MLS-C01 Modeling Practice Question
A team is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking a long time, and the team suspects that data loading is the bottleneck. The dataset consists of many small files (average size 10KB). Which change would most effectively reduce the I/O bottleneck?
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
✓
Combine the small files into larger files (e.g., TFRecord format)
Combining small files into larger ones (e.g., TFRecord or Parquet) reduces the number of S3 GET requests and improves throughput. Using Pipe mode reads data sequentially, which is less efficient for random access. Increasing instance count or using P3 instances addresses compute, not I/O. Amazon EFS is not recommended for training jobs due to higher latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Combine the small files into larger files (e.g., TFRecord format)
Why this is correct
Larger files reduce the number of S3 API calls and improve throughput.
- ✗
Use SageMaker Pipe mode instead of File mode
Why it's wrong here
Pipe mode streams data and can be faster for sequential access, but it may still suffer from many small files.
- ✗
Increase the number of training instances
Why it's wrong here
More instances would help parallelize compute but not I/O per instance.
- ✗
Use a P3 instance type for better GPU performance
Why it's wrong here
GPU performance does not address I/O bottleneck.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.