CCNA AI Governance and Ethics Questions — Page 2 of 2

MCQhard

An AI governance team is implementing the NIST AI Risk Management Framework. They have identified a high-risk AI system and are in the 'Measure' function. Which activity is most appropriate for this function?

A.Conduct bias and fairness impact assessments on the model

B.Implement technical controls to mitigate identified risks

C.Document the system's intended purpose and data sources

D.Establish an AI ethics board to oversee risk decisions

AnswerA

Bias and fairness impact assessments generate quantitative and qualitative evidence about model behaviour, which is exactly what the Measure function covers: analysing, benchmarking and monitoring identified risks. Mapping and framing belong to earlier functions, while this activity quantifies the high-risk system's performance.

Why this answer

In the NIST AI Risk Management Framework (AI RMF), the 'Measure' function focuses on assessing and analyzing risks associated with AI systems. For a high-risk AI system, conducting bias and fairness impact assessments is a core activity within this function, as it quantifies and evaluates potential harms related to fairness, accuracy, and transparency. This aligns with the framework's emphasis on quantitative and qualitative risk measurement before moving to risk treatment in the 'Manage' function.

Exam trap

The AI0-001 exam often tests the distinction between the NIST AI RMF functions (Map, Measure, Manage, Govern) by presenting risk mitigation actions (like implementing controls) as plausible activities for the 'Measure' function, when they actually belong to the 'Manage' function.

How to eliminate wrong answers

Option B is wrong because implementing technical controls to mitigate risks belongs to the 'Manage' function, which involves risk response and treatment, not the 'Measure' function that focuses on assessment and analysis. Option C is wrong because documenting the system's intended purpose and data sources is part of the 'Map' function, which establishes context and identifies risks, not the 'Measure' function that evaluates those risks. Option D is wrong because establishing an AI ethics board is a governance structure typically associated with the 'Govern' function, which sets policies and oversight, not the 'Measure' function's risk assessment activities.

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MCQeasy

A company uses an AI system to generate marketing images. They are concerned about copyright ownership of the generated content. According to current US copyright law, who typically owns the copyright for AI-generated work?

A.No one; the work may be in the public domain

B.The user who provided the input prompts

C.The AI system itself, as the creator

D.The company that owns the AI model

AnswerA

US copyright law requires human authorship, so purely AI-generated images lack a protectable author. The work therefore falls into the public domain, meaning no party holds copyright, which is the position the scenario's ownership concern must account for.

Why this answer

Under current US copyright law and US Copyright Office guidance, copyright protection requires human authorship. Works generated entirely by AI without sufficient human creative input are not copyrightable and effectively fall into the public domain, meaning no one owns the copyright.

Exam trap

The trap is assuming the prompt author or the AI company automatically owns the output; the key legal principle is that copyright requires human authorship, so purely AI-generated works may have no owner.

How to eliminate wrong answers

Option B is wrong because providing input prompts alone has generally not been deemed sufficient human authorship by the Copyright Office; prompts are treated as instructions, not creative expression fixed in the output. Option C is wrong because an AI system is not a legal person and cannot hold copyright. Option D is wrong because owning the AI model does not confer copyright on outputs generated by users, absent a separate contractual assignment.

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MCQmedium

A bank uses an AI system for credit scoring. To meet fairness requirements, they want to ensure the model predicts similar outcomes for individuals who are similar with respect to the target variable, regardless of protected attributes. Which fairness metric addresses this?

A.Demographic parity

B.Calibration

C.Individual fairness

D.Equalised odds

AnswerC

Individual fairness requires that similar individuals receive similar predictions, measuring outcome consistency between comparable cases irrespective of protected attributes. This matches the bank's requirement, unlike group fairness metrics such as demographic parity or equalised odds.

Why this answer

Individual fairness requires that similar individuals receive similar predictions — that is, the model treats people who are alike with respect to the target variable in the same way, regardless of protected attributes. This matches the bank's requirement to predict similar outcomes for similar individuals irrespective of protected characteristics.

Exam trap

AI0-001 often tests the distinction between group-level fairness metrics (demographic parity, equalised odds, calibration) and individual-level fairness, so candidates may pick a group metric when the question emphasises 'similar individuals'.

