Question 568 of 1,672
MLS-C01 Modeling Practice Question
A data scientist wants to automate the selection of optimal hyperparameters for a model. Which SageMaker feature should be used?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Experiments (which tracks and compares runs) with Automatic Model Tuning (which actively searches for optimal hyperparameters), leading them to pick D instead of C.
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) is the correct feature because it automates hyperparameter optimization by running multiple training jobs with different hyperparameter combinations, using algorithms like Bayesian optimization or random search to find the best set. This directly addresses the requirement to automate selection of optimal hyperparameters.
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 Debugger
Why it's wrong here
Debugger monitors training.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift.
- ✓
SageMaker Automatic Model Tuning
Why this is correct
Automatic Model Tuning optimizes hyperparameters.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments track runs, not tune hyperparameters.
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Last reviewed: Jun 24, 2026
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