MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Autopilot to automatically build a binary classification model. The dataset is imbalanced. Which action will Autopilot take by default to address class imbalance?
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 class balancing weights
Autopilot automatically applies techniques to handle imbalanced data, such as class balancing weights, when it detects imbalance. It does not require manual configuration. Ensemble selection is part of Autopilot but not specifically for imbalance. SMOTE and undersampling are not built-in defaults.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform random undersampling of the majority class
Why it's wrong here
Undersampling is not a default Autopilot behavior.
- ✗
Ignore the imbalance and proceed with raw data
Why it's wrong here
Autopilot does not ignore imbalance; it handles it.
- ✗
Apply SMOTE oversampling
Why it's wrong here
SMOTE is not automatically applied by Autopilot.
- ✓
Use class balancing weights
Why this is correct
Autopilot automatically uses class weights when imbalance is detected.
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