MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to train a large language model. The training job is taking too long. The data scientist wants to reduce training time without sacrificing model accuracy. Which THREE strategies are MOST appropriate?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing batch size always speeds up training, but without gradient accumulation, a larger batch size may exceed GPU memory limits and cause out-of-memory errors, while gradient accumulation safely simulates a larger batch size without increasing memory usage.
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 mixed precision training (float16)
Mixed precision training (float16) reduces memory usage and accelerates computation by using half-precision floating-point numbers for most operations, while maintaining a single-precision copy of critical parameters to preserve accuracy. This directly reduces training time on compatible GPUs (e.g., NVIDIA V100, A100) without sacrificing model quality, as the loss scaling technique prevents underflow in gradients.
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 mixed precision training (float16)
Why this is correct
Mixed precision reduces memory and speeds up training on GPUs.
- ✓
Increase the batch size to utilize GPU memory more efficiently
Why this is correct
Larger batch sizes can improve GPU utilization and reduce training time.
- ✗
Switch from GPU instance to CPU instance
Why it's wrong here
CPU instances are slower for training large models.
- ✗
Increase the maximum sequence length
Why it's wrong here
Longer sequences increase computation and training time.
- ✓
Use gradient accumulation to increase effective batch size
Why this is correct
Gradient accumulation allows larger batch sizes without memory overflow, improving training efficiency.
Go deeper
Related to this question
About these practice questions
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.