hardMultiple Choice
AIF-C01 Practice Question: Building a resume screening model and discovers…
A company is building a resume screening model and discovers that the training data contains only resumes from one gender, leading to biased predictions. Which type of bias does this represent, and what is the most effective mitigation strategy?
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
✓
Representation bias; mitigate by collecting more diverse training data
Representation bias occurs when certain groups are underrepresented in the training data. Mitigation includes collecting more diverse data or using techniques like re-weighting or synthetic data generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Aggregation bias; mitigate by using a single model for all groups
Why it's wrong here
Aggregation bias arises when one model is fitted across heterogeneous subgroups, not when a dataset contains a single gender. Using a single model for all groups is tempting as a fairness approach, but the stem describes sampling skew, which requires rebalancing or augmenting the training data instead.
- ✓
Representation bias; mitigate by collecting more diverse training data
Why this is correct
Training data drawn from a single gender under-represents the population the model scores, so the model learns skewed patterns. Collecting diverse resumes that reflect the real applicant distribution corrects the sampling gap at source, satisfying the need to remove the bias rather than mask it post hoc.
- ✗
Measurement bias; mitigate by using more precise measurement tools
Why it's wrong here
Measurement bias concerns distorted or inaccurate feature values, whereas the stem describes a training set containing only one gender. More precise measurement tools are tempting because they address noisy labels, but they cannot introduce the missing gender group; resampling or augmenting the dataset is required.
- ✗
Historical bias; mitigate by removing sensitive attributes from the model
Why it's wrong here
Removing sensitive attributes does not correct a training set drawn from one gender; the label correlations remain skewed, so predictions stay biased. Historical bias is tempting because it names skewed sampling, yet the effective mitigation is rebalancing or augmenting the dataset, since proxy variables still encode gender.
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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.