MLA-C01 ML Model Development Practice Question
A company uses SageMaker Clarify to detect bias during training. They want to ensure that the trained model does not rely on a sensitive attribute like gender. Which Clarify feature should they configure?
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
✓
Clarify bias config with post-training bias metrics
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Clarify bias config with post-training bias metrics
Why this is correct
Post-training bias metrics can be configured to check for bias in model predictions.
- ✗
Clarify with SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift, not training bias.
- ✗
SHAP analysis
Why it's wrong here
SHAP explains predictions but does not enforce fairness constraints.
- ✗
Clarify processing job with pre-training bias metrics
Why it's wrong here
Pre-training metrics analyze data, not the model.
- ✗
Bias report generation
Why it's wrong here
Reports detect bias but do not prevent it during training.
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JA
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.