hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is using SageMaker to…
A machine learning engineer is using SageMaker to train a model with the built-in LightGBM algorithm. The engineer wants to use early stopping to prevent overfitting. The training job is configured with a validation dataset. Which hyperparameter should be set to enable early stopping?
⚠ Common exam trap
Many candidates confuse the generic concept of early stopping with the exact hyperparameter name used by SageMaker's built-in LightGBM, often selecting `early_stopping` (which is not a valid parameter) instead of the precise `early_stopping_rounds`.
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
✓
early_stopping_rounds
In SageMaker's built-in LightGBM algorithm, the hyperparameter `early_stopping_rounds` controls early stopping. When a validation dataset is provided, training will stop if the evaluation metric does not improve for the specified number of consecutive rounds, preventing overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
early_stopping_rounds
Why this is correct
early_stopping_rounds triggers early stopping after a specified number of rounds without validation improvement.
- ✗
num_iterations
Why it's wrong here
num_iterations sets the maximum number of iterations, not early stopping.
- ✗
early_stopping
Why it's wrong here
early_stopping is not a valid hyperparameter for LightGBM in SageMaker.
- ✗
num_boost_round
Why it's wrong here
num_boost_round sets the number of boosting rounds, not early stopping.
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