MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train and deploy machine learning models. The data science team wants to track and compare model versions, hyperparameters, and metrics across multiple training jobs. Which TWO AWS services should they use together to achieve this? (Choose TWO.)
⚠ Common exam trap
A common mix-up: candidates confuse CloudWatch Logs (which only stores raw logs) with a proper experiment tracking solution, or assume a database like RDS is needed for metadata storage, when SageMaker Experiments natively handles this with S3 as the artifact store.
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 S3
Amazon S3 is correct because it serves as the central repository for storing model artifacts, training data, and output files from SageMaker training jobs. By default, SageMaker saves model artifacts and training results to S3, enabling version tracking and reproducibility across 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 RDS
Why it's wrong here
RDS is a relational database, not needed for experiment tracking.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs stores logs, not experiment metadata.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is used for ETL, not for experiment tracking.
- ✓
Amazon S3
Why this is correct
S3 stores experiment artifacts and outputs.
- ✓
Amazon SageMaker Experiments
Why this is correct
SageMaker Experiments tracks and compares training jobs, metrics, and parameters.
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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