MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a deep learning model using a built-in algorithm. The training job uses an ml.p3.2xlarge instance and takes 10 hours to complete. The scientist wants to reduce training time without changing the algorithm or model architecture. The instance's GPU utilization is consistently at 95%, but CPU utilization is only 20%. The data input pipeline uses SageMaker Pipe mode with the 'TrainingInputMode' set to 'Pipe'. The training dataset is 200 GB in CSV format stored in S3. Which approach is most likely to reduce training time?
⚠ Common exam trap
It's easy for candidates to assume low CPU utilization indicates an I/O bottleneck and choose to optimize the data pipeline (e.g., Pipe mode changes), when in fact the high GPU utilization reveals the true bottleneck is compute capacity, making a larger instance with more GPUs the correct solution.
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 a larger instance type with more GPUs, such as ml.p3.8xlarge
GPU utilization is already at 95%, indicating the GPU is the bottleneck. Switching to a larger instance type like ml.p3.8xlarge provides four times the number of GPUs (4 vs. 1), allowing parallel processing of more data and directly reducing wall-clock training time without altering the algorithm or model architecture. The low CPU utilization (20%) confirms that the data pipeline is not a bottleneck, so I/O optimizations are unlikely to help.
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 from Pipe mode to File mode to reduce I/O overhead
Why it's wrong here
File mode copies data to local storage, which may increase startup time and not help GPU bottleneck.
- ✗
Use Pipe mode with 'S3DataType' as 'AugmentedManifestFile'
Why it's wrong here
AugmentedManifestFile is for different data format, not for reducing training time.
- ✓
Use a larger instance type with more GPUs, such as ml.p3.8xlarge
Why this is correct
More GPUs can parallelize computation and reduce training time.
- ✗
Reduce the batch size to improve GPU utilization
Why it's wrong here
Reducing batch size may decrease GPU utilization.
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.