Question 1,246 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
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?
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 Model Registry
Amazon SageMaker Model Registry is a purpose-built service for cataloging, versioning, and managing machine learning models along with their metadata such as hyperparameters and metrics. It provides a central repository to track model versions, lineage, and approval status. Option A (S3 with versioning) is object storage that can store model artifacts but lacks metadata management and versioning capabilities for ML models. Option C (DynamoDB) is a NoSQL database, not designed for ML model management. Option D (ECR) is for storing container images, not ML models directly.
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 S3 with versioning enabled
Why it's wrong here
Amazon S3 with versioning can store model artifacts but does not provide built-in metadata management, search, or versioning for ML models and their associated hyperparameters and metrics.
- ✓
Amazon SageMaker Model Registry
Why this is correct
Amazon SageMaker Model Registry is specifically designed to catalog, version, and manage ML models with metadata such as hyperparameters and metrics.
- ✗
Amazon DynamoDB
Why it's wrong here
Amazon DynamoDB is a NoSQL database, not a model registry. It can store metadata but is not designed for model versioning and management.
- ✗
Amazon Elastic Container Registry (ECR)
Why it's wrong here
Amazon Elastic Container Registry (ECR) stores container images, not ML models. While models can be packaged in containers, ECR is not a model registry.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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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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