- A
Amazon SageMaker Experiments
SageMaker Experiments captures and compares training runs, metrics, and parameters.
- B
Amazon S3
Why wrong: S3 provides storage but lacks the tracking and comparison features needed.
- C
Amazon SageMaker Studio notebooks
Why wrong: Notebooks are for development, not tracking runs across experiments.
- D
Amazon SageMaker Model Registry
Model Registry stores and manages model versions, including artifacts from experiments.
- E
Amazon CloudWatch Logs
Why wrong: CloudWatch Logs is for logging and monitoring, not experiment tracking.
Question 850 of 1,000
easyMultiple SelectObjective-mapped
Tracking ML Experiments in SageMaker
This MLA-C01 practice question tests your understanding of amazon sagemaker experiments. 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. A key principle to apply: amazon SageMaker Experiments. 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.
A data science team needs to track and compare multiple ML training runs, including hyperparameters, metrics, and output artifacts. Which TWO AWS services can be used together to meet this requirement? (Choose two.)
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 Experiments
Amazon SageMaker Experiments is designed to organize, track, and compare ML training runs by capturing hyperparameters, metrics, and output artifacts. Amazon SageMaker Model Registry complements Experiments by providing a central repository to version and manage trained models, allowing teams to track model lineage and associate models with specific training runs. Together, they enable comprehensive tracking and comparison of training runs and their resulting artifacts.
Key principle: Amazon SageMaker Experiments
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 Experiments
Why this is correct
SageMaker Experiments captures and compares training runs, metrics, and parameters.
Related concept
Amazon SageMaker Experiments
- ✗
Amazon S3
Why it's wrong here
S3 provides storage but lacks the tracking and comparison features needed.
- ✗
Amazon SageMaker Studio notebooks
Why it's wrong here
Notebooks are for development, not tracking runs across experiments.
- ✓
Amazon SageMaker Model Registry
Why this is correct
Model Registry stores and manages model versions, including artifacts from experiments.
Related concept
Amazon SageMaker Experiments
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs is for logging and monitoring, not experiment tracking.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall is to think that only one service is needed, but Experiments handles run tracking while Model Registry handles model versioning and lineage. Both are required to fully meet the requirement of tracking hyperparameters, metrics, and output artifacts across multiple runs.
Detailed technical explanation
How to think about this question
Amazon SageMaker Experiments uses a trial and component hierarchy where each training run is a trial, and trials are grouped into experiments. Under the hood, it automatically captures hyperparameters from the training script and metrics from CloudWatch or SageMaker's built-in metric definitions, storing them in a searchable metadata store. This allows for programmatic comparison using the SageMaker SDK, such as listing all trials with a specific hyperparameter value or plotting learning curves across runs.
KKey Concepts to Remember
- Amazon SageMaker Experiments
- Amazon SageMaker Model Registry
- Hyperparameter tracking
- Model lineage
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Amazon SageMaker Experiments
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review amazon SageMaker Experiments, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Amazon SageMaker Experiments
What is the correct answer to this question?
The correct answer is: Amazon SageMaker Experiments — Amazon SageMaker Experiments is designed to organize, track, and compare ML training runs by capturing hyperparameters, metrics, and output artifacts. Amazon SageMaker Model Registry complements Experiments by providing a central repository to version and manage trained models, allowing teams to track model lineage and associate models with specific training runs. Together, they enable comprehensive tracking and comparison of training runs and their resulting artifacts.
What should I do if I get this MLA-C01 question wrong?
Review amazon SageMaker Experiments, then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Amazon SageMaker Experiments
About these practice questions
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Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team wants to track and compare multiple machine learning experiments, including hyperparameters, metrics, and artifacts. They are using Amazon SageMaker. Which AWS service or feature should they use to achieve this?
easy- A.AWS CloudTrail
- ✓ B.Amazon SageMaker Experiments
- C.Amazon SageMaker Model Registry
- D.Amazon SageMaker Studio
Why B: Amazon SageMaker Experiments is the correct service because it is specifically designed to track and compare machine learning experiments, including hyperparameters, metrics, and artifacts. It provides a structured way to log, organize, and analyze multiple runs, enabling teams to identify the best-performing model configurations.
Keep practising
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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