MLS-C01 Modeling Practice Question
A machine learning team is using SageMaker to train a custom TensorFlow model on a dataset that fits in memory. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which approach is most effective?
⚠ Common exam trap
AWS often tests the misconception that larger instances always reduce training time, but the trap here is that the dataset fits in memory, so the bottleneck is typically I/O, not compute, making data streaming optimizations like Pipe mode more effective than scaling up hardware.
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
✓
Switch the input mode from File to Pipe
Switching the input mode from File to Pipe is the most effective approach because it streams data directly from Amazon S3 to the training container, eliminating the need to download the entire dataset to the local storage before training begins. This reduces the I/O bottleneck and significantly cuts down the time spent on data loading, especially for datasets that fit in memory, as the model can start training almost immediately while data is being streamed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Switch the input mode from File to Pipe
Why this is correct
Pipe mode streams data directly, reducing I/O wait time and speeding up training.
- ✗
Use SageMaker managed spot training
Why it's wrong here
Spot training reduces cost but does not directly reduce training time; interruptions may even increase time.
- ✗
Use Amazon EFS as the input data source instead of S3
Why it's wrong here
EFS may have higher latency than S3 for SageMaker training.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
Larger instances can reduce training time but at higher cost; not the most effective without changing architecture.
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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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.