Question # 1
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of
cancer that is most likely arise in adults. Specifically, the healthcare network intends to
create a recognition algorithm that will perform an initial review of all imaging and then
route records a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a
confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical
principles: conducted discovery to identify the intended uses and success criteria for the
system: established an Al governance committee; assembled a broad, cross functional
team with clear roles and responsibilities; and created policies and procedures to document
standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a
consulting firm to help develop the algorithm using the healthcare network's existing data
and de-identified data that is licensed from a large US clinical research partner.
The most significant risk from combining the healthcare network’s existing data with the
clinical research partner data is? |
| A. Privacy risk. | | B. Security risk. | | C. Operational risk. | | D. Reputational risk. |
A. Privacy risk.
Explanation:
The most significant risk from combining the healthcare network’s existing data with the
clinical research partner data is privacy risk. Combining data sets, especially in healthcare,
often involves handling sensitive information that could lead to privacy breaches if not
managed properly. De-identified data can still pose re-identification risks when combined
with other data sets. Ensuring privacy involves implementing robust data protection
measures, maintaining compliance with privacy regulations such as HIPAA, and conducting
thorough privacy impact assessments.
Question # 2
| Which of the following deployments of generative Al best respects intellectual property
rights? |
| A. The system produces content that is modified to closely resemble copyrightedwork. | | B. The system categorizes and applies filters to content based on licensing terms. | | C. The system provides attribution to creators of publicly available information. | | D. The system produces content that includes trademarks and copyrights. |
B. The system categorizes and applies filters to content based on licensing terms.
Explanation:
Respecting intellectual property rights means adhering to licensing terms and ensuring that
generated content complies with these terms. A system that categorizes and applies filters
based on licensing terms ensures that content is used legally and ethically, respecting the
rights of content creators. While providing attribution is important, categorization and
application of filters based on licensing terms are more directly tied to compliance with
intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge
sections on intellectual property and compliance.
Question # 3
| What is the main purpose of accountability structures under the Govern function of the
NIST Al Risk Management Framework? |
| A. To empower and train appropriate cross-functional teams. | | B. To establish diverse, equitable and inclusive processes. | | C. To determine responsibility for allocating budgetary resources. | | D. To enable and encourage participation by external stakeholders. |
A. To empower and train appropriate cross-functional teams.
Explanation:
The NIST AI Risk Management Framework’s Govern function emphasizes the importance
of establishing accountability structures that empower and train cross-functional teams.
This is crucial because cross-functional teams bring diverse perspectives and expertise,
which are essential for effective AI governance and risk management. Training these
teams ensures that they are well-equipped to handle their responsibilities and can make
informed decisions that align with the organization’s AI principles and ethical standards.
Question # 4
An artist has been using an Al tool to create digital art and would like to ensure that it has
copyright protection in the United States.
Which of the following is most likely to enable the artist to receive copyright protection? |
| A. Ensure the tool was trained using publicly available content. | | B. Obtain a representation from the Al provider on how the tool works. | | C. Provide a log of the prompts the artist used to generate the images. | | D. Update the images in a creative way to demonstrate that it is the artist's. |
D. Update the images in a creative way to demonstrate that it is the artist's.
Explanation:
For the artist to receive copyright protection, the most effective approach is to demonstrate
that the final artwork includes sufficient creative input by the artist. By updating or altering
the images in a way that reflects the artist's personal creativity, the artist can claim
originality, which is a core requirement for copyright protection under U.S. law. The other
options do not directly address the originality and creative input required for copyright. This
is highlighted in the sections on copyright protection in the IAPP AIGP Body of Knowledge.
Question # 5
| When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT? |
| A. Feature drift. | | B. System cost. | | C. Model drift. | | D. Data loss. |
B. System cost.
Explanation:
When monitoring the functional performance of a model deployed into production, concerns
typically include feature drift, model drift, and data loss. Feature drift refers to changes in
the input features that can affect the model's predictions. Model drift is when the model's
performance degrades over time due to changes in the data or environment. Data loss can
impact the accuracy and reliability of the model. However, system cost, while important for
budgeting and financial planning, is not a direct concern when monitoring the functional
performance of a deployed model.
Question # 6
| What is the technique to remove the effects of improperly used data from an ML system? |
| A. Data cleansing. | | B. Model inversion. | | C. Data de-duplication. | | D. Model disgorgement. |
D. Model disgorgement.
Explanation:
Model disgorgement is the technique used to remove the effects of improperly used data
from an ML system. This process involves retraining or adjusting the model to eliminate
any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's
outputs are not influenced by data that was not meant to be used or was used incorrectly.
Question # 7
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of
cancer that is most likely arise in adults. Specifically, the healthcare network intends to
create a recognition algorithm that will perform an initial review of all imaging and then
route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a
confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical
principles: conducted discovery to identify the intended uses and success criteria for the
system: established an Al governance committee; assembled a broad, crossfunctional
team with clear roles and responsibilities; and created policies and procedures to document
standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a
consulting firm to help develop the algorithm using the healthcare network's existing data
and de-identified data that is licensed from a large US clinical research partner.
Which stakeholder group is most important in selecting the specific type of algorithm? |
| A. The cloud provider. | | B. The consulting firm. | | C. The healthcare network's data science team. | | D. The healthcare network's Al governance committee. |
C. The healthcare network's data science team.
Explanation: In selecting the specific type of algorithm for the AI solution, the healthcare
network's data science team is most important. This team possesses the technical
expertise and understanding of the data, the clinical context, and the performance
requirements needed to make an informed decision about which algorithm is most suitable.
While the cloud provider and consulting firm can offer support and infrastructure, and the AI
governance committee provides oversight, the data science team’s specialized knowledge
is crucial for selecting and implementing the appropriate algorithm.
Question # 8
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of
cancer that is most likely arise in adults. Specifically, the healthcare network intends to
create a recognition algorithm that will perform an initial review of all imaging and then
route records a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a
confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical
principles: conducted discovery to identify the intended uses and success criteria for the
system: established an Al governance committee; assembled a broad, cross functional
team with clear roles and responsibilities; and created policies and procedures to document
standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data
and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to
training and testing the Al solution? |
| A. Procure more data from clinical research partners. | | B. Engage a third party to perform an audit. | | C. Perform an impact assessment. | | D. Create a bias bounty program. |
C. Perform an impact assessment.
Explanation: Performing an impact assessment is the best step to mitigate the possibility
of discrimination before training and testing the AI solution. An impact assessment, such as
a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA),
helps identify potential biases and discriminatory outcomes that could arise from the AI
system. This process involves evaluating the data and the algorithm for fairness,
accountability, and transparency. It ensures that any biases in the data are detected and
addressed, thus preventing discriminatory practices and promoting ethical AI deployment.
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Artificial Intelligence Governance Professional Test Dumps
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Questions People Ask About AIGP Exam
Artificial Intelligence Governance Professional (AIGP) is a certification offered by the IAPP. This certification is valuable for roles in compliance, privacy, security, risk management, legal, HR, and governance, as well as for data scientists, AI project managers, and business analysts.
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