MLS-C01 Modeling Practice Question
A machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?
⚠ Common exam trap
Test-takers frequently confuse early stopping with reducing training time per job (Option B) or assume Bayesian optimization always converges faster, but in practice, early stopping directly cuts wasted time on poor trials, which is the most effective strategy when individual training jobs are very long.
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 to terminate poorly performing jobs.
Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable early stopping to terminate poorly performing jobs.
Why this is correct
Early stops poor trials early, saving compute time.
- ✗
Use a larger instance type for each training job.
Why it's wrong here
Faster training per job but does not reduce number of jobs.
- ✗
Switch to Bayesian optimization.
Why it's wrong here
Bayesian optimization may converge faster but still requires many jobs.
- ✗
Increase the number of training jobs.
Why it's wrong here
More jobs increase total time.
Go deeper
Related to this question
About these practice questions
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.