AAIR Practice Test 2 — 10 Questions

Question 1

When assessing the organizational readiness for AI risk governance, which TWO areas must be evaluated to ensure the framework is sustainable?

Accepted Answer

The availability of cross-functional expertise (e.g., legal, compliance, ethics) within the AI governance body.. Sustainable governance requires both the human element (culture/skills) and the procedural element (integration into existing systems).

Answer

The speed of the network bandwidth between training sites.

Answer

The capability of the internal audit team to audit AI models.

Answer

The total number of GPUs available in the data center.

Question 2

Which THREE factors should be monitored to detect 'Model Drift' in the deployment phase?

Accepted Answer

The distribution of prediction outputs.. Feature distribution, target distribution, and model confidence scores all provide signals of drift.

Answer

The temperature of the server room.

Answer

The current market price of GPU hardware.

Question 3

When reporting AI risks to the Board of Directors, which THREE elements should be included to ensure informed decision-making?

Accepted Answer

Qualitative overview of ethical AI alignment with company values.. Board-level reports must focus on strategic impact, compliance posture, and high-level risk exposure.

Answer

List of all developers who worked on the model.

Answer

Granular technical logs of model inference errors.

Question 4

Which document defines the 'AI Risk Appetite' for an organization?

Accepted Answer

The AI Risk Appetite Statement.. The AI Risk Appetite Statement is the document approved by the Board that defines the risk tolerance for AI activities.

Answer

The Employee Training Manual.

Answer

The IT Service Catalog.

Answer

The AI Project Status Report.

Question 5

What is the primary risk associated with 'Shadow AI' in an organization?

Accepted Answer

It bypasses established AI risk governance and compliance controls.. Shadow AI refers to AI tools deployed without the knowledge or oversight of the governance team, creating invisible, unmanaged risks.

Answer

It makes the network run slower.

Answer

It costs too much.

Answer

It makes the developers work too hard.

Question 6

A firm is integrating AI into its ERM (Enterprise Risk Management). What is the primary benefit of a 'Common Risk Taxonomy' for AI?

Accepted Answer

It ensures consistent risk identification, communication, and reporting across the organization.. A common taxonomy ensures that AI risk is understood and communicated consistently across the enterprise.

Answer

It forces all AI models to be identical.

Answer

It makes AI code easier to write.

Answer

It eliminates the need for risk assessments.

Question 7

You are assessing risk for an AI model that uses 'Transfer Learning'. What is the most critical risk to manage regarding the pre-trained base model?

Accepted Answer

The base model may have been trained on data that contains inherent, unidentifiable biases.. Transfer learning risks include the inheritance of latent biases from the pre-trained weights.

Answer

The base model cannot be used with custom datasets.

Answer

The base model will always run out of memory.

Answer

The base model requires a proprietary operating system.

Question 8

Which THREE factors contribute to 'Data Quality Risk' during the training phase?

Accepted Answer

Incomplete datasets with many missing values.. Incomplete data, selection bias, and incorrect labeling all directly degrade the model's foundation.

Answer

The model is too large for the GPU memory.

Answer

Using the wrong cloud vendor.

Question 9

A firm is establishing an AI Governance Committee. Which TWO groups should be represented to ensure comprehensive oversight?

Accepted Answer

Data Privacy Office. Legal/Compliance and Data Privacy are essential for addressing the regulatory and ethical risks inherent in AI deployments.

Answer

Public relations

Answer

Hardware vendors

Answer

External marketing agencies

Question 10

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.

Answer

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

Answer

Delegate all risk assessments to the external software vendor.