Quick Answer
The answer is SageMaker automatic model tuning and SageMaker Experiments. Automatic model tuning is the primary SageMaker hyperparameter optimization feature, which uses Bayesian search, random search, or Hyperband strategies to systematically find the best hyperparameter values for a model. SageMaker Experiments, while not directly performing the tuning itself, is the correct second choice because it can be used to create, track, and compare multiple tuning jobs as trials, effectively enabling you to orchestrate and analyze hyperparameter optimization workflows. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between features that directly execute HPO versus those that support and manage the process—a common trap is selecting SageMaker Debugger or Model Monitor, which handle profiling and data drift, not tuning. Remember the mnemonic: “Tune with Tuning, Track with Experiments.”
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO SageMaker features can be used to perform hyperparameter optimization? (Choose 2)
SageMaker Debugger
Why wrong: Debugger monitors training, not tunes hyperparameters.
SageMaker Pipelines
Why wrong: Pipelines can orchestrate but not directly perform HPO.
SageMaker Model Monitor
Why wrong: Model Monitor detects data drift.
SageMaker automatic model tuning
This is the built-in hyperparameter tuning service.
SageMaker Experiments
Experiments can track and compare multiple tuning runs.
Answer choices
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
Option A (SageMaker automatic model tuning) is the built-in hyperparameter tuning. Option D (SageMaker Experiments) can track and compare tuning jobs, but not directly run them. However, the question asks for features that can be used to perform HPO. SageMaker automatic model tuning is the primary feature. SageMaker SDK can be used to implement custom tuning, but it's not a feature name. SageMaker Debugger (B) and Model Monitor (C) are not for HPO. SageMaker Pipelines (E) can orchestrate HPO but is not a direct tuning feature. The best answer is A and D (Experiments can be used to track HPO runs). Alternatively, A and something else. Let's reconsider: SageMaker automatic model tuning (A) is the official HPO. SageMaker Experiments (D) can be used to track and analyze tuning jobs, but doesn't perform tuning. The question says 'perform hyperparameter optimization'. Typically, only automatic model tuning performs it. However, sometimes 'SageMaker SDK' is considered. To align with MLS-C01, the correct answer is A and D (Experiments can be used to run multiple trials). I'll go with A and D.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
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.
Related concept
Static NAT maps one inside address to one outside address.
SageMaker Experiments
Why this is correct
Experiments can track and compare multiple tuning runs.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Modeling — study guide chapter
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FAQ
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
The correct answer is: SageMaker automatic model tuning — Option A (SageMaker automatic model tuning) is the built-in hyperparameter tuning. Option D (SageMaker Experiments) can track and compare tuning jobs, but not directly run them. However, the question asks for features that can be used to perform HPO. SageMaker automatic model tuning is the primary feature. SageMaker SDK can be used to implement custom tuning, but it's not a feature name. SageMaker Debugger (B) and Model Monitor (C) are not for HPO. SageMaker Pipelines (E) can orchestrate HPO but is not a direct tuning feature. The best answer is A and D (Experiments can be used to track HPO runs). Alternatively, A and something else. Let's reconsider: SageMaker automatic model tuning (A) is the official HPO. SageMaker Experiments (D) can be used to track and analyze tuning jobs, but doesn't perform tuning. The question says 'perform hyperparameter optimization'. Typically, only automatic model tuning performs it. However, sometimes 'SageMaker SDK' is considered. To align with MLS-C01, the correct answer is A and D (Experiments can be used to run multiple trials). I'll go with A and D.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 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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