Question 471 of 1,672
MLS-C01 Modeling Practice Question
A team is using SageMaker to train a model with hyperparameter tuning. The training jobs are taking too long. The team wants to reduce time without sacrificing model quality. Which approach should they take?
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 terminates poorly performing training jobs early, saving time without sacrificing model quality. Option A is incorrect: random search may be faster but does not guarantee quality as it is less efficient than Bayesian optimization. Option C is incorrect: increasing the maximum number of training jobs would increase time. Option D is incorrect: reducing the maximum runtime per training job may prevent convergence, harming model quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use random search instead of Bayesian optimization.
Why it's wrong here
Random search selects hyperparameter combinations uniformly at random, which lacks the sequential model-based exploration that Bayesian optimisation uses to focus on promising regions of the search space, so it would likely require more trials to converge to a comparable model quality, thus failing to reduce training time. This option is tempting because random search is computationally cheaper per trial and can be effective when the hyperparameter space is low-dimensional or when parallelising many independent trials, but here the goal is to minimise total wall-clock time while preserving quality, which Bayesian optimisation’s informed sampling directly addresses.
- ✓
Enable early stopping in the hyperparameter tuning job.
Why this is correct
Early stops poorly performing training jobs, saving time.
- ✗
Increase the maximum number of training jobs.
Why it's wrong here
More jobs increase total time.
- ✗
Reduce the maximum runtime per training job.
Why it's wrong here
This may prevent convergence for good hyperparameter sets.
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Last reviewed: Jun 20, 2026
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