MLS-C01 Practice Question: Machine Learning Implementation and Operations
An ML engineer needs to store and version training datasets and model artifacts. Which AWS service should they use?
⚠ Common exam trap
Test-takers frequently confuse storage services for ML artifacts with database or file system services, mistakenly choosing DynamoDB for its versioning-like features (e.g., DynamoDB Streams) or EFS for its shared file system access, without recognizing that S3 is the only service that offers native, durable object versioning at scale for ML use cases.
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 Simple Storage Service (S3)
Amazon S3 is the correct choice because it provides scalable, durable, and cost-effective object storage with built-in versioning capabilities, making it ideal for storing and versioning large training datasets and model artifacts. S3's versioning feature allows you to preserve, retrieve, and restore every version of an object, which is essential for reproducibility in ML workflows.
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 DynamoDB
Why it's wrong here
Database, not for file storage.
- ✓
Amazon Simple Storage Service (S3)
Why this is correct
S3 supports versioning and is commonly used for ML artifacts.
- ✗
Amazon Elastic File System (EFS)
Why it's wrong here
File storage, but not optimized for large-scale ML.
- ✗
Amazon Elastic Block Store (EBS)
Why it's wrong here
Not designed for multi-access or versioning.
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 |
Go deeper
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