AI0-001 AI Governance and Ethics Practice Test 3 — 15 Questions

Question 1

A company is implementing an AI ethics board. Which TWO responsibilities should the board typically have?

Accepted Answer

Approving or rejecting high-risk AI initiatives. An AI ethics board should oversee AI projects for ethical compliance and approve high-risk AI initiatives. Implementing technical models and auditing data privacy are operational tasks not typically under the board's direct purview.

Answer

Writing code for fairness algorithms

Answer

Developing marketing strategies for AI products

Answer

Conducting daily data privacy audits

Question 2

A company is deploying an AI system that falls under the EU AI Act's high-risk category. Which THREE requirements must the company fulfill?

Accepted Answer

Ensure human oversight to prevent or minimise risks. The EU AI Act for high-risk systems requires risk management, human oversight, and transparency documentation. Open-sourcing the model is not required; obtaining consent is not a specific requirement for high-risk systems.

Answer

Obtain explicit consent from all affected individuals

Answer

Open-source the model's code to the public

Question 3

A data scientist is using LIME to explain a black-box model. Which TWO characteristics of LIME are true?

Accepted Answer

It creates an interpretable surrogate model locally around a prediction. LIME (Local Interpretable Model-agnostic Explanations) creates local surrogate models to explain individual predictions. It does not provide global feature importance or an inherent measure of model confidence.

Answer

It provides a measure of model confidence in its prediction

Answer

It requires access to the model's internal parameters

Answer

It provides a global ranking of feature importance across the entire dataset

Question 4

A healthcare AI system diagnosing diabetic retinopathy from retinal images shows high accuracy overall but significantly lower recall for patients with darker skin tones. Which fairness metric would BEST capture this disparity by comparing true positive rates across groups?

Accepted Answer

Equalised odds. Equalised odds requires that the true positive rate and false positive rate be equal across groups. Demographic parity only checks outcome rates, not error types. Individual fairness compares similar individuals. Calibration checks confidence alignment.

Answer

Calibration

Answer

Demographic parity

Answer

Individual fairness

Question 5

A data scientist needs to explain a single prediction from a complex ensemble model to a business stakeholder. Which technique generates local, interpretable explanations by perturbing input features and fitting a simple surrogate model?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by perturbing inputs and learning a linear surrogate. SHAP provides Shapley values, which are also local but game-theoretic. Attention visualisation is for transformer models. Model cards describe global model behaviour.

Answer

Model card

Answer

SHAP

Answer

Attention visualisation

Question 6

Under the EU AI Act, an AI system that uses subliminal techniques to materially distort a person's behaviour, causing psychological or physical harm, would be classified under which risk tier?

Accepted Answer

Unacceptable risk. The EU AI Act categorises such systems as 'unacceptable risk' and prohibits them outright. High risk includes critical infrastructure, education, employment, etc. Limited risk involves transparency obligations. Minimal risk covers all other systems.

Answer

Limited risk

Answer

High risk

Answer

Minimal risk

Question 7

A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?

Accepted Answer

Federated learning. Federated learning trains models across decentralised data without exchanging raw data. Differential privacy adds noise. Anonymisation removes identifiers. Pseudonymisation replaces identifiers with pseudonyms.

Answer

Pseudonymisation

Answer

Differential privacy

Answer

Anonymisation

Question 8

A model's predicted probabilities are well-calibrated overall but the model systematically assigns higher probabilities to one demographic group even when the actual outcome likelihood is the same. Which fairness issue is present?

Accepted Answer

Calibration disparity. Calibration fairness requires that for each predicted probability, the actual outcome rate is the same across groups. The scenario describes a calibration disparity. Demographic parity would involve different selection rates. Equalised odds involves equal error rates. Individual fairness is about treating similar individuals similarly.

Answer

Demographic parity violation

Answer

Equalised odds violation

Answer

Individual fairness violation

Question 9

A company using an AI-based hiring tool receives a candidate request for explanation of an automated rejection. Which GDPR principle is most directly relevant?

Accepted Answer

Right to explanation. The GDPR includes a right to explanation for automated individual decision-making, including profiling. The right to access is broader. The right to erasure is about deletion. The right to data portability is about data transfer.

Answer

Right to erasure

Answer

Right to data portability

Answer

Right to access

Question 10

Which NIST AI RMF function involves identifying the context, risks, and potential impacts of an AI system, including mapping the AI lifecycle and stakeholders?

Accepted Answer

Map. The AI RMF's four functions are: Govern, Map, Measure, Manage. Map focuses on context and risk identification. Govern sets policies. Measure evaluates metrics. Manage addresses risks through controls.

Answer

Manage

Answer

Measure

Answer

Govern

Question 11

A company is required to disclose that content has been generated or significantly modified by AI. Which practice directly addresses this transparency obligation?

Accepted Answer

Applying AI watermarking. AI watermarking directly addresses the transparency obligation by embedding a detectable signal into AI-generated content, enabling clear disclosure that the content was produced or significantly modified by AI. This practice aligns with regulatory requirements for provenance and traceability, as watermarks can be verified by automated systems or human inspection to confirm AI origin.

Answer

Using LIME for explanations

Answer

Implementing federated learning

Answer

Publishing a model card

Question 12

A data scientist is training a large language model and wants to reduce the carbon footprint. Which practice is MOST effective for reducing energy consumption during training?

Accepted Answer

Apply model pruning and knowledge distillation. Model pruning reduces the number of parameters in the model, and knowledge distillation trains a smaller student model to mimic a larger teacher model. Both techniques directly reduce the computational operations (FLOPs) required during training and inference, leading to significant energy savings. In contrast, using FP32 or increasing epochs increases energy consumption, and adding more GPUs increases total power draw even if wall-clock time decreases.

Answer

Use FP32 precision instead of mixed precision

Answer

Use more GPUs in parallel to finish training faster

Answer

Increase the number of training epochs for better accuracy

Question 13

An organisation is deploying an AI system for credit scoring, which is considered high-risk under the EU AI Act. Which requirement is NOT typically mandated for high-risk systems?

Accepted Answer

Publish the complete source code of the AI system. The EU AI Act requires risk management, data governance, transparency, human oversight, accuracy, and robustness for high-risk AI. However, it does not require publishing the full source code; only documentation and model cards may be required.

Answer

Ensure training data is relevant and representative

Answer

Establish a risk management system

Answer

Provide human oversight mechanisms

Question 14

Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?

Accepted Answer

Differential privacy. Differential privacy adds calibrated noise to ensure the output does not reveal individual participation. Anonymisation removes identifiers. Pseudonymisation replaces identifiers. Federated learning decentralises data but does not necessarily add noise.

Answer

Anonymisation

Answer

Federated learning

Answer

Pseudonymisation

Question 15

A company is forming an AI ethics board to oversee the development of a high-stakes AI system for bail decision recommendations. Which THREE responsibilities should the board primarily undertake?

Accepted Answer

Review model outputs for disparate impact across demographic groups. An AI ethics board should define fairness criteria, review models for bias, and establish a human oversight process. Designing the algorithm is a technical task for engineers. Marketing the system is a business function, not an ethics board duty.

Answer

Market the AI system to potential clients

Answer

Write the production code for the AI model