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NEW QUESTION # 33
Which risk management framework/guide/standard focuses on value-based engineering methodology?

• A. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
• B. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for Al Systems.
• C. ISO/IEC Guide 51 (Safety).
• D. ISO 31000 Guidelines (Risk Management).

Answer: A

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 # 34
Which of the following most encourages accountability over Al systems?

• A. Defining the roles and responsibilities of Al stakeholders.
• B. Performing due diligence on third-party Al training and testing data.
• C. Understanding Al legal and regulatory requirements.
• D. Determining the business objective and success criteria for the Al project.

Answer: A

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 # 35
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. Multi-modal model.
• B. Transformative Al.
• C. Deductive inference.
• D. Expert system.

Answer: A

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 # 36
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.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?

• A. Procure more data from clinical research partners.
• B. Engage a third party to perform an audit.
• C. Create a bias bounty program.
• D. Perform an impact assessment.

Answer: D

Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.

NEW QUESTION # 37
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?

• A. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
• B. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
• C. Employing a new software platform to modernize existing compliance processes across the organization.
• D. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.

Answer: C

Explanation:
When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles. Reference: AIGP Body of Knowledge on AI Governance and Compliance Integration.

NEW QUESTION # 38
In the machine learning context, feature engineering is the process of?

• A. Developing guidelines to train and test a model.
• B. Converting raw data into clean data.
• C. Extracting attributes and variables from raw data.
• D. Creating learning schema for a model apply.

Answer: C

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 # 39
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact Al system?

• A. Upon initial deployment of the system.
• B. When use of the system causes or is likely to cause material harm.
• C. When the algorithmic impact assessment has been completed.
• D. Upon release of a new version of the system.

Answer: A

Explanation:
According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.

NEW QUESTION # 40
You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative Al tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative Al tool is an example of what is commonly called?

• A. Overfitting.
• B. Model collapse.
• C. Prompt injection.
• D. Hallucination.

Answer: D

Explanation:
In the context of AI, particularly generative models, "hallucination" refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.

NEW QUESTION # 41
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?

• A. Statistical sampling.
• B. Decision analysis.
• C. Adversarial robustness.
• D. Risk probability/severity.

Answer: D

Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.

NEW QUESTION # 42
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?

• A. System cost.
• B. Model drift.
• C. Data loss.
• D. Feature drift.

Answer: A

Explanation:
When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

NEW QUESTION # 43
To maintain fairness in a deployed system, it is most important to?

• A. Detect anomalies outside established metrics that require new training data.
• B. Protect against loss of personal data in the model.
• C. Monitor for data drift that may affect performance and accuracy.
• D. Optimize computational resources and data to ensure efficiency and scalability.

Answer: C

NEW QUESTION # 44
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

• A. Establish a human-in-the-loop procedure.
• B. Provide a just-in-time notice about the automated decision-making logic.
• C. Provide a right to review automated decision.
• D. Define suitable measures to safeguard personal data.

Answer: A

Explanation:
The GDPR requires that individuals have the right to not be subject to decisions based solely on automated processing, including profiling, unless specific exceptions apply. One effective control is to establish a human-in-the-loop procedure (D), ensuring human oversight and the ability to contest decisions. This goes beyond just-in-time notices (A), data safeguarding (B), or review rights (C), providing a more robust mechanism to protect individuals' rights.

NEW QUESTION # 45
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. Civil rights violations.
• B. Discriminatory treatment.
• C. Intellectual property infringement.
• D. Reputational harm.

Answer: C

Explanation:
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.

NEW QUESTION # 46
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 .. 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.
During the first month when ABC monitors the model for bias, it is most important to?

• A. Seek approval from management for any changes to the model.
• B. Continue disparity testing.
• C. Analyze the quality of the training and testing data.
• D. Compare the results to human decisions prior to deployment.

Answer: B

Explanation:
During the first month of monitoring the model for bias, it is most important to continue disparity testing.
Disparity testing involves regularly evaluating the model's decisions to identify and address any biases, ensuring that the model operates fairly across different demographic groups.
Reference: Regular disparity testing is highlighted in the AIGP Body of Knowledge as a critical practice for maintaining the fairness and reliability of AI models. By continuously monitoring for and addressing disparities, organizations can ensure their AI systems remain compliant with ethical and legal standards, and mitigate any unintended biases that may arise in production.

NEW QUESTION # 47
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

• A. Rules on General Purpose Al will apply after 6 months as a specific provision.
• B. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
• C. Fines for SMEs and startups will be proportionally capped.
• D. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.

Answer: D

Explanation:
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.

NEW QUESTION # 48
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. Perform a readiness assessment.
• B. Identify known edge cases to monitor post-deployment.
• C. Define a model-validation methodology.
• D. Document maintenance teams and processes.

Answer: A

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 # 49
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. National Institute of Standards and Technology (NIST).
• B. National Science and Technology Council (NSTC).
• C. Office of Science and Technology Policy (OSTP).
• D. Department of Homeland Security (DHS).

Answer: A

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 # 50
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 contact details of the data protection officer and the data protection national authority.
• C. The existence of automated decision-making and meaningful information on its logic and consequences.
• D. The data protection impact assessments carried out on the Al system and legal bases for processing.

Answer: C

Explanation:
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.

NEW QUESTION # 51
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