AAIR AI Risk Program Management Practice Test 2 — 15 Questions

Question 1

In an AI risk program, how should 'Residual Risk' be treated?

Accepted Answer

It must be formally documented and periodically reviewed against risk appetite. Residual risk is the risk that remains after controls are applied; it must be periodically reviewed against the established appetite.

Answer

It should be ignored if below a specific performance threshold

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It should always be zero for production systems

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It should be formally accepted by the risk owner if it exceeds appetite

Question 2

To ensure cross-functional coordination, you are designing a workflow for AI incident reporting. Which group should be the first point of contact upon detecting an AI model bias issue?

Accepted Answer

AI Governance/Ethics Committee. The AI Governance team or AI Ethics office is tasked with immediate triage of AI-specific risks before escalation to legal or IT operations.

Answer

IT Help Desk

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Customer Support

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External Regulators

Question 3

Which of the following is an example of an 'AI Risk Metric'?

Accepted Answer

Rate of false negatives in model output. The rate of false negatives in a credit approval model is a direct measure of AI-specific operational risk.

Answer

Budget spent on office supplies

Answer

Employee headcount in AI department

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Number of coffee machines in the breakroom

Question 4

What is the primary purpose of an 'AI Risk Dashboard' for executive leadership?

Accepted Answer

To provide high-level visibility into AI risk posture and trends. Dashboards allow leadership to see aggregated risk trends across the enterprise, not just individual project metrics.

Answer

To replace the need for an AI ethics committee

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To provide raw model logs to the public

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To monitor individual developer productivity

Question 5

Which of the following is a key component of 'AI Risk Governance'?

Accepted Answer

The definition of roles and responsibilities. Clear roles and responsibilities are essential for accountability, which is a core component of governance.

Answer

The choice of programming language

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The vendor of the cloud hosting platform

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The speed of model deployment

Question 6

When integrating AI risk into the broader ERM (Enterprise Risk Management) framework, which approach is most effective?

Accepted Answer

Map AI risks to existing risk categories (e.g., operational, compliance). AI risks should not be siloed; they should be categorized alongside existing operational and reputational risks to ensure they are visible to the Board.

Answer

Only report AI risks if they result in a financial loss

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Keep AI risks in a separate, isolated tracking system

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Automate the removal of AI risks from the main risk register

Question 7

When performing an AI risk assessment, which question is most appropriate to ask?

Accepted Answer

What is the potential impact if the model produces biased results?. Risk assessment focuses on impact and likelihood, and the potential for unintended harm is the core of AI risk analysis.

Answer

How many people are on the development team?

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What is the model's highest accuracy score?

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Does the model use open-source libraries?

Question 8

Which documentation is necessary to provide to auditors for an AI system?

Accepted Answer

The model card or model documentation. A model card provides the necessary metadata, performance limitations, and intended use cases required for auditability.

Answer

A list of all employees who attended a conference

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The login credentials for the production environment

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The complete source code without comments

Question 9

A company is utilizing a third-party LLM service. Which risk is specifically heightened by this arrangement?

Accepted Answer

Data leakage and privacy violations. Data leakage is a significant risk when sending proprietary or sensitive data to external AI service providers.

Answer

Server downtime

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Model training cost inflation

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Lack of software updates

Question 10

Why is 'Data Lineage' important in an AI risk management program?

Accepted Answer

It helps ensure data quality and integrity. Data lineage allows you to trace data back to its source, which is critical for identifying and mitigating bias or training data contamination.

Answer

It makes user interfaces more attractive

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It improves server load times

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It reduces the amount of storage needed

Question 11

What is the primary responsibility of a 'Model Owner' in an AI risk framework?

Accepted Answer

Maintaining the risk profile and performance of the model. The model owner is accountable for the entire lifecycle and risk profile of a specific AI model.

Answer

Writing all the Python code for the model

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Approving company-wide bonuses

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Fixing broken office furniture

Question 12

What is the primary reason for maintaining an 'AI Model Inventory'?

Accepted Answer

To identify and mitigate shadow AI risks. An inventory ensures that no 'shadow AI' exists, allowing for comprehensive risk assessment and oversight.

Answer

To share model code with competitors

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To make the database easier to back up

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To increase the size of the IT department

Question 13

In a cross-functional AI governance meeting, the Data Science team wants to deploy a new model, but the Legal team is concerned about data privacy. How should the AI Risk Manager resolve this?

Accepted Answer

Conduct a joint risk assessment to identify potential mitigations. The manager should facilitate a risk assessment to quantify the concern and identify potential technical controls to mitigate the privacy risk.

Answer

Order the Data Science team to stop work immediately

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Ask the CEO to decide

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Ignore the Legal team's concerns

Question 14

When assessing the risk of AI-generated content, which factor is the most important for calculating 'Impact'?

Accepted Answer

The potential for reputational and legal consequences. The potential for reputational or legal harm (consequence) is the most critical component when assessing the impact of AI-generated content.

Answer

The speed at which the content is generated

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The color of the font used for the output

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The number of GPUs used to create the content

Question 15

What is 'Bias' in the context of AI risk?

Accepted Answer

Systematic prejudice in model outputs leading to unfair results. Bias refers to systematic errors that lead to unfair or discriminatory outcomes, posing both social and regulatory risks.

Answer

The model is missing documentation

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The model is too expensive to maintain

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The model is running too fast