MLA-C01 ML Model Development Practice Question
An ML team is using SageMaker Automatic Model Tuning to optimize hyperparameters for a neural network. They want to prioritize exploration of the hyperparameter space early in the tuning process. Which 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
✓
Bayesian optimization
Bayesian optimization balances exploration and exploitation, but early in the process it tends to explore more. Random search explores uniformly without adaptation. Hyperband focuses on early stopping. Grid search is exhaustive. Bayesian optimization is the best choice for systematic exploration.
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 is exhaustive and does not prioritize exploration.
- ✓
Bayesian optimization
Why this is correct
Bayesian optimization uses a probabilistic model to guide search, balancing exploration and exploitation.
- ✗
Random search
Why it's wrong here
Random search explores uniformly but does not learn from previous trials.
- ✗
Hyperband
Why it's wrong here
Hyperband focuses on early stopping, not exploration.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. 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.