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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q64-Q69):

NEW QUESTION # 64
A company initially intended to use a large data set containing personal information to train an Al model.
After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?

A. Federated learning.
B. Differential privacy.
C. Pseudonymization.
D. Anonymization.

Answer: D

Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.

NEW QUESTION # 65
Scenario:
A public sector agency is reviewing proposed AI use cases for improving services. It wants to prioritize implementations that deliver value but minimize unintended negative consequences.
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:

A. Equitable access to the AI tool
B. The users and their expectations
C. Related TEVV (test, evaluate, verify, validate) and system metrics
D. Potential positive and negative impacts of the system

Answer: C

Explanation:
The correct answer is A. While TEVV is important in later lifecycle phases, it is not the primary consideration when evaluating and prioritizing use cases.
From the AIGP Body of Knowledge - Use Case Assessment Module:
"Use case evaluation focuses on value, impact, fairness, and accessibility-technical testing considerations come later." ILT Guide confirms:
"Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization." Thus, A is not a top-level consideration during use case selection.

NEW QUESTION # 66
A company plans on procuring a tool from an AI provider for its employees to use for certain business purposes. Which contractual provision would best protect the company's intellectual property in the tool, including training and testing data?

A. The provider will obtain and maintain insurance to cover potential claims.
B. The provider will give privacy notice to individuals before using their personal data to train or test the tool.
C. The provider will warrant that the tool will work as intended.
D. The provider will defend and indemnify the company against infringement claims.

Answer: D

Explanation:
Indemnification provisions protect the company against intellectual property infringement claims arising from the provider's tool, including data used in training and testing.

NEW QUESTION # 67
In the machine learning context, feature engineering is the process of?

A. Developing guidelines to train and test a model.
B. Creating learning schema for a model apply.
C. Converting raw data into clean data.
D. Extracting attributes and variables from raw data.

Answer: D

Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.

NEW QUESTION # 68
During the first month when the company monitors the model for bias, it is most important to?

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

Answer: C

Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking forbias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report2025:
"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 # 69
......

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