AIGP TEST DISCOUNT | PREPARATION AIGP STORE

AIGP Test Discount | Preparation AIGP Store

AIGP Test Discount | Preparation AIGP Store

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding 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 2
  • Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and copyright’s Bill C-27.
Topic 3
  • Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which of the following elements of feature engineering is most important to mitigate the potential bias in an Al system?

  • A. Feature validation.
  • B. Feature transformation.
  • C. Feature selection.
  • D. Feature importance analysis.

Answer: C

Explanation:
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.


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 Science and Technology Council (NSTC).
  • C. Department of Homeland Security (DHS).
  • D. National Institute of Standards and Technology (NIST).

Answer: D

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
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers.
Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

  • A. A classic machine learning model.
  • B. A free large language model.
  • C. A subscription-based, multimodal model.
  • D. A proprietary generative AI model.

Answer: A

Explanation:
Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in, classic ML models(e.g., regression, decision trees, random forests) are optimal.
From theAI Governance in Practice Report 2024:
"Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance." (p. 33)
* AandCmay have limited transparency and are often tied to specific providers.
* Dinvolves ongoing costs and limited model control.


NEW QUESTION # 55
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.
Which of the following measures should XYZ adopt to best mitigate its risk of reputational harm from using the Al tool?

  • A. Continue to require XYZ's hiring personnel to manually screen all applicants.
  • B. Test the Al tool pre- and post-deployment.
  • C. Direct the procurement team to select the most economical Al tool.
  • D. Ensure the vendor assumes responsibility for all damages.

Answer: B

Explanation:
To mitigate the risk of reputational harm from using an AI hiring tool, XYZ Corp should rigorously test the AI tool both before and after deployment. Pre-deployment testing ensures the tool works correctly and does not introduce bias or other issues. Post-deployment testing ensures the tool continues to operate as intended and adapts to any changes in data or usage patterns. This approach helps to identify and address potential issues proactively, thereby reducing the risk of reputational harm. Ensuring the vendor assumes responsibility for damages (B) does not address the root cause of potential issues, selecting the most economical tool (C) may compromise quality, and continuing manual screening (D) defeats the purpose of using the AI tool.


NEW QUESTION # 56
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. Engage a third party to perform an audit.
  • B. Procure more data from clinical research partners.
  • C. Perform an impact assessment.
  • D. Create a bias bounty program.

Answer: C

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 # 57
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