AIGP DOWNLOAD PDF DUMPS & AIGP LATEST TRAINING MATERIAL & AIGP EXAM PREP STUDY

AIGP download pdf dumps & AIGP latest training material & AIGP exam prep study

AIGP download pdf dumps & AIGP latest training material & AIGP exam prep study

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Tags: AIGP Actual Exams, AIGP Latest Dumps Free, Latest AIGP Dumps Pdf, Sample AIGP Questions, Best AIGP Practice

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AIGP Latest Dumps Free & Latest AIGP Dumps Pdf

Do not hesitate to seek our extraordinary IAPP AIGP practice material to make a name in the field of Technology. DumpExam has designed the IAPP AIGP product in three formats. You will find their specifications below to comprehend them better.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
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 Canada’s Bill C-27.
Topic 3
  • Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
Topic 4
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 5
  • 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 6
  • 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.

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

NEW QUESTION # 70
What is the primary purpose of conducting ethical red-teaming on an Al system?

  • A. To ensure compliance with applicable law.
  • B. To simulate model risk scenarios.
  • C. To improve the model's accuracy.
  • D. To identify security vulnerabilities.

Answer: B

Explanation:
The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios.
Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system's reliability, fairness, and security. Reference: AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.


NEW QUESTION # 71
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.
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?

  • A. Anti-discriminationlaws.
  • B. Privacy laws.
  • C. Accessibility laws.
  • D. Product liability laws.

Answer: D

Explanation:
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.


NEW QUESTION # 72
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?

  • A. Autoregression, which analyzes and makes predictions about time-series data.
  • B. Momentum, which improves the convergence speed and stability of neural network training.
  • C. Backpropagation, which starts from the last layer working backwards.
  • D. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.

Answer: A

Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization.
Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.


NEW QUESTION # 73
A US company has developed an Al system, CrimeBuster 9619, that collects information about incarcerated individuals to help parole boards predict whether someone is likely to commit another crime if released from prison.
When considering expanding to the EU market, this type of technology would?

  • A. Be subject approval by the relevant EU authority.
  • B. Require the company to register the tool with the EU database.
  • C. Be banned under the EU Al Act.
  • D. Require a detailed conformity assessment.

Answer: C

Explanation:
Under the EU AI Act, high-risk AI systems like CrimeBuster 9619 would require a detailed conformity assessment before being deployed in the EU market. This assessment ensures that the AI system complies with all relevant regulations and standards, addressing potential risks related to privacy, security, and discrimination. The company would not need to register the tool with the EU database (A), seek approval from an EU authority (B), or face a ban (D) as long as it meets the necessary conformity requirements.


NEW QUESTION # 74
Scenario:
A European AI technology company was found to be non-compliant with certain provisions of the EU AI Act.
The regulator is considering penalties under the enforcement provisions of the regulation.
According to the EU AI Act, which of the following non-compliance examples could lead to fines of up to €
15 million or 3% of annual worldwide turnover(whichever is higher)?

  • A. In case of the supply of misleading information to notified bodies in reply to a request
  • B. In case of breach of a provider's obligations for high-risk AI systems
  • C. In case of a breach of AI Act prohibition by the Union institutions, bodies, offices and agencies
  • D. In case of AI Act prohibitions

Answer: B

Explanation:
The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation.
A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €
15 million or 3% of annual global turnover.
From the AIGP ILT Guide - EU AI Act Module:
"Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover." AI Governance in Practice Report 2024 supports this:
"Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article
71(3) of the EU AI Act."
Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.


NEW QUESTION # 75
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