AAIA Practice Test 4 — 10 Questions

Question 1

When scoping an audit for a model using SHAP values, what is the primary objective of the auditor?

Accepted Answer

Explainability of model predictions. SHAP (SHapley Additive exPlanations) values are a game-theoretic method for attributing a model's prediction to its input features, so an audit scoped around SHAP is fundamentally about explainability of model predictions. The auditor's primary objective is to verify that the model's decisions can be understood and justified per-feature. This supports regulatory, fairness, and trust requirements rather than operational concerns.

Answer

Detecting data drift

Answer

Security vulnerability scanning

Answer

Infrastructure scaling

Question 2

Which THREE factors influence 'AI Pipeline' latency?

Accepted Answer

Model parameter size. Option B (Model parameter size) is correct because larger models require more computation per inference, increasing processing time and thus pipeline latency. Option C (Network transfer speed) is correct because AI pipelines often move data between storage, preprocessing, and inference services, and slower bandwidth or higher latency links directly add to end-to-end delay. Option D (Data preprocessing complexity) is correct because heavy operations such as tokenization, resizing, normalization, or feature engineering consume CPU/GPU cycles before inference, extending total pipeline latency. Options A (Company CEO name) and E (Office building height) are irrelevant metadata that have no technical effect on compute, transfer, or processing time in an AI pipeline.

Answer

Company CEO name

Answer

Office building height

Question 3

In a financial services firm, you are auditing the 'Explainability' of a credit scoring model. The model uses complex non-linear features. Which technique should you verify is being used to provide local explanations for individual credit decisions?

Accepted Answer

SHAP values. SHAP (SHapley Additive exPlanations) values are grounded in cooperative game theory and assign each feature a contribution to a single prediction, producing faithful local explanations even for complex non-linear models. For a credit-scoring audit, SHAP lets you explain why one specific applicant was approved or denied by decomposing that individual prediction into per-feature additive contributions. Permutation importance, pruning, and normalization do not provide per-instance local explanations.

Answer

Feature Importance Permutation

Answer

Model pruning

Answer

Data normalization

Question 4

An auditor is evaluating an AI system for 'Model Inversion' risk. What is this?

Accepted Answer

Reconstructing training data from outputs. Model Inversion is an attack where an adversary reconstructs training data from model outputs.

Answer

Reducing inference latency

Answer

Increasing training bias

Answer

Overfitting the data

Question 5

As part of an AI audit, you are checking the 'AI Transparency' requirement. Which documentation artifact is essential to provide to external regulators to explain the model's design, intended use, and limitations?

Accepted Answer

Model Card. A Model Card is the documentation artifact specifically designed to disclose a model's design, intended use, limitations, performance characteristics, and ethical considerations to external stakeholders. It provides regulators with a standardized, transparent summary of the model's purpose and constraints. This directly satisfies AI transparency requirements in audit and regulatory contexts.

Answer

Unit testing suite

Answer

API Swagger documentation

Answer

Infrastructure-as-Code (IaC) templates

Question 6

Which of the following is a symptom of 'Overfitting' during the training phase?

Accepted Answer

High training accuracy, low validation accuracy. Overfitting happens when the model performs well on training data but poorly on unseen test data.

Answer

Low training accuracy, low validation accuracy

Answer

Model crashes during training

Answer

Model is too small

Question 7

Which THREE of the following are potential risks associated with AI model 'Drift' that an auditor must account for?

Accepted Answer

Bias against specific demographic groups that emerged post-deployment. Drift impacts operational accuracy, fairness, and compliance, making it a critical governance risk.

Answer

Increased electricity consumption of servers

Answer

Inability to access the source code of the model

Question 8

Which environment should be used to perform 'Model Validation' before moving to production?

Accepted Answer

Staging Environment. Staging or UAT environments mirror production and are used for testing.

Answer

Training Cluster

Answer

Development Environment

Answer

Public Internet

Answer

Local Desktop

Question 9

Which THREE tasks are required for a 'Model Decommissioning' workflow?

Accepted Answer

Terminate serving infrastructure. Decommissioning requires archival, notification, and resource cleanup.

Answer

Ask developer for coffee

Answer

Delete all company records

Question 10

If a model's performance drops, and the input data distribution has changed, what operational process must be triggered?

Accepted Answer

Model Retraining. Data distribution change triggers model retraining using updated data samples.

Answer

Security audit

Answer

Model Deletion

Answer

Hardware Upgrade

Answer

Code Refactoring