Pass Guaranteed 2025 Valid AIGP: New IAPP Certified Artificial Intelligence Governance Professional Test Dumps

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q22-Q27):

NEW QUESTION # 22
During the planning and design phases of the Al development life cycle, bias can be reduced by all of the following EXCEPT?

A. Feature selection.
B. Data collection.
C. Human oversight.
D. Stakeholder involvement.

Answer: A

Explanation:
Bias in AI can be reduced during the planning and design phases through stakeholder involvement, human oversight, and careful data collection. While feature selection is critical in the development phase, it does not specifically occur during planning and design. Ensuring diverse stakeholder involvement and human oversight helps identify and mitigate potential biases early, and data collection ensures a representative dataset.
Reference: AIGP Body of Knowledge on AI Development Lifecycle and Bias Mitigation.

NEW QUESTION # 23
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?

A. Statistical sampling.
B. Risk probability/severity.
C. Decision analysis.
D. Adversarial robustness.

Answer: B

Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.

NEW QUESTION # 24
During the planning and design phases of the Al development life cycle, bias can be reduced by all of the following EXCEPT?

A. Feature selection.
B. Data collection.
C. Human oversight.
D. Stakeholder involvement.

Answer: A

Explanation:
Bias in AI can be reduced during the planning and design phases through stakeholder involvement, human oversight, and careful data collection. While feature selection is critical in the development phase, it does not specifically occur during planning and design. Ensuring diverse stakeholder involvement and human oversight helps identify and mitigate potential biases early, and data collection ensures a representative dataset. Reference: AIGP Body of Knowledge on AI Development Lifecycle and Bias Mitigation.

NEW QUESTION # 25
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

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

Answer: A

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 # 26
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?

A. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
B. Implementing a formal incident response plan to address incidents that may occur during system operation.
C. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
D. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.

Answer: A

Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remain internal or shared with regulators.
From theAI Governance in Practice Report 2024:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)

NEW QUESTION # 27
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