hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A financial institution uses Amazon SageMaker to…
A financial institution uses Amazon SageMaker to host a model for credit scoring. The model was trained on data that includes demographic attributes. During a routine audit, the compliance team finds that the model produces significantly different approval rates for applicants of different ethnicities, even when credit profiles are similar. The institution must continue using the model but needs to ensure compliance with fair lending laws. What should the company do FIRST?
⚠ Common exam trap
AWS often tests the principle that the first step in addressing bias is always to measure and understand it using a dedicated tool like SageMaker Clarify, rather than jumping to a corrective action like threshold adjustment or model replacement.
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
✓
Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.
Amazon SageMaker Clarify is the correct first step because it provides built-in bias detection and explainability for machine learning models. Before taking any corrective action, the company must first quantify and understand the nature and extent of the bias using SageMaker Clarify's bias metrics (e.g., Difference in Positive Proportions, Disparate Impact). This diagnostic report is essential for compliance documentation and for determining whether the bias is due to the model, the data, or the threshold, thereby guiding any subsequent remediation steps.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Adjust the decision threshold to equalize approval rates across groups.
Why it's wrong here
Adjusting thresholds without proper analysis may improve fairness metrics but could degrade overall model performance and may not address underlying bias.
- ✓
Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.
Why this is correct
SageMaker Clarify provides bias metrics and explanations, which is the first step in understanding and mitigating bias.
- ✗
Document the disparity in a compliance report and continue using the model.
Why it's wrong here
Documentation alone does not fix the non-compliance; action must be taken to mitigate bias.
- ✗
Replace the model with a simpler explainable model to eliminate bias.
Why it's wrong here
A simpler model may still exhibit bias and the institution wants to continue using the current model if possible.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.