MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Automatic Model Tuning with Hyperband. They want to stop poorly performing trials early to save resources. Which strategy does Hyperband use?
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
✓
Successive Halving
Hyperband uses early stopping by allocating resources to promising configurations and stopping poorly performing ones. Bayesian optimization uses acquisition functions. Random search does not early stop. Grid search exhaustively evaluates all combinations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Grid search
Why it's wrong here
Grid search evaluates all combinations without early stopping.
- ✗
Random search
Why it's wrong here
Random search does not implement early stopping.
- ✓
Successive Halving
Why this is correct
Hyperband uses Successive Halving to allocate more resources to promising trials.
- ✗
Bayesian optimization
Why it's wrong here
Bayesian optimization is a separate strategy.
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.