Quick Answer
The answer is to reduce the batch size. This is the most efficient fix because the CUDA out of memory error in SageMaker occurs when the GPU’s VRAM is overwhelmed by the combined footprint of model parameters, gradients, optimizer states, and the current batch of data; lowering the batch size directly shrinks the memory consumed per training step, allowing the model to run on the existing GPU without upgrading to a more expensive instance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of GPU memory management during deep learning training—a common trap is to immediately choose a larger instance type, but the question specifically asks for the *most efficient* solution, which is a simple hyperparameter adjustment rather than a costly infrastructure change. Remember the mnemonic: “Batch down, memory found”—always try shrinking the batch size first before scaling up hardware.
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is using Amazon SageMaker to train a deep learning model with a large dataset. The training job fails with a 'CUDA out of memory' error. What is the MOST efficient way to resolve this issue?
Switch to a CPU-only instance
Why wrong: CPU training is significantly slower for deep learning.
Use a larger instance type with more GPUs
Why wrong: More GPUs don't solve per-GPU memory limits unless you use model parallelism.
Increase the batch size
Why wrong: Increasing batch size increases memory usage.
Reduce the batch size
Smaller batch size reduces memory consumption per GPU.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Reduce the batch size
The 'CUDA out of memory' error occurs when the GPU's memory is insufficient to hold the model parameters, gradients, optimizer states, and the current batch of data. Reducing the batch size decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without requiring a more expensive instance or sacrificing GPU acceleration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Switch to a CPU-only instance
Why it's wrong here
CPU training is significantly slower for deep learning.
Use a larger instance type with more GPUs
Why it's wrong here
More GPUs don't solve per-GPU memory limits unless you use model parallelism.
Increase the batch size
Why it's wrong here
Increasing batch size increases memory usage.
Reduce the batch size
Why this is correct
Smaller batch size reduces memory consumption per GPU.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the misconception that 'more resources' (larger instance or more GPUs) is always the best fix, when in fact adjusting hyperparameters like batch size is the most efficient and cost-effective first step.
Detailed technical explanation
GPU memory is shared among model parameters, gradients (which are typically the same size as parameters), optimizer states (e.g., Adam stores two additional values per parameter), and the batch itself. Reducing batch size directly reduces the memory allocated to activations stored for backpropagation, which often dominates memory usage in deep learning. In practice, a batch size of 32 or 64 is common, but if memory is exhausted, halving the batch size (e.g., to 16) can free enough memory while still leveraging GPU acceleration.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the batch size — The 'CUDA out of memory' error occurs when the GPU's memory is insufficient to hold the model parameters, gradients, optimizer states, and the current batch of data. Reducing the batch size decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without requiring a more expensive instance or sacrificing GPU acceleration.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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