MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a model using Amazon SageMaker and wants to reduce the training time. The training job uses a single GPU instance. Which THREE actions can reduce training time?
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 distributed training across multiple GPU instances.
Options A, B, and C are correct. Distributed training across multiple GPU instances (A) leverages parallelism to reduce training time. Pipe input mode (B) streams data directly from S3, reducing I/O wait time compared to File mode which downloads data first. Using a larger instance type with more GPU memory and compute (C) provides more processing power, allowing faster training. Option D is incorrect because increasing the amount of training data typically increases training time, not reduces it. Option E is incorrect because reducing batch size can lead to more iterations and longer training time, though it may sometimes affect convergence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use distributed training across multiple GPU instances.
Why this is correct
Distributed training parallelizes the workload.
- ✓
Use Pipe input mode instead of File input mode.
Why this is correct
Pipe input mode streams training data directly from Amazon S3 without first downloading it to the local instance volume, eliminating the disk-write bottleneck that occurs with File mode on a single GPU instance. This reduces I/O wait time during each epoch, allowing the GPU to remain more fully utilised for computation rather than idling while data is staged locally.
- ✓
Use a larger instance type with more GPU memory and compute.
Why this is correct
More compute power speeds up training.
- ✗
Increase the amount of training data.
Why it's wrong here
More data increases training time.
- ✗
Reduce the batch size.
Why it's wrong here
Smaller batch size can increase iterations and training 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.