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Free PDF Quiz Updated AIGP - Pdf Demo IAPP Certified Artificial Intelligence Governance Professional Download

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IAPP AIGP Exam Syllabus Topics:

Topic
Details

Topic 1

Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.

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.

Topic 4

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 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

Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q165-Q170):

NEW QUESTION # 165
During the first month when the company monitors the model for bias, it is most important to?

A. Continue disparity testing.
B. Analyze the quality of the training and testing data.
C. Provide regular awareness training.
D. Document the results of final decisions made by the human underwriter.

Answer: A

Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking for bias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report 2024:
"Performance monitoring protocols... should include mechanisms to assess and measure disparities in outcomes across different demographic groups." (p. 12)
"Bias may not be evident during pre-deployment testing but can emerge in real-world use." (p. 41)
* B. Awareness trainingis helpful, but not a technical bias mitigation activity.
* C. Analyzing training datais apre-deploymenttask.
* D. Documenting human decisionsmay support auditability but doesn't detect bias in AI outputs.

NEW QUESTION # 166
All of the following are included within the scope of post-deployment Al maintenance EXCEPT?

A. Ensuring that all model components are subject a control framework.
B. Evaluating the need for an audit under certain standards.
C. Defining thresholds to conduct new impact assessments.
D. Dedicating experts to continually monitor the model output.

Answer: C

Explanation:
Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity.
Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing AI lifecycle management and post-deployment activities.

NEW QUESTION # 167
Retrieval-Augmented Generation (RAG) is defined as?

A. Fine tuning LLMs to minimize biased outputs.
B. Reducing computational processing requirements of the LLMs.
C. Applying advanced filtering techniques to the LLMs.
D. Combining LLMs with private knowledge bases to improve their outputs.

Answer: D

Explanation:
Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline-allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.
Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high- stakes environmentswhere hallucinations are a concern.
* B, C, and Ddescribe optimization or bias mitigation-not the core function of RAG.

NEW QUESTION # 168
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight.
These failures are an example of?

A. Forgetting.
B. Hallucination.
C. Brittleness.
D. Uncertainty.

Answer: C

Explanation:
The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

NEW QUESTION # 169
Which of the following is NOT a common type of machine learning?

A. Deep learning.
B. Unsupervised learning.
C. Reinforcement learning.
D. Cognitive learning.

Answer: D

Explanation:
The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.

NEW QUESTION # 170
......

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