mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Using Amazon SageMaker to train a large deep…
A company is using Amazon SageMaker to train a large deep learning model. The training job is taking a very long time. The data scientist suspects that the GPU utilization is low due to inefficient data loading. Which action should the data scientist take to diagnose and address this issue?
⚠ Common exam trap
Candidates often assume adding more GPUs or reducing batch size will speed up training, but without addressing the data pipeline bottleneck, these changes can actually worsen GPU utilization and 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
✓
Check GPU utilization using Amazon CloudWatch metrics, and if low, optimize the data loading pipeline by using Pipe mode or faster data formats.
Low GPU utilization during deep learning training often indicates a data loading bottleneck, where the GPU spends cycles waiting for data. Amazon CloudWatch provides GPU utilization metrics for SageMaker training jobs, and if utilization is low, optimizing the data pipeline with Pipe mode (streaming data directly from Amazon S3) or using faster data formats like RecordIO or TFRecord can reduce I/O overhead and keep the GPU busy.
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 to a CPU-only instance to reduce overhead.
Why it's wrong here
CPU training is slower for deep learning.
- ✓
Check GPU utilization using Amazon CloudWatch metrics, and if low, optimize the data loading pipeline by using Pipe mode or faster data formats.
Why this is correct
Monitoring GPU utilization and optimizing data loading addresses the bottleneck.
- ✗
Reduce the batch size to speed up training.
Why it's wrong here
Smaller batch size reduces throughput.
- ✗
Increase the number of GPUs in the training instance.
Why it's wrong here
More GPUs won't help if data loading is the bottleneck.
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
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.