AAIA Practice Test 15 — 15 Questions

Question 1

You are auditing a model's 'Data Pre-processing' step. Which practice is a major concern regarding data privacy?

Accepted Answer

Storing training data that can be re-identified without proper access controls. Re-identification of anonymized data is a significant risk that must be mitigated in data handling.

Answer

Documenting the transformations applied to the data

Answer

Compressing the data to save storage costs

Answer

Using open-source data transformation libraries

Question 2

In the context of 'AI Compliance', what is a 'Model Card'?

Accepted Answer

A document providing transparency regarding model features, limitations, and training data. A model card provides transparency by documenting the model's intended use, limitations, and performance metrics.

Answer

A physical access card for the data center

Answer

A credit card used for paying cloud compute bills

Answer

A list of all users authorized to access the AI

Question 3

Which TWO techniques should an auditor employ to detect bias in a model where the training data has imbalanced demographic representation?

Accepted Answer

Implementation of fairness-aware loss functions. Stratified sampling and fairness-aware loss functions are common techniques to address and audit bias.

Answer

Updating the corporate logo

Answer

Changing the font size in the UI

Answer

Increasing the number of project meetings

Question 4

A board of directors requests a quarterly report on AI risk exposure. Which component of the NIST AI Risk Management Framework should you prioritize to demonstrate that the board's AI policy is being operationally enforced?

Accepted Answer

Govern. The 'Govern' function of the NIST AI RMF is the primary domain for board-level oversight and organizational policy enforcement.

Answer

Measure

Answer

Manage

Answer

Map

Question 5

What is the primary purpose of a 'Champion-Challenger' deployment pattern in MLOps?

Accepted Answer

To safely test a new model vs production. It allows safe A/B testing of a new model version against the current production model.

Answer

To manage database indexes

Answer

To compress model weights

Answer

To increase training speed

Question 6

What is the purpose of 'Model Validation' in the audit lifecycle?

Accepted Answer

To verify model performance on test data. Validation confirms that the model performs as expected against independent, unseen data.

Answer

To speed up data ingestion

Answer

To lower API costs

Answer

To increase training time

Question 7

When scoping an audit, which TWO stakeholders should the auditor interview?

Accepted Answer

Model Owners. Data Scientists and Model Owners have the necessary technical and business context for the AI system.

Answer

The lobby receptionist

Answer

The company's external marketing agency

Answer

Office cleaning staff

Question 8

What is the primary benefit of using 'Containerization' for AI model auditability?

Accepted Answer

Environment reproducibility. Containers (e.g., Docker) encapsulate code, libraries, and dependencies, ensuring that the model runs the same way in audit as it did in training.

Answer

Reduced data storage costs

Answer

Increased hardware speed

Answer

Simplified UI design

Question 9

You are reviewing the 'AI Incident Response Plan'. Which element is essential for compliance?

Accepted Answer

Defined escalation paths for ethical or performance incidents. Incident response must include specific triggers for escalation and communication regarding AI failures.

Answer

The history of the company's founding

Answer

A log of all office supply purchases

Answer

A list of all cafeteria menus in the building

Question 10

Which THREE criteria are necessary for an AI system to be deemed 'Trustworthy' under most governance frameworks?

Accepted Answer

Reliability and consistent performance over time. Trustworthiness is derived from transparency, reliability, and security.

Answer

The ability to generate high revenue for the firm

Answer

The use of the latest marketing buzzwords

Question 11

During an AI audit, what is a 'PII Scrubber' tool used for?

Accepted Answer

Anonymizing sensitive data. PII scrubbers remove or anonymize Personally Identifiable Information from training sets to ensure privacy compliance.

Answer

Cleaning up logs

Answer

Compressing images

Answer

Improving accuracy

Question 12

Which technique is best for verifying that a model has not been subject to 'training data poisoning'?

Accepted Answer

Review of data provenance. Analyzing data provenance and lineage ensures that only trusted sources were used for the training set.

Answer

Latency testing

Answer

Model Accuracy testing

Answer

Hyperparameter tuning

Question 13

Which of these is a typical 'audit finding' in an AI governance review?

Accepted Answer

Insufficient model documentation. Lack of version control or undocumented model changes are classic audit findings in AI systems.

Answer

Too many GPU cores

Answer

Cloud-based deployment

Answer

Use of Python language

Question 14

A data scientist proposes using a 'black-box' model for a high-stakes loan approval process. Which action does your AI audit framework require?

Accepted Answer

Mandating the use of XAI (Explainable AI) tools to interpret model decisions. Explainability is a prerequisite for models that have significant impact on individuals, as per most AI governance frameworks.

Answer

Requiring the data scientist to publish the code on an internal repository

Answer

Allowing the model if it achieves 99% accuracy on the test set

Answer

Asking the marketing team to verify the model's fairness

Question 15

You are managing an Azure Machine Learning workspace and need to ensure that model training jobs are reproducible across environments. Which feature should be configured to capture the exact environment state, including Python packages and system dependencies?

Accepted Answer

Environment objects. Conda or Docker environments are the standard for reproducibility in Azure ML. Environment objects define the exact dependencies required for a job.

Answer

Azure ML Datasets

Answer

Model Registries

Answer

Compute Targets