The answer is to use a larger instance type with more GPU memory. This is correct because a SageMaker out-of-memory error on GPU indicates that the model parameters, activations, or batch size exceed the available VRAM on the current instance, causing the training job to fail. By selecting an instance with higher GPU memory, such as moving from an ml.p3.2xlarge to an ml.p3.16xlarge, you directly increase the memory ceiling, allowing the deep learning model to fit and train without crashing. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker instance sizing and resource allocation for GPU-accelerated training. A common trap is to try reducing the batch size or using gradient accumulation, which can work but is not the most direct fix when the exam explicitly asks for a configuration change to resolve the OOM error. Remember the mnemonic: "OOM means more VRAM—size up the instance to make it fit."
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 company is training a deep learning model on a large dataset using Amazon SageMaker. The training script uses TensorFlow and requires GPUs. The training job is failing with an out-of-memory error. Which configuration change should be made to resolve this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a larger instance type with more GPU memory.
The training job is failing with an out-of-memory error, which indicates that the model or batch size exceeds the GPU memory capacity of the current instance. Using a larger instance type with more GPU memory directly addresses this by providing additional VRAM, allowing the model to fit in memory and the training to proceed without failure.
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 a larger instance type with more GPU memory.
Why this is correct
Larger instance types have more GPU memory, resolving the OOM error.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of instances in the training job.
Why it's wrong here
Adding instances does not increase per-instance memory; it only distributes the workload.
✗
Switch to using spot instances to reduce cost.
Why it's wrong here
Spot instances do not provide more memory and may be interrupted.
✗
Enable distributed training across multiple instances.
Why it's wrong here
Distributed training requires code changes and does not increase per-instance memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse horizontal scaling (adding instances) with vertical scaling (increasing instance size), assuming that more instances will magically fix a per-GPU memory limit, when in fact distributed training requires the model to fit on each GPU unless model parallelism is explicitly implemented.
Detailed technical explanation
How to think about this question
GPU out-of-memory errors in TensorFlow on SageMaker typically occur when the model parameters, optimizer states, and activations exceed the GPU's VRAM (e.g., 16 GB on a p3.2xlarge). Increasing to a p3.8xlarge (32 GB) or p3.16xlarge (64 GB) provides more memory, but also consider reducing batch size or using mixed precision training (FP16) to halve memory usage. In real-world scenarios, gradient checkpointing can trade compute for memory, but the simplest fix is a larger GPU instance.
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 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.
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 a larger instance type with more GPU memory. — The training job is failing with an out-of-memory error, which indicates that the model or batch size exceeds the GPU memory capacity of the current instance. Using a larger instance type with more GPU memory directly addresses this by providing additional VRAM, allowing the model to fit in memory and the training to proceed without failure.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.