: 2025-03-31 11:04:26