MLS-C01 Practice Question: Machine Learning Implementation and Operations
An ML team wants to perform batch inference on a large dataset stored in Amazon S3 using a pre-trained model. The team needs to process the data in parallel across multiple instances to reduce processing time. Which approach should they use?
⚠ Common exam trap
Many candidates confuse SageMaker Processing (which sounds like it could handle inference) with Batch Transform, but Processing is strictly for data transformation, not model inference.
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
✓
Use SageMaker Batch Transform with multiple instances.
SageMaker Batch Transform is designed specifically for batch inference on large datasets stored in Amazon S3. It automatically distributes the data across multiple instances, processes them in parallel, and writes the results back to S3, making it the optimal choice for reducing processing time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Processing to run a custom inference script.
Why it's wrong here
Processing is for data processing, not optimized for inference.
- ✓
Use SageMaker Batch Transform with multiple instances.
Why this is correct
Batch Transform splits the input data and runs inference in parallel.
- ✗
Use SageMaker Training to run inference as a training job.
Why it's wrong here
Training jobs are for training, not batch inference.
- ✗
Use SageMaker Ground Truth to process the data.
Why it's wrong here
Ground Truth is for creating labeled datasets.
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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