MLS-C01 Practice Question: Machine Learning Implementation and Operations
A model deployed on a SageMaker endpoint is producing predictions that are consistently biased against a certain demographic. Which step should the team take FIRST to address this issue?
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
✓
Use SageMaker Clarify to analyze bias in the training data and predictions
The first step is to analyze the data and model for bias. SageMaker Clarify can detect bias in training data and predictions, making option C the correct first step. Option A (SageMaker Model Monitor) tracks prediction quality but does not specifically analyze bias. Option B (switching algorithm) is a reactive change without understanding the bias source. Option D (retraining with balanced data) is a potential fix but should come after bias analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable SageMaker Model Monitor to track prediction quality
Why it's wrong here
Model Monitor tracks drift, not bias directly.
- ✗
Switch to a different algorithm that is less prone to bias
Why it's wrong here
Bias often stems from data, not algorithm; switching may not help.
- ✓
Use SageMaker Clarify to analyze bias in the training data and predictions
Why this is correct
Clarify can detect and explain bias, guiding corrective actions.
- ✗
Retrain the model with balanced data
Why it's wrong here
Retraining without understanding the bias source may not solve it.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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