IAPP AIGP Exam Questions - Navigate Your Path to Success

The IAPP Artificial Intelligence Governance Professional (AIGP) exam is a good choice for AI project managers AI governance professionals and if the candidate manages to pass IAPP Artificial Intelligence Governance Professional exam, he/she will earn IAPP Certification Programs Certification. Below are some essential facts for IAPP AIGP exam candidates:

In actual IAPP Artificial Intelligence Governance Professional (AIGP) exam, a candidate can expect 100 Questions and the officially allowed time is expected to be around 180 Minutes.
TrendyCerts offers 100 Questions that are based on actual IAPP AIGP syllabus.
Our IAPP AIGP Exam Practice Questions were last updated on: Feb 28, 2025

Sample Questions for IAPP AIGP Exam Preparation

Question 1

CASE STUDY

Please use the following answer the next question:

A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.

The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.

The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.

The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.

During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?

ATo comply with applicable law.

BTo assist the fairness of the Al system.

CTo evaluate the reliability of the Al system.

DTo determine the explainability of the Al system.

Correct : B

The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.

Options Selected by Other Users:

Question 2

Retraining an LLM can be necessary for all of the following reasons EXCEPT?

ATo minimize degradation in prediction accuracy due to changes in data.

BAdjust the model's hyper parameters specific use case.

CAccount for new interpretations of the same data.

DTo ensure interpretability of the model's predictions.

Correct : D

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.