MLS-C01 Modeling Practice Question
Which TWO SageMaker features can be used to perform hyperparameter optimization? (Choose 2)
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
The correct answers are D (SageMaker automatic model tuning) and E (SageMaker Experiments). SageMaker automatic model tuning is the built-in feature that performs hyperparameter optimization by running multiple training jobs with different hyperparameter combinations. SageMaker Experiments can be used to track, organize, and analyze hyperparameter tuning jobs, including running multiple trials with different parameters, effectively performing HPO through manual or automated trial management. SageMaker Debugger (A) monitors training metrics and conditions but does not perform HPO. SageMaker Pipelines (B) orchestrates workflows but is not a direct tuning feature. SageMaker Model Monitor (C) detects data drift in deployed models and is unrelated to HPO.
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, not tunes hyperparameters.
- ✗
SageMaker Pipelines
Why it's wrong here
Pipelines can orchestrate but not directly perform HPO.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift.
- ✓
SageMaker automatic model tuning
Why this is correct
This is the built-in hyperparameter tuning service.
- ✓
SageMaker Experiments
Why this is correct
Experiments can track and compare multiple tuning runs.
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