Question 157 of 835
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML engineer is using Amazon SageMaker…
An ML engineer is using Amazon SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a gradient boosting model. The tuning job is taking a long time and has completed many training jobs. The engineer wants to stop training jobs that are unlikely to improve the objective metric. What should they configure?
⚠ Common exam trap
Many exam-takers confuse early stopping (which stops individual training jobs) with reducing the search space or changing the search strategy, which only affect the overall tuning job configuration without addressing the need to terminate underperforming trials mid-execution.
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
✓
Enable early stopping in the hyperparameter tuning job
Enabling early stopping in the Amazon SageMaker Automatic Model Tuning (AMT) job allows the tuning job to automatically stop training jobs that are unlikely to improve the objective metric based on intermediate results. This reduces the total time and compute cost by terminating poorly performing trials early, which directly addresses the engineer's goal of stopping unpromising training jobs.
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 number of hyperparameter ranges
Why it's wrong here
This limits the search space but does not stop unpromising active jobs.
- ✗
Use a random search strategy instead of Bayesian
Why it's wrong here
Random search does not incorporate early stopping; it may still complete all jobs.
- ✗
Increase the maximum number of training jobs
Why it's wrong here
Increasing the max jobs would allow more training, not stop unpromising ones.
- ✓
Enable early stopping in the hyperparameter tuning job
Why this is correct
Early stopping terminates training jobs that are not meeting an improvement threshold, reducing overall tuning time.
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Last reviewed: Jul 4, 2026
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