AIF-C01 Fundamentals of AI and ML Practice Question
A financial services company uses a machine learning model to approve loan applications. The model is a gradient boosting classifier trained on historical loan data. Recently, the company noticed that the model's approval rate for applicants from a certain demographic group is significantly lower than for other groups, even though the model's overall accuracy remains high. The data science team has been asked to address this potential bias while minimizing the impact on overall model performance. The team has access to the training data and the trained model. They have limited time and budget. Which course of action should the team take first?
⚠ Common exam trap
AIF-C01 often tests the misconception that removing the sensitive attribute eliminates bias; candidates must recognize that proxy variables and historical bias persist, and that data analysis plus mitigation techniques is the correct first step.
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
✓
Analyze the training data for bias and retrain the model using bias mitigation techniques such as reweighting.
The first step should be to analyze the training data for bias and then retrain the model using bias mitigation techniques such as reweighting, because this addresses the root cause of the disparity while aiming to preserve overall performance. Reweighting adjusts sample weights to reduce the influence of biased historical patterns, and it can be applied with limited time and budget since it works with existing data and model. This is a targeted, cost-effective first action before considering more expensive data collection or post-deployment adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the sensitive attribute from the training data and retrain the model.
Why it's wrong here
Dropping the sensitive attribute does not remove bias, because correlated proxy features still encode group membership in the gradient boosting splits. This approach is correct when the attribute is genuinely irrelevant and no proxies exist, which the observed approval gap contradicts.
- ✗
Collect more data from the under-represented demographic group and retrain the model.
Why it's wrong here
Collecting additional data is a longer-term remediation requiring budget and time the team lacks, and it does not first establish which features cause the disparity. This is correct when under-representation in training data is the confirmed root cause, not merely a suspected one.
- ✓
Analyze the training data for bias and retrain the model using bias mitigation techniques such as reweighting.
Why this is correct
Inspecting the training data for demographic imbalance or proxy features reveals the bias source before any modelling change. Reweighting or resampling then corrects the disparity at its root, which is cheaper and faster than post-hoc threshold tuning or full model replacement.
- ✗
Adjust the model's decision threshold for the affected group after deployment.
Why it's wrong here
Post-deployment threshold adjustment is a mitigation applied at inference, not a first diagnostic step; it also treats symptoms without identifying which features drive the disparity. Threshold tuning is correct when operating-point calibration is the goal, such as balancing precision and recall for a fixed classifier.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.