AAIA Practice Test 2 — 10 Questions

Question 1

What is the key difference between 'AI Ethics' and 'AI Compliance'?

Accepted Answer

Ethics focuses on societal values, while compliance focuses on legal and regulatory requirements. Ethics concerns what *should* be done based on values, while compliance concerns what *must* be done based on rules.

Answer

There is no difference between the two

Answer

Ethics is only for AI, while compliance is for all departments

Answer

Compliance is only for technical roles, while ethics is for executives

Question 2

You are auditing a model's 'Robustness' against adversarial attacks. Which test is most appropriate?

Accepted Answer

Performing adversarial perturbation testing. Adversarial testing involves introducing perturbations to inputs to see if the model's output changes unexpectedly.

Answer

Checking if the server is plugged in

Answer

Checking the model's training accuracy

Answer

Asking the team to describe their favorite movies

Question 3

You are assessing the risk of 'Data Poisoning' in a retail AI model. Which control should you implement during the data ingestion pipeline to mitigate this risk?

Accepted Answer

Input data validation and sanitization. Data validation and sanitization during the ingestion pipeline ensure that malicious or corrupted data samples are blocked before entering the training set.

Answer

Model version control

Answer

Increasing the GPU compute capacity

Answer

Enabling Multi-Factor Authentication (MFA) on the model registry

Question 4

Which THREE criteria are essential when selecting an AI deployment strategy?

Accepted Answer

Service Level Agreements (SLAs). Strategy must balance risk, speed, and resource availability.

Answer

Color of the UI

Answer

Number of coffee machines

Question 5

An auditor finds that a model's 'input feature importance' has changed significantly after a retrain. What is the most appropriate action?

Accepted Answer

Investigate source data and feature logic. Investigate if the change is due to a shift in data or a feature engineering error.

Answer

Ignore the change

Answer

Delete the model

Answer

Upgrade the hardware

Question 6

Which operational process is required to ensure 'Data Privacy' when using user-generated data for model retraining?

Accepted Answer

Data anonymization. Anonymization or pseudonymization must be performed before training.

Answer

Increasing compute power

Answer

Model quantization

Answer

Removing the model

Question 7

When auditing a model deployment pipeline, which TWO aspects are critical to verify to ensure compliance with AI governance frameworks?

Accepted Answer

Validation of data processing against privacy policies. Governance requires both reproducibility and data privacy/security checks.

Answer

Whether the team uses a specific cloud provider

Answer

The speed of the training job

Answer

Usage of proprietary algorithms only

Question 8

When auditing model deployment, what is the primary purpose of 'Shadow Mode' testing?

Accepted Answer

To compare model performance against production traffic without impacting users. Shadow mode allows running the new model in production alongside the old one to compare outputs without affecting users.

Answer

To encrypt model weights in memory

Answer

To hide model artifacts from unauthorized users

Answer

To automate hyperparameter optimization

Question 9

Which of the following is a common 'drift' symptom an auditor should look for in production models?

Accepted Answer

Decreasing model accuracy. When data distribution changes (Concept Drift), the model's accuracy typically degrades over time.

Answer

Increasing server costs

Answer

Increased team size

Answer

Slow Git repository commits

Question 10

What is a 'Model Repository' in an AI audit context?

Accepted Answer

A versioned registry of models. A central, versioned store (like MLflow or Azure Model Registry) for managing trained models.

Answer

A backup hard drive

Answer

A list of developers

Answer

A documentation folder