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AI Associate Data for AI Practice Question

A data scientist discovers that an AI model used for loan approval predicts high default risk disproportionately for a specific demographic group. What is the first step to address this issue?

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

Audit the training data for bias

Auditing the training data for bias helps identify if the model learned biased patterns. Option A is wrong because retraining with more data may not solve the bias if the new data also contains bias. Option C is wrong because removing demographic features may not eliminate bias if other correlated features exist. Option D is wrong because changing the algorithm does not address biased data.

Answer analysis

Option-by-option breakdown

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

  • Use a different algorithm

    Why it's wrong here

    Algorithm change does not fix biased data.

  • Audit the training data for bias

    Why this is correct

    Auditing helps identify and mitigate bias in data.

  • Remove demographic features from the model

    Why it's wrong here

    Correlated features may still cause bias.

  • Retrain the model with more data

    Why it's wrong here

    More data may still contain bias.

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JA

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.