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AIGP Practice Questions

Question # 1
Random forest algorithms are in what type of machine learning model?
A. Symbolic.
B. Generative.
C. Discriminative.
D. Natural language processing.


C. Discriminative.

Explanation:

Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.

[Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks​​., , ]

Question # 2
An EU bank intends to launch a multi-modal Al platform for customer engagement and automated decision-making assist with the opening of bank accounts. The platform has been subject to thorough risk assessments and testing, where it proves to be effective in not discriminating against any individual on the basis of a protected class. What additional obligations must the bank fulfill prior to deployment?
A. The bank must obtain explicit consent from users under the privacy Directive.
B. The bank must disclose how the Al system works under the Ell Digital Services Act.
C. The bank must subject the Al system an adequacy decision and publish its appropriate safeguards.
D. The bank must disclose the use of the Al system and implement suitable measures for users to contest automated decision-making.


D. The bank must disclose the use of the Al system and implement suitable measures for users to contest automated decision-making.

Explanation:

Under the EU regulations, particularly the GDPR, banks using AI for decision-making must inform users about the use of AI and provide mechanisms for users to contest decisions. This is part of ensuring transparency and accountability in automated processing. Explicit consent under the privacy directive (A) and disclosing under the Digital Services Act (B) are not specifically required in this context. An adequacy decision is related to data transfers outside the EU (C).

Question # 3
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?
A. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
B. Employing a new software platform to modernize existing compliance processes across the organization.
C. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.


B. Employing a new software platform to modernize existing compliance processes across the organization.

Explanation:

When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles.

Reference:

AIGP Body of Knowledge on AI Governance and Compliance Integration.

Question # 4
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 # 5
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A. Perform a readiness assessment.
B. Define a model-validation methodology.
C. Document maintenance teams and processes.
D. Identify known edge cases to monitor post-deployment.


A. Perform a readiness assessment.

Explanation:

After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness.

Reference:

AIGP Body of Knowledge on Deployment Readiness.

Question # 6
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 # 7
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.

Question # 8
Training data is best defined as a subset of data that is used to?
A. Enable a model to detect and learn patterns.
B. Fine-tune a model to improve accuracy and prevent overfitting.
C. Detect the initial sources of biases to mitigate prior to deployment.
D. Resemble the structure and statistical properties of production data.


A. Enable a model to detect and learn patterns.

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.

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IAPP AIGP Exam Dumps

Exam Name: Artificial Intelligence Governance Professional
Certification Name: Artificial Intelligence Governance

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