AIGP Exam Questions and Answers | Exam-Dumps (Page: 2)

QUESTION: 9 Exam Topic: 1, Part 1

Case Study:

Please use the following answer the next question:

ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.

ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data--including applications, policies, and claims--and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed tA. human underwriter for final review.

ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.

Each of the following steps would support fairness testing by the compliance team during the first month in production EXCEPT?

Validating a similar level of decision-making across different demographic groups.
Providing the loan applicants with information about the model capabilities and limitations.
Identifying if additional training data should be collected for specific demographic groups.
Using tools to help understand factors that may account for differences in decision-making.

Answer(s): B

Explanation:

Providing the loan applicants with information about the model capabilities and limitations would not directly support fairness testing by the compliance team. Fairness testing focuses on evaluating the model's decisions for biases and ensuring equitable treatment across different demographic groups, rather than informing applicants about the model.

Reference:

The AIGP Body of Knowledge outlines that fairness testing involves technical assessments such as validating decision-making consistency across demographics and using tools to understand decision factors.
While transparency to applicants is important for ethical AI use, it does not contribute directly to the technical process of fairness testing.

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QUESTION: 10 Exam Topic: 1, Part 1

Case Study:

Please use the following answer the next question:

ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.

ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data--including applications, policies, and claims--and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.

ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.

What is the best strategy to mitigate the bias uncovered in the loan applications?

Retrain the model with data that reflects demographic parity.
Procure a third-party statistical bias assessment tool.
Document all instances of bias in the data set.
Delete all gender-based data in the data set.

Answer(s): A

Explanation:

Retraining the model with data that reflects demographic parity is the best strategy to mitigate the bias uncovered in the loan applications. This approach addresses the root cause of the bias by ensuring that the training data is representative and balanced, leading to more equitable decision- making by the AI model.

Reference:

The AIGP Body of Knowledge stresses the importance of using high-quality, unbiased training data to develop fair and reliable AI systems. Retraining the model with balanced data helps correct biases that arise from historical inequalities, ensuring that the AI system makes decisions based on equitable criteria.

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QUESTION: 11 Exam Topic: 1, Part 1

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

Segmentation.
Generative Al.
Expert systems.
Supervised learning.

Answer(s): D

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).

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QUESTION: 12 Exam Topic: 1, Part 1

A company developed Al technology that can analyze text, video, images and sound to tag content, including the names of animals, humans and objects.

What type of Al is this technology classified as?

Deductive inference.
Multi-modal model.
Transformative Al.
Expert system.

Answer(s): B

Explanation:

A multi-modal model is an AI system that can process and analyze multiple types of data, such as text, video, images, and sound. This type of AI integrates different data sources to enhance its understanding and decision-making capabilities. In the given scenario, the AI technology that tags content including names of animals, humans, and objects falls under this category.

Reference:

AIGP BODY OF KNOWLEDGE, which outlines the capabilities and use cases of multi-modal models.

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QUESTION: 13 Exam Topic: 1, Part 1

All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?

Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
Momentum, which improves the convergence speed and stability of neural network training.
Autoregression, which analyzes and makes predictions about time-series data.
Backpropagation, which starts from the last layer working backwards.

Answer(s): C

Explanation:

Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization.

Reference:

AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.

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QUESTION: 14 Exam Topic: 1, Part 1

What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?

GPUs run many tasks concurrently, resulting in faster processing.
GPUs can access memory quickly, resulting in lower latency than CPUs.
GPUs can run every task on a computer, making them more robust than CPUs.
The number of transistors on GPUs doubles every two years, making the chips smaller and lighter.

Answer(s): A

Explanation:

GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks.

Reference:

AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.

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QUESTION: 15 Exam Topic: 1, Part 1

Which of the following best defines an "Al model"?

A system that applies defined rules to execute tasks.
A system of controls that is used to govern an Al algorithm.
A corpus of data which an Al algorithm analyzes to make predictions.
A program that has been trained on a set of data to find patterns within the data.

Answer(s): D

Explanation:

An AI model is best defined as a program that has been trained on a set of data to find patterns within that data. This definition captures the essence of machine learning, where the model learns from the data to make predictions or decisions.

Reference:

AIGP BODY OF KNOWLEDGE, which provides a detailed explanation of AI models and their training processes.

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QUESTION: 16 Exam Topic: 1, Part 1

Case Study:

Please use the following answer the next question:

Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.

In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.

GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.

Which of the following risks should be of the highest concern to individual teachers using generative Al to ensure students learn the course material?

Financial cost.
Model accuracy.
Technical complexity.
Copyright infringement.

Answer(s): B

Explanation:

The highest concern for individual teachers using generative AI to ensure students learn the course material is model accuracy. Ensuring that the AI-generated content is accurate and relevant to the curriculum is crucial for effective learning. If the AI model produces inaccurate or irrelevant content, it can mislead students and hinder their understanding of the subject matter.

Reference:

According to the AIGP Body of Knowledge, one of the core risks posed by AI systems is the accuracy of the data and models used. Ensuring the accuracy of AI-generated content is essential for maintaining the integrity of the educational material and achieving the desired learning outcomes.

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IAPP AIGP Exam (page: 2) IAPP Artificial Intelligence Governance Professional Updated on: 12-Feb-2026

QUESTION: 9 Exam Topic: 1, Part 1

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QUESTION: 10 Exam Topic: 1, Part 1

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QUESTION: 11 Exam Topic: 1, Part 1

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QUESTION: 12 Exam Topic: 1, Part 1

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QUESTION: 13 Exam Topic: 1, Part 1

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QUESTION: 14 Exam Topic: 1, Part 1

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QUESTION: 15 Exam Topic: 1, Part 1

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QUESTION: 16 Exam Topic: 1, Part 1

Explanation:

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IAPP AIGP Exam (page: 2)
IAPP Artificial Intelligence Governance Professional
Updated on: 12-Feb-2026