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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.
Topic 2	Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.
Topic 3	Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
Topic 4	Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q70-Q75):

NEW QUESTION # 70
You are an engineer that developed an Al-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool's effectiveness?

A. Output data, assess the delta between the prediction and actual ad clicks.
B. Algorithmic patterns, to show the model has a high degree of accuracy.
C. Input data, to ensure the ads are reaching the target audience.
D. GPU performance, to evaluate the tool's robustness.

Answer: A

Explanation:
To evaluate the effectiveness of an AI-based ad recommendation tool, the most relevant metric is the output data, specifically assessing the delta between the prediction and actual ad clicks. This metric directly measures the tool's accuracy and effectiveness in making accurate recommendations that lead to user engagement. While monitoring algorithmic patterns and input data can provide insights into the model's behavior and targeting accuracy, and GPU performance can indicate the robustness and efficiency of the tool, the primary indicator of effectiveness for an ad recommendation tool is how well it predicts actual ad clicks.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI performance metrics and evaluation methods.

NEW QUESTION # 71
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

A. Explainable and Interpretable.
B. Accountable and Transparent.
C. Secure and Resilient.
D. Tested and Effective.

Answer: A

Explanation:
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.

NEW QUESTION # 72
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Training an AI model is time-consuming because of?

A. The quality of the training data.
B. The number of stakeholders.
C. The maturity of AI governance.
D. The volume of training data.
E. The complexity of the AI model.

Answer: A,D,E

Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report 2024:
"Most AI requires sizeable amounts of high-quality data... to ensure desired and accurate output." (p. 15)
"The accuracy of AI model outputs depends significantly on the quality of their inputs." (p. 24)
"Complex AI systems... with many parameters... result in long development and training phases." (p. 32)
* B. Maturity of governanceaffects oversight, not training time.
* D. Number of stakeholdersaffects alignment, not direct training duration.

NEW QUESTION # 73
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. Test the Al tool pre- and post-deployment.
B. Direct the procurement team to select the most economical Al tool.
C. Continue to require XYZ's hiring personnel to manually screen all applicants.
D. Ensure the vendor assumes responsibility for all damages.

Answer: A

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 # 74
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?

A. Reinforced.
B. Robust.
C. Resilient.
D. Reliable.

Answer: C

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 # 75
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Reliable AIGP Exam Preparation Pass Certify | Latest AIGP Real Brain Dumps: IAPP Certified Artificial Intelligence Governance Professional

IAPP AIGP Exam Syllabus Topics:

IAPP AIGP Real Brain Dumps - AIGP Exam Guide Materials

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q70-Q75):

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