MLS-C01 Modeling Practice Question
A data scientist is using SageMaker to train a custom TensorFlow model. The training script reads data from S3 using TensorFlow's tf.data API. The training is bottlenecked by I/O. Which strategy would MOST effectively improve data throughput?
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 Pipe mode and shard the S3 dataset
Using SageMaker Pipe mode with a sharded S3 dataset allows the training instances to stream data in parallel, reducing I/O bottlenecks. Increasing workers in tf.data may help but not as effectively as optimizing data ingestion. Using FSx for Lustre provides high throughput but adds cost and complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compress the data files in S3
Why it's wrong here
Compression reduces size but adds decompression overhead, not directly solving I/O.
- ✗
Use Amazon FSx for Lustre as a mounted filesystem
Why it's wrong here
FSx for Lustre is high-performance but adds cost and setup complexity.
- ✗
Increase the number of parallel workers in tf.data
Why it's wrong here
This may help but does not address the underlying I/O bottleneck from S3.
- ✓
Use SageMaker Pipe mode and shard the S3 dataset
Why this is correct
Pipe mode streams data directly, and sharding distributes data across instances, improving throughput.
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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