hardMultiple Choice
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
Setting early_stopping_rounds halts training once the validation metric fails to improve for that many consecutive boosting rounds, retaining the best iteration. This directly satisfies the requirement to prevent overfitting using the configured validation dataset in the built-in LightGBM algorithm.
- ✗
num_iterations
Why it's wrong here
num_iterations sets the total boosting rounds; raising it does not itself trigger early stopping, and without the stopping parameter the job simply runs all rounds. It is tempting because it controls training length, and would be correct for capping model complexity when no validation-based stopping is wanted.
- ✗
early_stopping
Why it's wrong here
early_stopping is not a SageMaker LightGBM hyperparameter, so specifying it has no effect on the training job. It is tempting because the name matches the goal exactly, and would be correct in frameworks such as XGBoost or Keras where that parameter genuinely enables stopping.
- ✗
num_boost_round
Why it's wrong here
num_boost_round is the underlying LightGBM round count, not the SageMaker switch that activates early stopping; setting it alone leaves stopping disabled. It is tempting because it bounds boosting iterations, and would be correct for fixing a maximum round count in native LightGBM training.
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