MLA-C01 ML Model Development Practice Question
A company uses SageMaker Clarify to detect bias in their training data. They find that the model has a high disparate impact for a protected attribute. What should they do to mitigate this bias during training?
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
✓
Preprocess the data using techniques like reweighing or resampling to reduce bias
SageMaker Clarify can generate bias reports, but mitigation techniques like reweighing or using bias-aware algorithms are applied separately. Adjusting the threshold does not address training bias. Removing the attribute may not eliminate indirect bias. Using a different algorithm may help but is not the direct mitigation step from Clarify.
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 SageMaker Clarify’s built-in bias mitigation algorithm during training
Why it's wrong here
Clarify is for detection and reporting, not mitigation.
- ✗
Remove the protected attribute from the dataset
Why it's wrong here
Removing the attribute may not remove proxy features; also, this is not a mitigation guarantee.
- ✗
Increase the model complexity to capture more patterns
Why it's wrong here
Increasing complexity may exacerbate bias.
- ✓
Preprocess the data using techniques like reweighing or resampling to reduce bias
Why this is correct
Bias mitigation often involves preprocessing steps such as reweighing or resampling.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.