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 model, but the training job fails with an 'Out of memory' error. The instance type is ml.p3.2xlarge. Which action should the data scientist take to resolve the issue?
Use Pipe input mode.
Why wrong: Pipe mode addresses I/O, not memory.
Increase the number of instances.
Why wrong: Increasing instances does not increase per-instance memory.
Reduce the mini-batch size in the training script.
Reducing batch size reduces memory consumption.
Use a Spot instance.
Why wrong: Spot instances do not affect memory.
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 mini-batch size in the training script.
The 'Out of memory' error on a single ml.p3.2xlarge instance indicates that the GPU memory is insufficient for the current workload. Reducing the mini-batch size directly decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without changing the instance type or incurring additional costs.
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.
Use Pipe input mode.
Why it's wrong here
Pipe mode addresses I/O, not memory.
Increase the number of instances.
Why it's wrong here
Increasing instances does not increase per-instance memory.
Reduce the mini-batch size in the training script.
Why this is correct
Reducing batch size reduces memory consumption.
Related concept
Read the scenario before looking for a memorised answer.
Use a Spot instance.
Why it's wrong here
Spot instances do not affect memory.
Common exam traps
The trap here is that candidates confuse storage-related issues (disk space, data loading) with compute memory (GPU RAM), leading them to select Pipe input mode or Spot instances, which do not address the fundamental memory constraint.
Detailed technical explanation
GPU memory is primarily consumed by model parameters, activations, and gradients during forward and backward passes. The mini-batch size directly determines the size of activation tensors stored for backpropagation; halving the batch size roughly halves the memory required for activations. In practice, frameworks like PyTorch and TensorFlow allow dynamic batch sizing, and SageMaker training jobs can pass hyperparameters like 'batch_size' via the estimator's hyperparameters argument to adjust this without code changes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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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 mini-batch size in the training script. — The 'Out of memory' error on a single ml.p3.2xlarge instance indicates that the GPU memory is insufficient for the current workload. Reducing the mini-batch size directly decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without changing the instance type or incurring additional costs.
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 24, 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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