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MLA-C01 Practice Question: A data scientist is training a binary…
A data scientist is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The model is evaluated on a held-out test set, and the F1 score is 0.12. The data scientist wants to improve the F1 score. Which two actions should the data scientist take? (Choose two.)
⚠ Common exam trap
Many candidates confuse threshold tuning with addressing imbalance directly, not realizing that adjusting the threshold without rebalancing the data or weighting classes typically fails to improve F1 score because it does not change the underlying model's learned distribution.
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
✓
Apply SMOTE (Synthetic Minority Oversampling Technique) to the training data using a preprocessing script in SageMaker Processing.
SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the class imbalance by creating a more balanced training set. This increases the model's exposure to positive examples, improving recall and precision, and thus the F1 score. Using SageMaker Processing allows this preprocessing step to be integrated into the ML pipeline efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the model complexity by decreasing the number of layers in a deep neural network.
Why it's wrong here
Incorrect: Reducing complexity may not address class imbalance and could hurt performance.
- ✓
Apply SMOTE (Synthetic Minority Oversampling Technique) to the training data using a preprocessing script in SageMaker Processing.
Why this is correct
Correct: SMOTE generates synthetic samples of the minority class, balancing the dataset and improving F1.
- ✗
Increase the decision threshold to reduce false positives.
Why it's wrong here
Incorrect: Increasing the threshold typically reduces false positives but also reduces recall, worsening F1.
- ✗
Use recall as the primary evaluation metric instead of F1.
Why it's wrong here
Incorrect: Changing the metric does not improve the model's underlying imbalance issue.
- ✓
Set the `scale_pos_weight` parameter in the SageMaker XGBoost estimator to the ratio of negative to positive samples.
Why this is correct
Correct: This adjusts the loss function to emphasize the minority class, improving recall and F1.
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