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IAPP AIGP Exam (page: 1)
IAPP Artificial Intelligence Governance Professional
Updated on: 25-Sep-2025

Viewing Page 1 of 22
AIGP PDF 164 Questions

Machine learning is best described as a type of algorithm by which?

  1. Systems can mimic human intelligence with the goal of replacing humans.
  2. Systems can automatically improve from experience through predictive patterns.
  3. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
  4. Previously unknown properties are discovered in data and used to predict and make improvements in the data.

Answer(s): B

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.



You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative Al tool recommended five restaurants for you to visit. After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative Al tool is an example of what is commonly called?

  1. Prompt injection.
  2. Model collapse.
  3. Hallucination.
  4. Overfitting.

Answer(s): C

Explanation:

In the context of AI, particularly generative models, "hallucination" refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination.


Reference:

AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AImodels.



Each of the following actors are typically engaged in the Al development life cycle EXCEPT?

  1. Data architects.
  2. Government regulators.
  3. Socio-cultural and technical experts.
  4. Legal and privacy governance experts.

Answer(s): B

Explanation:

Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects). Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry.


Reference:

AIGP BODY OF KNOWLEDGE and AI development frameworks.



A company is working to develop a self-driving car that can independently decide the appropriate route to take the driver after the driver provides an address. If they want to make this self-driving car "strong" Al, as opposed to "weak," the engineers would also need to ensure?

  1. Thatthe Al has full human cognitive abilities that can independently decide where to take the driver.
  2. That they have obtained appropriate intellectual property (IP) licenses to use data for training the Al.
  3. That the Al has strong cybersecurity to prevent malicious actors from taking control of the car.
  4. That the Al can differentiate among ethnic backgrounds of pedestrians.

Answer(s): A

Explanation:

Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive abilities. For the self-driving car to be classified as "strong" AI, it would need to possess full human cognitive abilities to make independent decisions beyond pre-programmed instructions.


Reference:

AIGP BODY OF KNOWLEDGE and AI classifications.



Which of the following is NOT a common type of machine learning?

  1. Deep learning.
  2. Cognitive learning.
  3. Unsupervised learning.
  4. Reinforcement learning.

Answer(s): B

Explanation:

The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology.


Reference:

AIGP BODY OF KNOWLEDGE and standard AI/ML literature.



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 t

  1. 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.
    The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?
  2. A transparency notice.
  3. An opt-out mechanism.
  4. Detailed terms of service.
  5. Customer service support.

Answer(s): A

Explanation:

The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.


Reference:

According to the AIGP Body of Knowledge, transparency in AI systems is essential to ensure that stakeholders, including customers, understand how their data is being used and how decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are informed and can make knowledgeable decisions regarding their interactions with AI systems.



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 theaccuracy, 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.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

  1. Toprovide a reminder of a right appeal.
  2. Tosolicit on-going feedback on model performance.
  3. Toapply their own judgment to the initial assessment.
  4. Toensure they provide transparency applicants on the model.

Answer(s): C

Explanation:

Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment.
While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.


Reference:

The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.



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 .. 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.
During the first month when ABC monitors the model for bias, it is most important to?

  1. Continue disparity testing.
  2. Analyze the quality of the training and testing data.
  3. Compare the results to human decisions prior to deployment.
  4. Seek approval from management for any changes to the model.

Answer(s): A

Explanation:

During the first month of monitoring the model for bias, it is most important to continue disparity testing. Disparity testing involves regularly evaluating the model's decisions to identify and address any biases, ensuring that the model operates fairly across different demographic groups.


Reference:

Regular disparity testing is highlighted in the AIGP Body of Knowledge as a critical practice for maintaining the fairness and reliability of AI models. By continuously monitoring for and addressing disparities, organizations can ensure their AI systems remain compliant with ethical and legal standards, and mitigate any unintended biases that may arise in production.



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