MLS-C01 Modeling Practice Question
A data scientist is training an LSTM model for time series forecasting using Amazon SageMaker. The model is overfitting. Which action is LEAST likely to reduce overfitting?
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
✓
Increase the number of LSTM layers
Increasing the number of LSTM layers (Option B) is least likely to reduce overfitting because it increases model complexity, which can actually worsen overfitting. In contrast, adding dropout layers (Option A), reducing the number of hidden units (Option C), and using early stopping (Option D) are all techniques that help reduce overfitting by imposing regularization or limiting training time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add dropout layers
Why it's wrong here
Dropout regularizes.
- ✓
Increase the number of LSTM layers
Why this is correct
Increases complexity, likely overfits more.
- ✗
Reduce the number of hidden units
Why it's wrong here
Reduces model complexity.
- ✗
Use early stopping
Why it's wrong here
Prevents overfitting.
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