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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q32-Q37):

NEW QUESTION # 32
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.
Which of the following measures should XYZ adopt to best mitigate its risk of reputational harm from using the Al tool?

A. Ensure the vendor assumes responsibility for all damages.
B. Continue to require XYZ's hiring personnel to manually screen all applicants.
C. Direct the procurement team to select the most economical Al tool.
D. Test the Al tool pre- and post-deployment.

Answer: D

Explanation:
To mitigate the risk of reputational harm from using an AI hiring tool, XYZ Corp should rigorously test the AI tool both before and after deployment. Pre-deployment testing ensures the tool works correctly and does not introduce bias or other issues. Post-deployment testing ensures the tool continues to operate as intended and adapts to any changes in data or usage patterns. This approach helps to identify and address potential issues proactively, thereby reducing the risk of reputational harm. Ensuring the vendor assumes responsibility for damages (B) does not address the root cause of potential issues, selecting the most economical tool (C) may compromise quality, and continuing manual screening (D) defeats the purpose of using the AI tool.

NEW QUESTION # 33
Testing data is defined as a subset of data that is used to?

A. Evaluate a model's handling of randomized edge cases.
B. Assess a model's on-going performance in production.
C. Provide a robust evaluation of a final model.
D. Enable a model to discover and learn patterns.

Answer: C

Explanation:
Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.

NEW QUESTION # 34
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT?

A. Retaining system-generated logs for at least six months.
B. Conducting post-market monitoring.
C. Conducting a conformity assessment.
D. Publishing a detailed report on the training data used.

Answer: D

Explanation:
The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.
From theAI Governance in Practice Report 2024:
"It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations." (p. 34)
"To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle."
"Conducting post-market monitoring" and "conformity assessments" areexplicitrequirements for high-risk systems. (p. 34-35) However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.
* A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.

NEW QUESTION # 35
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

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

Answer: C

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 # 36
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. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
D. Previously unknown properties are discovered in data and used to predict and make improvements in the data.

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 # 37
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