Question 1,525 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist needs to perform hyperparameter optimization for a model. Which AWS service provides built-in hyperparameter tuning jobs?
⚠ Common exam trap
Watch out — candidates often confuse AWS Batch or Step Functions as capable of hyperparameter tuning because they can orchestrate multiple jobs, but they lack the built-in optimization algorithms and managed tuning lifecycle that SageMaker provides.
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
Amazon SageMaker provides built-in hyperparameter tuning jobs as a managed service, allowing data scientists to automatically search for optimal hyperparameter values using strategies like Bayesian optimization, random search, or Hyperband. This is a core feature of SageMaker's automatic model tuning capability, which integrates directly with SageMaker training jobs and supports early stopping to reduce compute costs.
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 EMR
Why it's wrong here
EMR does not have hyperparameter tuning.
- ✗
AWS Step Functions
Why it's wrong here
Step Functions is for orchestration, not tuning.
- ✓
Amazon SageMaker
Why this is correct
SageMaker has automatic model tuning.
- ✗
AWS Batch
Why it's wrong here
No built-in hyperparameter tuning.
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Last reviewed: Jul 4, 2026
This MLS-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 MLS-C01 exam.
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