AIGP - IAPP CERTIFIED ARTIFICIAL INTELLIGENCE GOVERNANCE PROFESSIONAL UPDATED LATEST TEST SIMULATIONS

AIGP - IAPP Certified Artificial Intelligence Governance Professional Updated Latest Test Simulations

AIGP - IAPP Certified Artificial Intelligence Governance Professional Updated Latest Test Simulations

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Tags: Latest Test AIGP Simulations, Valid AIGP Study Guide, PDF AIGP Cram Exam, Knowledge AIGP Points, Latest AIGP Exam Tips

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q65-Q70):

NEW QUESTION # 65
A company deploys an AI model for fraud detection in online transactions. During its operation, the model begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent.
Which is the best step the company should take to address this development?

  • A. Deactivate the model until an assessment is made.
  • B. Maintain records of all false positives.
  • C. Conduct training for customer service teams to handle flagged transactions.
  • D. Dedicate more resources to monitor the model.

Answer: A

Explanation:
When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.
From theAI Governance in Practice Report 2024:
"Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems." (p. 12, 35)


NEW QUESTION # 66
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?

  • A. Balancing Al innovation with ethical considerations.
  • B. Defining requirements specific to each industry sector and high-risk Al domain.
  • C. Establishing explain ability criteria to responsibly source and use data to train Al systems.
  • D. Focusing on Al technical design and post-deployment monitoring.

Answer: A

Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).


NEW QUESTION # 67
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 a breach of AI Act prohibition by the Union institutions, bodies, offices and agencies
  • C. In case of AI Act prohibitions
  • D. In case of breach of a provider's obligations for high-risk AI systems

Answer: D

Explanation:
The correct answer is B. The EU AI Act assigns a tiered penalty system based on the severity of the violation. A breach of obligations related to high-risk AI systems falls 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: The highest penalty (€35 million or 7%) applies to prohibited AI uses, not to obligations for high- risk systems.


NEW QUESTION # 68
What is the most important factor when deciding whether or not to select a proprietary AI model?

  • A. Whether its system card identifies risks.
  • B. How frequently it will be updated.
  • C. Whether its training data is disclosed.
  • D. What business purpose it will serve.

Answer: D

Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.


NEW QUESTION # 69
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?

  • A. GPUs can access memory quickly, resulting in lower latency than CPUs.
  • B. GPUs run many tasks concurrently, resulting in faster processing.
  • C. GPUs can run every task on a computer, making them more robust than CPUs.
  • D. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.

Answer: B

Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference: AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.


NEW QUESTION # 70
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