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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 occurs when a model fails to account for group differences. Using a single model could worsen the problem.
- ✓
Representation bias; mitigate by collecting more diverse training data
Why this is correct
Representation bias stems from underrepresentation of groups in training data. Collecting diverse data is the most direct mitigation.
- ✗
Measurement bias; mitigate by using more precise measurement tools
Why it's wrong here
Measurement bias arises from how features are measured, not from missing data.
- ✗
Historical bias; mitigate by removing sensitive attributes from the model
Why it's wrong here
Historical bias reflects existing societal biases, not underrepresentation. Removing sensitive attributes does not address lack of data.
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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.