MLS-C01 Data Engineering Practice Question
A data scientist is training a deep learning model using a large dataset stored in S3. The training job runs on a SageMaker training instance with a GPU. The data engineer notices that the GPU utilization is low, and the training is I/O bound. The data is read directly from S3 using the SageMaker SDK. Which change should the data engineer recommend to improve GPU utilization?
⚠ Common exam trap
A common mix-up: candidates confuse 'mounting S3' (which is not natively supported without third-party tools like s3fs-fuse) with SageMaker's built-in Pipe mode, or they incorrectly assume that increasing batch size will compensate for slow data loading.
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 to stream data directly from S3 to the training container.
SageMaker Pipe mode streams data directly from S3 to the training container, eliminating the need to download the entire dataset to disk. This reduces I/O latency and keeps the GPU fed with data, improving utilization. The current I/O bottleneck occurs because the SDK reads data from S3 as files, causing the GPU to wait for data.
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 in the training script to process more data per step.
Why it's wrong here
Larger batch size may cause out-of-memory errors and does not address I/O bottleneck.
- ✗
Mount the S3 bucket to the training instance using Amazon Elastic File System (EFS).
Why it's wrong here
EFS introduces network latency and does not improve I/O performance.
- ✓
Use SageMaker Pipe mode to stream data directly from S3 to the training container.
Why this is correct
Pipe mode eliminates disk I/O, allowing data to be streamed directly to the GPU.
- ✗
Copy the entire dataset to an Amazon EBS volume attached to the training instance.
Why it's wrong here
Copying data to EBS still involves disk I/O and increases training start time.
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.