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AIGP Practice Questions

Question # 1
A U.S. mortgage company developed an Al platform that was trained using anonymized details from mortgage applications, including the applicant’s education, employment and demographic information, as well as from subsequent payment or default information. The Al platform will be used automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default.

Which of the following laws is NOT relevant to this use case?
A. Fair Housing Act.
B. Fair Credit Reporting Act.
C. Equal Credit Opportunity Act.
D. Title VII of the Civil Rights Act of 1964.


D. Title VII of the Civil Rights Act of 1964.

Explanation:

The U.S. mortgage company's AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).

Question # 2
What is the term for an algorithm that focuses on making the best choice achieve an immediate objective at a particular step or decision point, based on the available information and without regard for the longer-term best solutions?
A. Single-lane.
B. Optimized.
C. Efficient.
D. Greedy.


D. Greedy.

Explanation:

A greedy algorithm is one that makes the best choice at each step to achieve an immediate objective, without considering the longer-term consequences. It focuses on local optimization at each decision point with the hope that these local solutions will lead to an optimal global solution. However, greedy algorithms do not always produce the best overall solution for certain problems, but they are useful when an immediate, locally optimal solution is desired. Reference: AIGP Body of Knowledge, algorithm types section.

Question # 3
Which of the following most encourages accountability over Al systems?
A. Determining the business objective and success criteria for the Al project.
B. Performing due diligence on third-party Al training and testing data.
C. Defining the roles and responsibilities of Al stakeholders.
D. Understanding Al legal and regulatory requirements.


C. Defining the roles and responsibilities of Al stakeholders.

Explanation:

Defining the roles and responsibilities of AI stakeholders is crucial for encouraging accountability over AI systems. Clear delineation of who is responsible for different aspects of the AI lifecycle ensures that there is a person or team accountable for monitoring, maintaining, and addressing issues that arise. This accountability framework helps in ensuring that ethical standards and regulatory requirements are met, and it facilitates transparency and traceability in AI operations. By assigning specific roles, organizations can better manage and mitigate risks associated with AI deployment and use.

Question # 4
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?
A. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
B. Momentum, which improves the convergence speed and stability of neural network training.
C. Autoregression, which analyzes and makes predictions about time-series data.
D. Backpropagation, which starts from the last layer working backwards.


C. Autoregression, which analyzes and makes predictions about time-series data.

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​​.

Question # 5
You are part of your organization’s ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating. What is the best first step address this?
A. Replace the model with a previous version.
B. Conduct champion/challenger testing.
C. Perform an audit of the model.
D. Run red-teaming exercises.


B. Conduct champion/challenger testing.

Explanation:

When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.

[Reference: AIGP BODY OF KNOWLEDGE, sections on model performance management and testing strategies., , ]

Question # 6
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. Anonymization.
B. Pseudonymization.
C. Differential privacy.
D. Federated learning.


A. Anonymization.

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.


Question # 7
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?
A. Data architects.
B. Government regulators.
C. Socio-cultural and technical experts.
D. Legal and privacy governance experts.


B. Government regulators.

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.

Question # 8
Which type of existing assessment could best be leveraged to create an Al impact assessment?
A. A safety impact assessment.
B. A privacy impact assessment.
C. A security impact assessment.
D. An environmental impact assessment.


B. A privacy impact assessment.

Explanation:

A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI.

Reference:

AIGP Body of Knowledge on Impact Assessments.

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IAPP AIGP Exam Dumps

Exam Name: Artificial Intelligence Governance Professional
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