How to eliminate wrong answers

Option A is wrong because demographic parity requires equal positive prediction rates across groups, which is a group-level statistical criterion, not a similarity-based individual one. Option B is wrong because calibration concerns whether predicted probabilities match observed frequencies within groups, not whether similar individuals get similar outcomes. Option D is wrong because equalised odds requires equal true positive and false positive rates across groups, again a group-level criterion rather than an individual similarity guarantee.

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Multi-Selecthard

A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?

Select 3 answers

A.Revisit training data for historical bias and consider reweighting

B.Ignore the disparity because the model is accurate overall

C.Remove all demographic attributes from the dataset

D.Apply fairness constraints or adversarial debiasing during training

E.Consider using a different model that achieves better fairness metrics

AnswersA, D, E

Addressing data bias is a fundamental step to reduce disparate impact.

Why this answer

Revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.

Exam trap

The AI0-001 exam often tests the misconception that removing protected attributes (option C) is sufficient to eliminate bias, when in reality it can hide bias and still allow proxy discrimination, making it an incomplete and sometimes counterproductive solution.

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MCQeasy

Under the GDPR, individuals have the right to not be subject to a decision based solely on automated processing if it produces legal effects. Which of the following is a typical safeguard that organisations must provide to comply with this right?

A.The right to have all personal data deleted immediately

B.The right to receive a detailed mathematical explanation of the model

C.The right to demand a more favourable automated decision

D.The right to obtain human intervention on the part of the controller

AnswerD

Human intervention lets a person review and override an automated decision, directly satisfying the GDPR safeguard against solely automated processing producing legal effects. It restores meaningful human involvement, which the regulation requires alongside rights to contest and express a viewpoint.

Why this answer

Option D is correct because GDPR Article 22(3) explicitly provides that where solely automated decision-making (including profiling) produces legal or similarly significant effects, the data subject has the right to obtain human intervention, express their point of view, and contest the decision. Human intervention is the canonical safeguard organisations must offer.

Exam trap

AI0-001 often tests the confusion between Article 22 safeguards (human intervention, contest) and other GDPR rights (erasure, access, explanation) — candidates who pick 'right to erasure' or 'mathematical explanation' conflate distinct articles and recitals.

How to eliminate wrong answers

Option A is wrong because the right to erasure (Article 17) is a separate right and is not the safeguard tied to Article 22 — and it is not absolute (it has exceptions for legal obligations, public interest, etc.). Option B is wrong because GDPR Recital 71 refers to 'meaningful information about the logic involved', not a detailed mathematical explanation of the model — trade secrets and the impracticality of explaining deep models mean a full mathematical explanation is not required. Option C is wrong because GDPR does not grant a right to a 'more favourable' automated decision — it grants the right to contest and to human intervention, not to a preferred outcome.

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MCQmedium

An AI team notices that their hiring model consistently selects male candidates over equally qualified female candidates. Analysis shows the training data contains past hiring decisions where men were predominantly hired. Which type of bias is the root cause?

A.Algorithmic bias

B.Confirmation bias

C.Selection bias

D.Historical bias

AnswerD

Historical bias arises when training data reflects past discriminatory decisions, so the model learns and reproduces those patterns. Here the data encodes prior hiring favouring men, which the model replicates against equally qualified women. This directly satisfies the stem's constraint: bias originates in the data itself, not the algorithm or deployment.

Why this answer

Historical bias is the root cause because the training data reflects past hiring decisions that systematically favored male candidates, encoding societal or organizational prejudices into the model. The model learns these historical patterns and perpetuates them, leading to discriminatory outcomes against equally qualified female candidates. This is distinct from algorithmic bias, which would arise from the model's design or optimization process itself.

Exam trap

CompTIA AI often tests the distinction between historical bias (data-driven) and algorithmic bias (model-driven), and the trap here is that candidates may confuse the source of bias as being from the algorithm itself rather than the training data.

How to eliminate wrong answers

Option A is wrong because algorithmic bias refers to bias introduced by the algorithm's design, training process, or optimization function, not by the data itself. Option B is wrong because confirmation bias is a cognitive bias where individuals favor information that confirms their preexisting beliefs, which is not applicable to a machine learning model's training data. Option C is wrong because selection bias occurs when the data is not representative of the population due to non-random sampling, but here the data accurately reflects historical hiring decisions, which are themselves biased.

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