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Ethical Considerations of AImediumMultiple SelectObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

Which THREE factors should an AI Associate consider when evaluating a model for potential bias?

⚠ Common exam trap

Salesforce often tests the misconception that model complexity or deployment recency are relevant to bias detection, when in fact bias is rooted in data representation and feature correlations with protected attributes.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Whether features are correlated with protected attributes.

If a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The complexity of the model architecture.

    Why it's wrong here

    Complexity does not indicate bias.

  • Whether features are correlated with protected attributes.

    Why this is correct

    Correlation can lead to proxy discrimination.

  • Disparities in model performance metrics across groups.

    Why this is correct

    Performance disparities signal bias.

  • The date the model was last deployed.

    Why it's wrong here

    Deployment date is not a bias factor.

  • Whether the training data is representative of all groups.

    Why this is correct

    Non-representative data can cause bias.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.