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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 3	Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 4	Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and copyright’s Bill C-27.
Topic 5	Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.

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IAPP AIGP Questions - AIGP Exam Lab Questions

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

A. Supervised learning.
B. Segmentation.
C. Generative Al.
D. Expert systems.

Answer: A

Explanation:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference:
AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).

NEW QUESTION # 43
Training data is best defined as a subset of data that is used to?

A. Enable a model to detect and learn patterns.
B. Detect the initial sources of biases to mitigate prior to deployment.
C. Fine-tune a model to improve accuracy and prevent overfitting.
D. Resemble the structure and statistical properties of production data.

Answer: A

Explanation:
Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.

NEW QUESTION # 44
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.
When notifying an accused perpetrator, what additional information should a police officer provide about the use of the Al system?

A. Information about how the individual was identified by the Al system.
B. Information about the composition of the training data of the system.
C. Information about how the accused can oppose the charges.
D. Information about the accuracy of the Al system.

Answer: A

Explanation:
When notifying an accused perpetrator, the police officer should provide information about how the individual was identified by the AI system. This transparency is crucial for maintaining trust and ensuring that the accused understands the basis of the charges against them. Information about the accuracy, how to oppose the charges, and the composition of the training data, while potentially relevant, do not directly address the immediate need for the accused to understand the specific process that led to their identification. Reference:
AIGP Body of Knowledge on AI Transparency and Explainability.

NEW QUESTION # 45
Scenario:
A distributor operating in the EU is responsible for selling imported high-risk AI systems to businesses. The distributor wants to ensure they fulfill all applicable obligations under the EU AI Act.
All of the following are obligations of a distributor of high-risk AI systems under the EU AI Act EXCEPT?

A. Corrective actions
B. Communication with national authorities
C. Verification of CE marking
D. Registration in EU Database

Answer: D

Explanation:
The correct answer is C. Registration in the EU database is an obligation of providers of high-risk AI systems-not distributors.
From the AIGP ILT Guide - Roles & Obligations Module:
"Distributors must verify CE marking, ensure instructions for use are provided, inform authorities of risks, and take corrective action when necessary. However, registration duties in the EU database lie with the provider." Also from the AI Governance in Practice Report 2024:
"The AI Act differentiates responsibilities for developers, providers, importers, and distributors. Only providers of high-risk systems are obligated to register their systems in the EU AI Database." Distributors focus on verification and communication, not formal registration.

NEW QUESTION # 46
To maintain fairness in a deployed system, it is most important to?

A. Monitor for data drift that may affect performance and accuracy.
B. Protect against loss of personal data in the model.
C. Detect anomalies outside established metrics that require new training data.
D. Optimize computational resources and data to ensure efficiency and scalability.

Answer: A

Explanation:
To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.

NEW QUESTION # 47
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NEW AIGP PRACTICE QUESTIONS & AIGP QUESTIONS

New AIGP Practice Questions & AIGP Questions