AAIR Practice Test 7 — 10 Questions

Question 1

In a retail company, which AI risk is most likely to cause direct financial loss?

Accepted Answer

The pricing model provides incorrect, discounted rates. The AI risk most likely to cause direct financial loss in a retail company is a pricing model that provides incorrect, discounted rates. This directly impacts revenue and profitability, as customers may be undercharged or the company may lose sales due to pricing errors. Incorrect pricing can also lead to competitive disadvantages and customer distrust.

Answer

The model documentation is outdated

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The font size in the UI is too small

Answer

The model's name is not catchy

Question 2

You are managing AI supply chain risk. Which control is most critical for third-party AI models?

Accepted Answer

Requiring a documented 'AI Bill of Materials' to understand data lineage and model provenance.. The most critical control for third-party AI models is requiring a documented 'AI Bill of Materials' (AI BOM) to understand data lineage and model provenance. This provides transparency into the components, data sources, and training methods used in the model, enabling effective risk assessment and mitigation. Without this visibility, organizations cannot verify compliance, detect biases, or manage supply chain risks.

Answer

Conducting a manual audit of the vendor's source code.

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Requiring the vendor to submit a monthly performance report.

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Setting a limit on the number of third-party vendors allowed.

Question 3

Which AI lifecycle stage is most susceptible to the risk of 'data leakage' where training data inadvertently contains information from the future/target?

Accepted Answer

Data preparation and training phase.. Data leakage occurs when information that should not be available at inference time (e.g., the target label, future data, or holdout data) is inadvertently included in the training set. This happens during data preparation and training, where feature engineering, joins, and train/test splits are performed. If a feature is computed using the target or future timestamps, the model learns a shortcut that inflates training metrics but fails in production.

Answer

Model monitoring phase.

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Model decommission phase.

Answer

Deployment phase.

Question 4

An organization is integrating AI risk into its existing ISO 31000 framework. How should the 'Risk Assessment' process be modified to account for AI-specific 'black box' issues?

Accepted Answer

Add a model explainability and interpretability assessment step to the process.. Inclusion of model explainability assessments is necessary to address the opacity inherent in deep learning models.

Answer

Remove the risk identification step, as AI risks are too unpredictable.

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Replace the qualitative assessment with a purely quantitative financial impact model.

Answer

Delegate all risk assessments to the external software vendor.

Question 5

An organization is revising its AI Governance to include 'Human-in-the-loop' (HITL) requirements. Why is this a critical risk mitigation strategy?

Accepted Answer

It ensures human oversight for critical decisions, reducing the risk of 'black box' failures.. Human-in-the-loop (HITL) is a critical risk mitigation because it places a human reviewer in the decision path for consequential AI outputs, providing a check against opaque 'black box' failures, edge cases, and harmful errors that automated controls may miss. It preserves meaningful human judgment and accountability where the cost of an AI mistake is high. It does not replace other controls or eliminate testing obligations.

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It removes the requirement for model bias testing.

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It is required for every AI model regardless of impact.

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It eliminates the need for any other automated risk controls.

Question 6

When coordinating AI risk across a global organization, why is it critical to include local legal counsel in the AI steering committee?

Accepted Answer

To interpret the varied regulatory landscapes in different operational regions.. Local legal counsel is essential on a global AI steering committee because AI regulations vary significantly by jurisdiction — the EU AI Act, US state laws, China's algorithm rules, Brazil's LGPD, etc. — and local counsel interprets how these apply to the organization's operations in each region. This ensures the committee's risk decisions are legally grounded and regionally compliant rather than assuming a single global standard.

Answer

To audit the training datasets for copyright infringement.

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To handle administrative tasks for the committee.

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To approve the technical architecture of the AI models.

Question 7

Which TWO strategies are recommended for 'Mitigating' AI model bias?

Accepted Answer

Applying fairness constraints during model training. Option C is correct because applying fairness constraints during model training directly mitigates bias by incorporating fairness metrics (such as demographic parity or equalized odds) into the objective function or as regularization terms, forcing the model to reduce disparate impact across protected groups. Option E is correct because training on more diverse and representative datasets addresses bias at its source—the data—by ensuring the model learns patterns from a broader population, reducing sampling bias and underrepresentation that lead to skewed predictions. Options A, B, and D are not recommended mitigation strategies: deleting all data destroys the training set and makes learning impossible, asking users to manually fix bias shifts responsibility away from systematic model design and is not scalable or reliable, and ignoring bias allows it to persist and potentially worsen in production.

Answer

Removing all data from the database

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Asking the user to manually fix the bias

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Ignoring the bias and hoping it goes away

Question 8

Which THREE factors are essential to consider when determining the 'AI Risk Appetite' for an enterprise?

Accepted Answer

The organization's tolerance for legal and reputational damage.. Option B is correct because AI risk appetite must be calibrated to how much legal exposure and reputational harm the organization is willing to absorb, since these are the primary consequence categories of AI failures and directly bound the acceptable risk threshold. Option C is correct because the complexity and degree of autonomy of the deployed AI models determine the severity and likelihood of adverse outcomes, so higher autonomy or opacity requires a more conservative appetite. Option D is correct because the applicable regulatory landscape and sector-specific compliance obligations (for example, EU AI Act risk tiers, HIPAA, or financial-services rules) set non-negotiable limits that the risk appetite cannot exceed. Option A is not essential because market share is a business-performance metric that does not define risk tolerance boundaries. Option E is not essential because historical data volume is an input to model development and data-governance decisions, not a determinant of how much AI risk the enterprise is willing to accept.

Answer

The current market share of the company.

Answer

Historical data volume.

Question 9

Which TWO of the following techniques help mitigate bias in the AI lifecycle?

Accepted Answer

Conducting fairness audits using specialized metrics (e.g., Disparate Impact).. Option C is correct because conducting fairness audits with specialized metrics such as Disparate Impact (the ratio of favorable outcomes for a protected group to those of a reference group, ideally near 1.0 under the 80% rule) quantitatively detects and helps correct disparate treatment or outcomes across groups in the AI lifecycle. Option D is correct because sourcing training data from diverse, representative populations reduces sampling and representation bias, ensuring the model learns patterns that generalize fairly rather than overfitting to a dominant or historically skewed subgroup. Option A is incorrect because the programming language (e.g., Java) is irrelevant to bias; bias arises from data, objectives, and evaluation, not from the implementation language. Option B is incorrect because reducing model monitoring would hide drift and emerging bias, worsening rather than mitigating it. Option E is incorrect because simply adding more features can amplify bias through proxy variables and increase overfitting without addressing underlying data or fairness issues.

Answer

Switching the programming language to Java.

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Reducing the frequency of model monitoring.

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Increasing the number of features in the model.

Question 10

A company is deploying an AI system that interacts with human users. According to the AI RMF, what should be the focus of the 'Map' function in the risk governance process?

Accepted Answer

Identifying stakeholders, intended use, and potential human impacts.. In the NIST AI RMF, the Map function focuses on understanding the context of the AI system: identifying stakeholders, intended use, affected populations, and potential human impacts. It establishes the foundation for risk assessment by framing what the system does, who it affects, and what could go wrong. This contextual mapping is essential before measuring or managing risks.

Answer

Choosing the most efficient algorithm.

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Selecting the cloud service provider.

Answer

Calculating the total budget for the project.