AIGP Latest Exam Preparation - AIGP Latest Demo

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

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

A. Account for new interpretations of the same data.
B. Adjust the model's hyper parameters to a specific use case.
C. To minimize degradation in prediction accuracy due tochanges in data.
D. To ensure interpretability of the model's predictions.

Answer: D

Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.

NEW QUESTION # 67
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?

A. Data architects.
B. Socio-cultural and technical experts.
C. Government regulators.
D. Legal and privacy governance experts.

Answer: C

Explanation:
Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects).
Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry. Reference: AIGP BODY OF KNOWLEDGE and AI development frameworks.

NEW QUESTION # 68
Machine learning is best described as a type of algorithm by which?

A. Systems can automatically improve from experience through predictive patterns.
B. Systems can mimic human intelligence with the goal of replacing humans.
C. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
D. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.

Answer: A

Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

NEW QUESTION # 69
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

A. Toensure they provide transparency applicants on the model.
B. Tosolicit on-going feedback on model performance.
C. Toprovide a reminder of a right appeal.
D. Toapply their own judgment to the initial assessment.

Answer: D

Explanation:
Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment. While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.

NEW QUESTION # 70
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. Brittleness.
C. Hallucination.
D. Uncertainty.

Answer: B

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 # 71
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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q66-Q71):

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