The answer is to set the shm-size parameter in the SageMaker training container to a larger value. This resolves the OutOfMemoryError because deep learning frameworks like PyTorch and TensorFlow rely heavily on shared memory (/dev/shm) for multiprocessing data loaders; when the default 64 MB limit is exceeded, workers crash even if the instance’s 61 GB RAM is underutilized. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of container resource allocation versus instance memory—a common trap is confusing instance RAM with container-level shared memory, leading candidates to incorrectly upgrade the instance type. Remember the mnemonic “SHM for SHared Memory” to distinguish it from standard heap memory, and always check /dev/shm limits when debugging dataloader failures in SageMaker custom containers.
MLS-C01 Modeling Practice Question
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 team is training a deep learning model on SageMaker using a custom Docker container. The training job fails with 'OutOfMemoryError'. The instance type is ml.p3.2xlarge with 61 GB memory. Which change should increase available memory?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set the 'shm-size' parameter in the SageMaker training container to a larger value.
The 'OutOfMemoryError' in a SageMaker training container often stems from insufficient shared memory (/dev/shm) for data-loading workers, especially with PyTorch or TensorFlow dataloaders that use multiprocessing. Increasing the 'shm-size' parameter allocates more shared memory to the container, resolving the error without altering the model or instance type.
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.
✗
Reduce the batch size to use less memory.
Why it's wrong here
Reducing batch size lowers memory consumption but does not increase available memory.
✗
Use SageMaker distributed data parallelism to distribute the model across multiple instances.
Why it's wrong here
Distributed training spreads the workload but does not increase the memory of each individual instance.
✓
Set the 'shm-size' parameter in the SageMaker training container to a larger value.
Why this is correct
Increasing shared memory (/dev/shm) can resolve OutOfMemory errors in deep learning frameworks.
Related concept
Read the scenario before looking for a memorised answer.
✗
Mount an Amazon FSx for Lustre file system to offload data.
Why it's wrong here
FSx for Lustre provides high-performance storage, not additional memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that 'OutOfMemoryError' always refers to GPU memory, leading candidates to choose batch size reduction, when in SageMaker containers it frequently indicates insufficient shared memory for data-loading processes.
Detailed technical explanation
How to think about this question
SageMaker training containers default to a shared memory size of 64 MB, which is easily exhausted by dataloader workers (e.g., PyTorch's DataLoader with num_workers > 0) that use /dev/shm for inter-process communication. Setting 'shm-size' to a larger value (e.g., 10 GB) via the SageMaker API's 'ShmSize' parameter in the 'ResourceConfig' or 'AlgorithmSpecification' directly increases this limit, preventing the OS-level 'Cannot allocate memory' error. This is a common pitfall when migrating from local training to SageMaker, where shared memory is often unconstrained.
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: Set the 'shm-size' parameter in the SageMaker training container to a larger value. — The 'OutOfMemoryError' in a SageMaker training container often stems from insufficient shared memory (/dev/shm) for data-loading workers, especially with PyTorch or TensorFlow dataloaders that use multiprocessing. Increasing the 'shm-size' parameter allocates more shared memory to the container, resolving the error without altering the model or instance type.
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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