MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team notices that a SageMaker training job using TensorFlow is running slower than expected. The training data is in S3 in TFRecord format. Which action is most likely to improve training throughput?
⚠ Common exam trap
Watch out — candidates often assume distributed training (Option B) always speeds up training, but the question specifically points to a data ingestion bottleneck, and Pipe mode directly addresses that by reducing I/O wait time.
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 Pipe mode for data ingestion
Pipe mode streams data directly from S3 into the training container without writing to disk, eliminating the I/O bottleneck of downloading TFRecord files first. Since TFRecords are already serialized for efficient reading, Pipe mode leverages this by feeding data sequentially, which reduces latency and improves throughput for TensorFlow jobs.
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 Pipe mode for data ingestion
Why this is correct
Pipe mode streams data, reducing I/O wait time.
- ✗
Use distributed training with more instances
Why it's wrong here
More instances can help, but might not fix slow data ingestion.
- ✗
Increase the batch size in the training script
Why it's wrong here
Larger batch size may not improve throughput and could cause OOM.
- ✗
Switch from Pipe mode to File mode
Why it's wrong here
File mode downloads full data first, which is slower.
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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