MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO services can be used to perform hyperparameter tuning in Amazon SageMaker? (Choose two.)
⚠ Common exam trap
Many candidates confuse Amazon SageMaker Experiments as merely a tracking tool rather than a service that can be used to perform and manage hyperparameter tuning, or they might incorrectly associate AWS Glue or EMR with machine learning tuning due to their data processing roles.
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
✓
Amazon SageMaker Automatic Model Tuning
Amazon SageMaker Automatic Model Tuning (option A) is the native hyperparameter tuning service in SageMaker, which automatically searches for the best hyperparameter values by launching training jobs with different combinations and evaluating them against a specified objective metric. Amazon SageMaker Experiments (option B) is used to organize, track, and compare machine learning experiments, including hyperparameter tuning runs, by capturing parameters, metrics, and artifacts for reproducibility and analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker Automatic Model Tuning
Why this is correct
This is the native hyperparameter tuning service.
- ✓
Amazon SageMaker Experiments
Why this is correct
Experiments can track and compare tuning jobs.
- ✗
AWS Glue
Why it's wrong here
Glue is an ETL service.
- ✗
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth is for data labeling.
- ✗
Amazon EMR
Why it's wrong here
EMR is for big data processing.
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