This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
Mixed precision training leverages Tensor Cores on NVIDIA GPUs, which can perform matrix multiplications on float16 data up to 8x faster than float32. The loss scaling mechanism dynamically adjusts the loss scale factor to keep gradients within the representable range of float16, preventing underflow. In practice, this can yield 2-3x speedups for transformer-based models while maintaining near-identical accuracy, as validated in frameworks like PyTorch AMP and SageMaker's built-in training toolkit.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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