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Pass Guaranteed Quiz IAPP - AIGP - IAPP Certified Artificial Intelligence Governance Professional –Trustable Valid Study Guide

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.
Topic 2	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 3	Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 4	Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
Topic 5	Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 6	Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
The White House Executive Order from November 2023 requires companies that develop dual-use foundation models to provide reports to the federal government about all of the following EXCEPT?

A. Any environmental impact study for each dual-use foundation model.
B. The results of red-team testing of each dual-use foundation model.
C. The physical and cybersecurity protection measures of their dual-use foundation models.
D. Any current training or development of dual-use foundation models.

Answer: A

Explanation:
The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.
Reference: AIGP BODY OF KNOWLEDGE, sections on compliance and reporting requirements, and the White House Executive Order of November 2023.

NEW QUESTION # 60
According to the EU Al Act, providers of what kind of machine learning systems will be required to register with an EU oversight agency before placing their systems in the EU market?

A. Al systems that are "strong" general intelligence.
B. Al systems trained on sensitive personal data.
C. Al systems that are harmful based on a legal risk-utility calculation.
D. Al systems that are high-risk.

Answer: D

Explanation:
According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act's framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards.
High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act's provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting "strong" general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.

NEW QUESTION # 61
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

A. Generative Al.
B. Supervised learning.
C. Expert systems.
D. Segmentation.

Answer: B

Explanation:
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).

NEW QUESTION # 62
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.
When notifying an accused perpetrator, what additional information should a police officer provide about the use of the Al system?

A. Information about how the individual was identified by the Al system.
B. Information about the composition of the training data of the system.
C. Information about how the accused can oppose the charges.
D. Information about the accuracy of the Al system.

Answer: A

Explanation:
When notifying an accused perpetrator, the police officer should provide information about how the individual was identified by the AI system. This transparency is crucial for maintaining trust and ensuring that the accused understands the basis of the charges against them. Information about the accuracy, how to oppose the charges, and the composition of the training data, while potentially relevant, do not directly address the immediate need for the accused to understand the specific process that led to their identification. Reference:
AIGP Body of Knowledge on AI Transparency and Explainability.

NEW QUESTION # 63
You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating.
What is the best first step address this?

A. Replace the model with a previous version.
B. Conduct champion/challenger testing.
C. Run red-teaming exercises.
D. Perform an audit of the model.

Answer: B

Explanation:
When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.
Reference: AIGP BODY OF KNOWLEDGE, sections on model performance management and testing strategies.

NEW QUESTION # 64
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AIGP Valid Study Guide & Valid Dumps AIGP Files

Pass Guaranteed Quiz IAPP - AIGP - IAPP Certified Artificial Intelligence Governance Professional –Trustable Valid Study Guide

IAPP AIGP Exam Syllabus Topics:

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q59-Q64):

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