MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Automatic Model Tuning to find the best hyperparameters for a model. They want to reduce the total tuning time for a given number of training jobs. Which tuning strategy should they choose?
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
✓
Hyperband
Hyperband is an early stopping strategy that allocates resources to promising configurations and stops poor performers early, reducing total tuning time compared to random search or Bayesian optimization without early stopping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hyperband
Why this is correct
Hyperband uses early stopping to prune bad trials, reducing total tuning time for the same number of jobs.
- ✗
Grid search
Why it's wrong here
Grid search is exhaustive and slow, not efficient for large search spaces.
- ✗
Random search
Why it's wrong here
Random search does not use early stopping; it can be slower for the same number of jobs.
- ✗
Bayesian optimization
Why it's wrong here
Bayesian optimization is sample-efficient but typically doesn't include aggressive early stopping like Hyperband.
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-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 MLA-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 MLA-C01 exam.