Quick Answer
The answer is Amazon SageMaker Automatic Model Tuning and Amazon SageMaker Experiments. SageMaker Automatic Model Tuning is the native service that automates the search for optimal hyperparameters by running multiple training jobs with different parameter combinations, using strategies like Bayesian optimization or random search. SageMaker Experiments complements this by tracking, organizing, and comparing the results of those tuning jobs, making it essential for managing the iterative tuning process. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between core ML services and supporting infrastructure—common traps include confusing SageMaker Ground Truth (for data labeling) or AWS Glue (for ETL) with tuning capabilities. Remember that tuning is about optimizing model performance, not data preparation or processing. A useful memory tip: think of Automatic Model Tuning as the "engine" that runs the search, and Experiments as the "logbook" that records every trial.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 services can be used to perform hyperparameter tuning in Amazon SageMaker? (Choose two.)
Amazon SageMaker Automatic Model Tuning
This is the native hyperparameter tuning service.
Amazon SageMaker Experiments
Experiments can track and compare tuning jobs.
AWS Glue
Why wrong: Glue is an ETL service.
Amazon SageMaker Ground Truth
Why wrong: Ground Truth is for data labeling.
Amazon EMR
Why wrong: EMR is for big data processing.
Answer choices
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
Options A and B are correct. SageMaker Automatic Model Tuning is the native tuning service. SageMaker Experiments can track tuning jobs. Option C (SageMaker Ground Truth) is for labeling. Option D (AWS Glue) is for ETL. Option E (Amazon EMR) is for big data processing.
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.
Amazon SageMaker Automatic Model Tuning
Why this is correct
This is the native hyperparameter tuning service.
Related concept
Static NAT maps one inside address to one outside address.
Amazon SageMaker Experiments
Why this is correct
Experiments can track and compare tuning jobs.
Related concept
Static NAT maps one inside address to one outside address.
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.
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.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
The correct answer is: Amazon SageMaker Automatic Model Tuning — Options A and B are correct. SageMaker Automatic Model Tuning is the native tuning service. SageMaker Experiments can track tuning jobs. Option C (SageMaker Ground Truth) is for labeling. Option D (AWS Glue) is for ETL. Option E (Amazon EMR) is for big data processing.
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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