MLA-C01 ML Model Development Practice Question
Which SageMaker feature allows you to automatically tune hyperparameters using Bayesian optimization?
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
✓
SageMaker Automatic Model Tuning
SageMaker Automatic Model Tuning (AMT) supports Bayesian optimization, random search, and Hyperband. Debugger is for monitoring. Experiments is for tracking. Autopilot is for AutoML.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates the entire ML pipeline, not just hyperparameter tuning.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments is for tracking and organizing runs.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger is for debugging training jobs.
- ✓
SageMaker Automatic Model Tuning
Why this is correct
AMT performs hyperparameter optimization.
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.