Quick Answer
The answer is Amazon SageMaker Model Registry, the AWS service purpose-built to store and version machine learning models along with their associated metadata like hyperparameters and evaluation metrics. This service provides a centralized catalog where models are registered, versioned, and managed through their lifecycle, enabling lineage tracking and approval workflows. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between general-purpose storage and ML-specific tooling—a common trap is confusing S3 (which stores model artifacts as objects but lacks versioning and metadata management) or ECR (for container images) with the Model Registry’s dedicated model governance features. Remember the key differentiator: SageMaker Model Registry is the only service that natively combines model versioning with metadata search and approval gates. A helpful memory tip is to think of it as a “Git for models” that tracks both the artifact and its training story.
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.
A data scientist needs to store and version machine learning models, along with metadata such as hyperparameters and metrics. Which AWS service is designed for this purpose?
Amazon S3 with versioning enabled
Why wrong: S3 lacks metadata management and model lineage features.
Amazon SageMaker Model Registry
It provides model versioning, metadata, and approval workflows.
Amazon DynamoDB
Why wrong: DynamoDB is a database, not a model registry.
Amazon Elastic Container Registry (ECR)
Why wrong: ECR stores Docker images, not model artifacts with metadata.
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 Model Registry
Option D is correct: SageMaker Model Registry is for cataloging and versioning models. Option A (S3) is object storage but not specialized. Option B (ECR) stores container images. Option C (DynamoDB) is a NoSQL database, not purpose-built for ML models.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon S3 with versioning enabled
Why it's wrong here
S3 lacks metadata management and model lineage features.
Amazon SageMaker Model Registry
Why this is correct
It provides model versioning, metadata, and approval workflows.
Related concept
Read the scenario before looking for a memorised answer.
Amazon DynamoDB
Why it's wrong here
DynamoDB is a database, not a model registry.
Amazon Elastic Container Registry (ECR)
Why it's wrong here
ECR stores Docker images, not model artifacts with metadata.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker Model Registry — Option D is correct: SageMaker Model Registry is for cataloging and versioning models. Option A (S3) is object storage but not specialized. Option B (ECR) stores container images. Option C (DynamoDB) is a NoSQL database, not purpose-built for ML models.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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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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