MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is building a pipeline to preprocess data and train a model using Amazon SageMaker. The data is stored in Amazon S3 and the preprocessing step is computationally intensive. The engineer wants to minimize costs while ensuring that the preprocessing step does not fail due to instance termination. Which instance type should be used for the preprocessing step?
⚠ Common exam trap
A common misconception in AWS is that Spot instances are always the cheapest option and should be used for all cost-sensitive workloads, ignoring the risk of interruption for non-fault-tolerant preprocessing steps.
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
✓
On-demand instances
On-demand instances (Option B) are the correct choice because they provide reliable, non-interruptible compute capacity for the preprocessing step. Spot instances can be terminated at any time (with a 2-minute warning) when AWS reclaims capacity, which would cause the computationally intensive preprocessing to fail. Reserved instances require a 1- or 3-year commitment and are not cost-effective for a single preprocessing job that may not run continuously.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reserved instances
Why it's wrong here
Reserved instances require a 1- or 3-year commitment.
- ✓
On-demand instances
Why this is correct
On-demand instances are reliable and not terminated, ensuring the step completes.
- ✗
A larger instance type to speed up processing
Why it's wrong here
Larger instances cost more and do not address the reliability issue.
- ✗
Spot instances
Why it's wrong here
Spot instances can be terminated, causing the preprocessing step to fail.
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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