AAIA Practice Test 1 — 10 Questions

Question 1

To verify the provenance of a model in Google Cloud Vertex AI, which service should the auditor inspect to review the lineage graph of the artifacts?

Accepted Answer

Vertex AI ML Metadata. Vertex AI ML Metadata tracks the lineage of artifacts and executions within the pipeline.

Answer

Vertex AI Feature Store

Answer

Cloud Logging

Answer

Vertex AI Model Registry

Question 2

When conducting an audit, which THREE of the following represent potential 'Model Risk' areas that require documentation?

Accepted Answer

Susceptibility to adversarial attacks. Adversarial vulnerability, data drift, and bias are three primary risks requiring documentation in an AI audit report.

Answer

The color of the server racks

Answer

The speed of the local internet connection

Question 3

An auditor is evaluating the 'Model Monitoring' dashboard. Which TWO items should the dashboard display to alert the team of potential issues?

Accepted Answer

Real-time model accuracy metrics. Data drift and prediction accuracy are the two most critical metrics for a model monitor.

Answer

The model developer's vacation schedule

Answer

Public stock prices of the firm

Answer

Office building temperature

Question 4

When reviewing an AI system log, what is the 'inference request' ID used for?

Accepted Answer

Audit trail and traceability. The inference ID links the specific request to its model output, facilitating trace-ability during audits.

Answer

Data encryption

Answer

Hyperparameter storage

Answer

Model compression

Question 5

Which metric is commonly used to audit classification models?

Accepted Answer

F1-score. The F1-score balances precision and recall, providing a single metric for classification performance.

Answer

Logarithmic growth

Answer

Mean Squared Error

Answer

CPU utilization

Question 6

Which TWO of the following are essential when performing an 'AI Risk Assessment'?

Accepted Answer

Assessing the likelihood and severity of identified risks. An assessment must identify both technical risks and potential impacts on stakeholders.

Answer

Listing the names of the team members' families

Answer

Choosing the best coffee brand for the office

Answer

Selecting the color scheme for the model's dashboard

Question 7

Which TWO of the following are typical 'Model Risk Management' (MRM) pillars?

Accepted Answer

Independent model validation. MRM is structured around independent validation and continuous monitoring.

Answer

Designing office holiday parties

Answer

Negotiating salaries for the data scientists

Answer

Public relations management

Question 8

Which 'AI Risk Mitigation' strategy is most effective for reducing 'Data Leakage' in training sets?

Accepted Answer

Implementing strict data partitioning and feature selection protocols. Data leakage occurs when information from the target variable leaks into the training features; strict separation and testing prevent this.

Answer

Using more colorful charts in the report

Answer

Increasing the number of servers

Answer

Changing the office seating arrangement

Question 9

Which TWO of the following are legitimate 'AI Governance' concerns regarding third-party AI service providers (SaaS)?

Accepted Answer

Data residency and compliance with local regulations. Auditors must ensure that third-party AI tools comply with internal data handling standards and that the vendor's own governance is adequate.

Answer

The programming language used by the vendor

Answer

Vendor's model update and retraining frequency

Answer

The physical location of the vendor's corporate office

Question 10

When selecting testing techniques for an AI model, which THREE are considered 'Model-Agnostic'?

Accepted Answer

SHAP (SHapley Additive exPlanations). LIME, SHAP, and Partial Dependence Plots (PDPs) are model-agnostic methods that work with any ML model.

Answer

Neural network weight pruning

Answer

Linear regression coefficients