MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using SageMaker Autopilot to automatically build a classification model. The dataset is highly imbalanced (1% positive class). Which configuration should the scientist set to handle the class imbalance?
⚠ Common exam trap
Test-takers frequently assume SageMaker Autopilot supports common imbalance-handling techniques like class weighting or sample weights, but in reality, the only built-in way to influence Autopilot's handling of imbalance is via the objective metric, specifically F1 or other recall-focused metrics.
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 the 'AutoMLJobObjective' with 'F1' metric.
SageMaker Autopilot does not support direct class weighting or sample weights for imbalanced datasets. By setting the objective metric to 'F1', Autopilot will optimize the model for the harmonic mean of precision and recall, which is more robust to class imbalance than accuracy. This encourages the model to pay attention to the minority (positive) class during training and hyperparameter tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the problem_type to 'BinaryClassification' and enable 'balance_class_weights'.
Why it's wrong here
Autopilot does not have a 'balance_class_weights' parameter.
- ✓
Use the 'AutoMLJobObjective' with 'F1' metric.
Why this is correct
Optimizing for F1 helps address class imbalance by balancing precision and recall.
- ✗
Set the 'sample_weight' attribute in the input data.
Why it's wrong here
Autopilot does not support sample weights.
- ✗
Manually downsample the majority class before training.
Why it's wrong here
Autopilot expects the dataset as is; manual preprocessing is not a configuration option within Autopilot.
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