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MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model on a large dataset (10 TB) stored in S3 in Parquet format. The training job uses an ml.p3.16xlarge instance with multiple GPUs. The data scientist notices that the GPU utilization is low (around 30%) and the training is slow. The dataset consists of hundreds of thousands of small Parquet files. The data scientist suspects that the I/O is bottlenecked. What should the data scientist do to improve GPU utilization and training speed?
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
✓
Consolidate the small Parquet files into larger files (e.g., 1 GB each)
Consolidating small Parquet files into larger files (e.g., 1 GB each) reduces the overhead of reading many small files from S3, improving I/O throughput and keeping GPUs busy. Option A (increase batch size) may help GPU utilization but does not address the I/O bottleneck. Option C (use a smaller instance) would not improve speed and may worsen the situation. Option D (Pipe input mode) can help with streaming but does not solve the small file issue; the data still comes from many small files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size
Why it's wrong here
Larger batch may increase GPU utilization but does not fix I/O bottleneck.
- ✓
Consolidate the small Parquet files into larger files (e.g., 1 GB each)
Why this is correct
Larger files reduce I/O overhead.
- ✗
Use a smaller instance type to reduce cost
Why it's wrong here
Smaller instance would not improve speed.
- ✗
Use Pipe input mode to stream data directly
Why it's wrong here
Pipe mode can help with streaming but still benefits from larger files.
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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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to use Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is expected to take several hours. Which storage option should be used to minimize data loading time and cost?
easy- A.Attach an Amazon EBS volume with the dataset pre-loaded
- B.Use File mode to copy data to the training instance's local storage
- ✓ C.Use Pipe mode to stream data directly from S3 during training
- D.Mount an Amazon EFS file system to the training instance
Why C: Pipe mode is the correct choice because it streams data directly from S3 into the training algorithm without writing to disk, eliminating the time and cost of copying large datasets to the instance's local storage. This minimizes data loading time (streaming starts immediately) and cost (no EBS volume or additional storage charges), making it ideal for large datasets that take hours to train.
Last reviewed: Jun 20, 2026
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